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Grafana Labs connects observability and incident response with Knowledge Graph
Grafana Labs connects observability and incident response with Knowledge Graph Grafana Labs has announced a Knowledge Graph in every Grafana Cloud stack, alongside new Digital Experience Monitoring capabilities and updates to Grafana Cloud Incident Response Management (IRM). The releases connect infrastructure and application dependencies with user experience and incident response, giving people and agents shared context to investigate problems and take action. As agentic systems become more architecturally complex and teams use agents to investigate and respond to problems, those agents need context that connects technical symptoms to business impact. Most teams have telemetry from databases all the way to the user’s browser, but lack the runtime context that connects a failing database to the service, the team, and the customer it affects. In the 2026 State of Observability Report, also released this week by Grafana Labs and ETR, a Futurum company, the most common maturity stage among the 300 IT and engineering leaders surveyed (30%) is being able to explain unexpected system behavior but not detect and prevent issues before users are affected. To close that gap, Grafana Cloud continues to expand full-stack observability so people and agents can connect those signals to the product customers are using. Because it is built on open standards, it also works with the agents teams already use, not only Grafana’s own. “Most teams do not have a monitoring problem. They have a technology debt problem: telemetry from every layer and no way to connect it to the customer it affects,” said Richard Lam, Director of Product, Grafana Labs. “Putting a Knowledge Graph in every Grafana Cloud stack, and extending it from the infrastructure all the way to the user, makes unified observability foundational instead of something every team has to assemble on its own, for people and for the agents working beside them.” One connected view of everything you run A full-stack view is only useful if its layers connect. A knowledge graph links infrastructure, applications, and user-facing signals into a shared model, helping people and agents trace symptoms across dependencies to investigate their cause. Grafana Cloud Knowledge Graph is the runtime context layer for Grafana Cloud, where teams curate what their agents know about their environment. It builds that context from telemetry derived across Grafana’s observability solutions, connecting agents, applications, and databases to cloud services and Kubernetes clusters, and reaches all the way to your users through frontend and synthetic monitoring. Teams add what only they know with custom entities, so people and agents work from the same context, built on open standards. With that context, they can work through complex problems with less guesswork and fewer dead ends. New Knowledge Graph capabilities include: Custom entities (GA): Group what Grafana Cloud discovers into the things your business runs on, such as a product, using entities and relationships defined from telemetry. Knowledge Graph mode in Grafana Assistant helps inspect existing connections and write the rules. Service catalog with ownership (private preview): Brings service ownership, on-call schedules, and runbooks from Grafana Cloud Service Center onto the graph, so following a dependency also shows who can act on it. Runtime context for agents (GA): Knowledge Graph is available as runtime context for Grafana Assistant and any agent connected through gcx. A new Cypher query capability lets agents retrieve relationships flexibly, so each starts from what you have already built instead of rebuilding context from raw telemetry. “The future of operations won’t be defined by who collects the most telemetry, but by who can provide the most accurate context. Telemetry tells us what’s happening. Context tells us why it matters,” said Neil Wilson, Senior Director of Software Engineering, LexisNexis Risk Solutions. “We’ve found that the hardest part of an investigation is rarely a lack of data. The challenge is connecting service ownership, dependencies, operational knowledge, and customer impact across a complex ecosystem. Entity Graph relationships help make those connections visible, giving engineers and AI systems the context they need to move from data, to understanding, to action.” See where users struggle, not just when systems fail Observability that stops short at the server misses where customers actually experience your product. Now, Grafana Cloud Digital Experience Monitoring extends full-stack observability to the browser and mobile devices, connecting proactive testing, which simulates what you expect, with real user signals, all on the same telemetry as the rest of the stack. Today, Grafana Labs is introducing new use cases, user journeys, data types, and views to further close the gap between system health and user experience: Session Replay (public preview): Watch a video-player-like recording of what your users actually did in your application. Correlate when something breaks to the underlying telemetry so that you know exactly what happened. Mobile Observability (private preview): Extends Frontend Observability to your iOS, Android, React Native, and Flutter apps using upstream native OpenTelemetry SDKs. See what mobile users experience, and connect it to the same telemetry as the rest of your stack, without a separate tool. Critical User Journeys (private preview): Define which journeys matter most, so every team works from the same definition of “critical” instead of improvising its own. Explore a journey end to end and go from a drop in conversion rate to the root cause in a single view. One shared picture for every responder, human or agent Full-stack visibility matters most when something breaks, and incident response is where the layers have to work together. But AI has made incident response more crowded, not simpler. As teams bring agents into incident response, coordinating investigations becomes another challenge. Engineers, their coding agents, and Grafana Assistant can all investigate the same incident at once, creating a risk of duplicated work without a shared record of what has already been checked. Updates to Grafana Cloud IRM give people and agents shared context and a coordinated workspace for incident response. Grafana Workflows (public preview): Event-driven automation that connects actions across Grafana Cloud and your other tools, with Grafana Assistant available for steps that need analysis or writing. Prebuilt blueprints cover major incident kickoff, on-call handoff reports, and weekly service health summaries. Operational Context with Knowledge Graph (private preview): Captures what past responses tried, found, and ruled out, so the next investigation starts with that experience and agents can test past hypotheses against current evidence. Response Console (private preview): A single interface where people and agents collaborate on incidents. Responders can work with Claude, Codex, and Assistant Investigations in one place, all drawing on the same Knowledge Graph context, so no one repeats work or loses track of what has already been checked.
helpnetsecurity.comOct 21, 2026extracted
ToolHive: The open-source way to run any MCP server securely
ToolHive: The open-source way to run any MCP server securely ToolHive is an open-source platform that runs Model Context Protocol servers inside containers. An MCP server is the connector that lets an AI client like Cursor or Claude Code reach an outside tool, and Stacklok ships ToolHive under Apache 2.0, so the runtime, the Kubernetes operator, and the registry cost nothing to self-host. A server you install by hand sits on the machine with the machine’s credentials and the machine’s network access. ToolHive drops each one into its own container with a minimal permission file and no local credentials attached. Point it at an authentication source and it starts enforcing identity and access policy per request, with audit logs to match. Don’t, and you have a sandbox and not much else. What the pieces do Four parts make up the platform. The Runtime is the one most people meet first: it runs MCP servers in containers, locally through Docker or Podman, or in a cluster through the Kubernetes operator, with permissions, network filtering, and secrets management applied on the way in. It will also containerize a server whose authors never shipped an image, pulling it straight from a package manager. The Registry Server is where an admin curates the catalog a team is allowed to use. It implements the official MCP Registry API, signs servers, and verifies provenance. The Gateway, which Stacklok calls Virtual MCP Server, collapses several backends behind one endpoint with OIDC or OAuth single sign-on, OpenTelemetry traces, and Prometheus metrics. The Portal is the friendly end: a desktop app, a catalog to browse, one-click installs. One piece is in motion. The browser-based cloud UI is marked retired, so build a rollout around the desktop app and the CLI rather than the web surface. What this changes for you Building an MCP server is the easy half, and plenty of tools already do it. The operational half is knowing which servers are running on which machines and what each one can touch, and that is the gap ToolHive goes after. The container boundary holds without much work from you. Everything past it, the identity enforcement, the audit trail, the policy filtering, means wiring ToolHive into an identity provider and a telemetry stack you already run. Install it on a laptop and you get isolation. Governance is a second project. ToolHive is available for free on GitHub. Must read: 20 open-source cybersecurity tools to keep your team ready for anything GitHub CISO on security strategy and collaborating with the open-source community Subscribe to the Help Net Security ad-free monthly newsletter to stay informed on the essential open-source cybersecurity tools. Subscribe here!
helpnetsecurity.comSep 7, 2026extracted
Incident response guide for AWS CloudTrail investigations – Part 2
In Part 1 of this guide, we examined two common incident scenarios: cross-account Amazon Simple Storage Service (Amazon S3) data deletion with ransomware implications, and cryptocurrency mining deployed through AWS CloudFormation using exposed AWS Management Console credentials. We also introduced key incident response terminology and investigative frameworks for analyzing AWS CloudTrail events. In this second part, we explore a more complex, multi-stage attack: how a web application vulnerability can cascade into credential harvesting and unauthorized access to Amazon Bedrock services across multiple AWS Regions. We also cover additional investigation techniques and hardening steps to strengthen your security posture. Scenario 3: SSRF to IMDSv1 credential harvesting with multi-Region Amazon Bedrock service misuse This scenario examines how a web application vulnerability can cascade into a multi-Region event targeting Amazon Bedrock services. The investigation demonstrates how threat actors chain together multiple techniques, using Amazon Elastic Compute Cloud (Amazon EC2) Instance Metadata Service version 1 (IMDSv1) through server-side request forgery (SSRF) and cross-Region pivoting to access Amazon Bedrock. Your security team receives multiple alerts: failed AWS Identity and Access Management (IAM) operations in the us-east-1 Region, successful console sign-ins without multi-factor authentication (MFA), and unusual Amazon Bedrock API calls from us-east-2. Initially, these might seem like unrelated events across different services and Regions. However, as our Security Incident Response Team (SIRT) discovered, they represent a carefully orchestrated event chain that began with a web application vulnerability and culminated in unauthorized access to your organization’s AI infrastructure. Architecture and progression The architecture in figure 1 maps a multi-stage attack that exploits the trust relationship between Amazon Elastic Compute Cloud (Amazon EC2) instances and AWS services. A threat actor identified a server-side request forgery (SSRF) vulnerability in a web application running on an EC2 instance that had an attached webdev IAM role. Rather than attempting to escalate privileges directly, the threat actor used this foothold to reach the Instance Metadata Service version 1 (IMDSv1) endpoint and retrieve the temporary credentials issued to the webdev role. Because IMDSv1 returns credentials in response to a basic request with no session token, an SSRF flaw is enough to harvest them, which is why these credentials became the pivot point for everything that followed. The attack unfolded in five stages. Each stage is numbered in figure 1 so you can follow the progression from the initial web request through to the cross-Region Amazon Bedrock activity: Initial access : The threat actor exploited the SSRF vulnerability in the web application to make server-side requests on the instance’s behalf. Credential harvesting : Those requests reached the IMDSv1 endpoint and returned the temporary credentials for the webdev role. Permission testing : Using the harvested credentials, the threat actor attempted IAM operations to probe the boundaries of what the role could do. Service pivoting : When IAM actions were denied, the threat actor shifted focus to Amazon Bedrock, a service the role could reach. Region hopping : The threat actor moved operations from us-east-1 to us-east-2, likely to evade Region-specific monitoring and access controls. Figure 1: Scenario 3 architecture CloudTrail evidence and structured extractions In this section, we walk through the CloudTrail evidence that documents the attack from start to finish. Each of the four events that follow maps to one or more stages in the progression described previously, and together they trace how the threat actor moved from harvested credentials to active misuse of Amazon Bedrock. For each event, we present the relevant portion of the CloudTrail log record, highlight the fields that matter most for the investigation, and include a forensic legend that explains what each highlighted field reveals. We cover the following events: Permission boundary testing (15:53:49 UTC) : A failed CreateUser call in us-east-1 that reveals the compromised role and the IMDSv1 credential source. Console access establishment (15:59:29 UTC) : A successful console sign-in without MFA, showing the pivot from programmatic to interactive access. Bedrock service reconnaissance (17:20:00 UTC) : A ListFoundationModels call in us-east-2 that marks the Region hop and the shift to AI services. Active model exploitation (17:25:48 UTC): A Converse call that invokes the Amazon Nova Pro model, confirming unauthorized usage. As you read each event, focus on how the fields connect one stage to the next. The same webdev role, the same source IP address, and the recurring ec2RoleDelivery value are the threads that tie these otherwise separate events into a single attack chain. Event 1: Permission boundary testing (15:53:49 UTC): The first suspicious activity appeared as a failed CreateUser API call in us-east-1. The CloudTrail log records an AssumedRole session attempting to create an IAM user named adm1n but receiving an AccessDenied error. The webdev role is visible in the userIdentity field, readOnly is false (indicating a write operation attempt), and the user-agent shows AWS Command Line Interface (AWS CLI) on Windows, suggesting programmatic access from the harvested credentials. { "eventVersion": "1.11", "userIdentity": { "type": "AssumedRole", "principalId": "AROAFINDANEXAMPLE:i-0123456789abcdef0", "arn": "arn:aws:sts::XXXXXXXXXXXX:assumed-role/webdev/i-0123456789abcdef0", "sessionContext": { "sessionIssuer": { "type": "Role", "userName": "webdev" }, ◄── ❶ Compromised EC2 role ‾‾‾‾‾‾‾‾ "attributes": { "mfaAuthenticated": "false" } }, "ec2RoleDelivery": "1.0" ◄── ❷ IMDSv1 confirmed (SSRF exploitation path) ‾‾‾‾‾ }, "eventTime": "2025-09-22T15:53:49Z", "eventSource": "iam.amazonaws.com", "readOnly": false, "eventName": "CreateUser", ◄── ❸ Intent: establish persistent backdoor ‾‾‾‾‾‾‾‾‾‾‾‾ "userAgent": "aws-cli/2.17.48 ua/2.0 os/windows#10 ...", "errorCode": "AccessDenied", ◄── ❺ Hard policy stop (least-privilege held) ‾‾‾‾‾‾‾‾‾‾‾‾‾‾ "errorMessage": "User: arn:aws:sts::XXXXXXXXXXXX:assumed-role/webdev/... is not authorized to perform: iam:CreateUser on resource: arn:aws:iam::XXXXXXXXXXXX:user/adm1n..." ◄── ❹ Lookalike name (1 not i) ‾‾‾‾‾ } ─────────────────────────────────────────────────────────────────── FORENSIC LEGEND ─────────────────────────────────────────────────────────────────── ❶ userName: "webdev" → Confirms the compromised EC2 role context ❷ ec2RoleDelivery: "1.0" → Credentials obtained via IMDSv1 (SSRF vector) ❸ eventName: "CreateUser" → Attacker attempting IAM persistence ❹ target user: "adm1n" → Typosquatting admin (number 1 instead of letter i) ❺ errorCode: "AccessDenied" → Attacker probing permission boundaries; blocked ─────────────────────────────────────────────────────────────────── Event 2: Console access establishment (15:59:29 UTC): Six minutes later, the threat actor successfully signed in to the AWS Management Console using the same credentials. The ConsoleLogin event records that MFA wasn’t used ( MFAUsed: No ), and the source IP (75.3.231.105) provides attribution data. The user agent indicates Chrome browser on Windows 10. { "eventVersion": "1.11", "userIdentity": { "type": "AssumedRole", "principalId": "AROAFINDANEXAMPLE:i-0123456789abcdef0", "arn": "arn:aws:sts::XXXXXXXXXXXX:assumed-role/webdev/i-0123456789abcdef0", "sessionContext": { "attributes": { "mfaAuthenticated": "false" } } }, "eventTime": "2025-09-22T15:59:29Z", "eventSource": "signin.amazonaws.com", "userAgent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36 Edg/140.0.0.0", "eventName": "ConsoleLogin", ◄── ❶ Pivoted to interactive console access ‾‾‾‾‾‾‾‾‾‾‾‾‾‾ "awsRegion": "us-east-1", "sourceIPAddress": "75.3.231.105", "responseElements": { "ConsoleLogin": "Success" },◄── ❷ Hijacked login succeeded ‾‾‾‾‾‾‾‾‾ "additionalEventData": { "MobileVersion": "No", "MFAUsed": "No" },◄── ❸ No MFA challenge ‾‾‾‾ "eventType": "AwsConsoleSignIn" ◄── ❹ Console sign-in (not API call) ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾ } ─────────────────────────────────────────────────────────────────── FORENSIC LEGEND ─────────────────────────────────────────────────────────────────── ❶ eventName: "ConsoleLogin"→ Attacker pivoted from programmatic to visual console access ❷ ConsoleLogin: "Success"→ Hijacked login successfully authenticated ❸ MFAUsed: "No" → Critical gap: no MFA enforced, enabling the pivot ❹ eventType: "AwsConsoleSignIn" → Distinguishes this from basic API calls ─────────────────────────────────────────────────────────────────── Event 3: Amazon Bedrock service reconnaissance (17:20:00 UTC): Nearly two hours later, the threat actor pivoted to Amazon Bedrock, making a ListFoundationModels API call in us-east-2. This event exhibits several patterns: a Region change from us-east-1 to us-east-2 (potential defense evasion), a shift from IAM to AI services, readOnly: true (reconnaissance rather than modification), and sessionCredentialFromConsole: “true” , which ties the call to the console session established in Event 2 rather than a fresh IMDSv1 credential retrieval. { "eventVersion": "1.11", "userIdentity": { "type": "AssumedRole", "arn": "arn:aws:sts::XXXXXXXXXXXX:assumed-role/webdev/i-0123456789abcdef0" }, "eventTime": "2025-09-22T17:20:00Z", "eventSource": "bedrock.amazonaws.com", ◄── ❶ Pivoted to cloud AI services ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾ "eventName": "ListFoundationModels", ◄── ❷ AI model reconnaissance ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾ "awsRegion": "us-east-2", ◄── ❸ Region hop (evasion technique) ‾‾‾‾‾‾‾‾‾‾‾ "sourceIPAddress": "75.3.231.105", "readOnly": true, "tlsDetails": { "clientProvidedHostHeader": "bedrock.us-east-2.amazonaws.com" ◄── ❹ Intentional alternate region targeting ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾ }, "sessionCredentialFromConsole": "true" } ─────────────────────────────────────────────────────────────────── FORENSIC LEGEND ─────────────────────────────────────────────────────────────────── ❶ eventSource: "bedrock.amazonaws.com"→ Attacker pivoted from IAM to managed AI services ❷ eventName: "ListFoundationModels"→ Reconnaissance: enumerating available AI models ❸ awsRegion: "us-east-2"→ Region hop from us-east-1 (defense evasion) ❹ clientProvidedHostHeader: "bedrock.us-east-2..." → Confirms intentional targeting of alternate region endpoint ─────────────────────────────────────────────────────────────────── Event 4: Active model exploitation (17:25:48 UTC): Five minutes after the reconnaissance call, the threat actor moved from enumeration to active exploitation, invoking the Amazon Nova Pro model through the Converse API in us-east-2. The additionalEventData field quantifies the unauthorized usage at 944 input tokens and 126 output tokens, confirming that the threat actor successfully prompted the model and received a response. { "eventVersion": "1.11", "userIdentity": { "type": "AssumedRole", "arn": "arn:aws:sts::XXXXXXXXXXXX:assumed-role/webdev/i-0123456789abcdef0" }, "eventTime": "2025-09-22T17:25:48Z", "eventSource": "bedrock.amazonaws.com", "eventName": "Converse", ◄── ❶ Active model invocation (recon → exploitation) ‾‾‾‾‾‾‾‾‾‾ "awsRegion": "us-east-2", "requestParameters": { "modelId": "amazon.nova-pro-v1:0", ◄── ❷ Specific model being misused ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾ "inferenceConfig": { "maxTokens": 1024 } }, "responseElements": null, "additionalEventData": { "inputTokens": 944, "outputTokens": 126 } ◄── ❸ Unauthorized usage quantified ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾ } ─────────────────────────────────────────────────────────────────── FORENSIC LEGEND ─────────────────────────────────────────────────────────────────── ❶ eventName: "Converse" → Attacker transitioned from reconnaissance to active exploitation ❷ modelId: "amazon.nova-pro-v1:0" → Identifies the specific foundation model being misused ❸ inputTokens: 944, outputTokens: 126 → Quantifies unauthorized usage (financial cost + data exfiltration exposure) ─────────────────────────────────────────────────────────────────── Notable event fields to track As you review the event logs, a handful of fields do most of the investigative work in this scenario. Understanding what each one reveals, and why it matters, is what turns a collection of individual log records into a coherent attack narrative. The userIdentity field is the starting point for attribution. In this scenario it carries the EC2 instance ID as the session name, which is what let us trace the harvested credentials back to a specific compromised instance rather than a human user. Whenever you see an assumed-role session, this field answers the first question of any investigation: w hose credentials are these, and where did they come from? The readOnly field reveals the intent behind an action. A value of true marks reconnaissance, such as the ListFoundationModels call the threat actor used to enumerate available models, while false marks an attempt to change or use something, such as the CreateUser call or the Converse invocation. Sorting events by this field quickly separates the threat actor’s information gathering from the actions that caused actual impact. The awsRegion field is easy to overlook, but in this scenario it exposed the threat actor’s evasion strategy. The shift from us-east-1 to us-east-2 wasn’t incidental; threat actors move between Regions because monitoring, alerting, and access controls are often configured inconsistently across them. Watching this field helps you spot activity that has deliberately moved away from where your detection is strongest. Finally, the userIdentity.invokedBy field identifies when an AWS service, rather than a user or a set of harvested credentials, made the request on your behalf. CloudTrail populates it only when the caller is an AWS service, such as through a service-linked role, a service role, or a forward access session. It doesn’t appear in the events for this scenario because the threat actor called Amazon Bedrock directly with the harvested webdev credentials. That absence is itself informative: it confirms the requests came from a principal acting on its own rather than from a legitimate service-driven workflow. As agent-based and service-integrated Amazon Bedrock workloads become more common, checking this field separates expected service activity from credentials driven directly by a threat actor. Investigation priorities With the full attack chain mapped, from SSRF through credential harvesting to Amazon Bedrock service misuse, the investigation turned to a harder question: what did each stage actually cost us, and what would stop it from happening again? A few priorities shaped that work. The first was figuring out where the credentials came from and how far the exposure reached. It was clear the threat actor had valid credentials for the webdev role, but the more useful question was why a web application role could reach Amazon Bedrock at all. The customer confirmed there was no business reason for it, so we needed to understand whether that permission was a deliberate misconfiguration or an oversight, and then look for other EC2 instances carrying the same role attachment. One compromised instance is an incident; a fleet of instances with the same over-scoped role is a much bigger problem waiting to happen. Next, we wanted to know what the threat actor did after they got into Amazon Bedrock. Reconnaissance and active use carry very different consequences, so we traced which foundation models were touched and whether any were actually invoked or only enumerated. That distinction matters for scoping the damage, and it signals whether data exfiltration is a concern. Unusual model usage, unexpected prompt volume, or output patterns that don’t match any legitimate workload are the signals that reconnaissance has turned into something worse. The Region hop was its own line of inquiry. The move from us-east-1 to us-east-2 was almost certainly deliberate, and the investigation focused on understanding what the threat actor gained by it. In practice, that meant comparing the two Regions: were the monitoring and access controls in us-east-2 weaker than in us-east-1, and what else did the threat actor reach in the secondary Region once they were there? Inconsistent controls across Regions are one of the most common ways activity slips past detection. Tying it all together was the timeline, which shows how quickly the threat actor moved through the chain: 15:53:49 : Failed IAM operation (us-east-1) 15:59:29 : Successful console login (us-east-1) 17:20:00 : Amazon Bedrock reconnaissance (us-east-2) 17:25:48 : Active model invocation ( Converse call) (us-east-2) Following the credentials across those events fills in the rest of the story. IMDSv1 handed the threat actor temporary credentials for the webdev role, and the same role appears in every event that followed, which confirms the credentials were reused rather than replaced. Nowhere in that sequence was MFA required, and that single gap is what let one harvested credential stay useful across two hours, two Regions, and two very different services. Incident response checklist The following checklist captures the actions needed to contain the incident, remediate the vulnerability, and assess the scope of unauthorized AI service usage. Each item names where to look and what a finding looks like, so the checklist stays usable under the time pressure of a live incident. Contain and remediate the entry point: Identify the specific web application feature that made the outbound request (URL fetchers, webhook callbacks, PDF or image renderers, and link-preview generators are the usual culprits), then confirm it can reach http://169.254.169.254 . Audit the rest of the application for the same pattern, because one unvalidated URL parameter usually means others exist. Enforce IMDSv2 on the affected instance and across the fleet with aws ec2 modify-instance-metadata-options --http-tokens required --http-put-response-hop-limit 1 . Setting --http-tokens required means credentials are only returned when the caller presents a session token it obtained through a PUT request, which a basic SSRF cannot do. Setting the hop limit to 1 keeps the metadata response on the instance itself, so a request coming from a container or proxy an extra hop away never receives it. Scope the Bedrock usage: List the foundation models the webdev role could reach by reviewing its IAM policy and any resource-based policies, so you know the full set of models that were exposed, not only the one that was invoked. Determine what was sent to and returned by the model. CloudTrail records the Converse call and the token counts, but only Amazon Bedrock model invocation logging captures the input prompts and model responses. If it was enabled, pull the log entries for the session; if it wasn’t, note that the prompt and response content can’t be recovered and enable it now. Flag any compliance exposure based on what those prompts and responses contained. Unauthorized processing of regulated data (such as personally identifiable information (PII), protected health information (PHI), or cardholder data) through the model might trigger notification obligations. Check for wider compromise and persistence: Query CloudTrail across all Regions and services—not only Amazon Bedrock—for every event tied to the webdev role’s session, to confirm what else the same credentials touched. Correlate the CloudTrail timestamps with VPC Flow Logs and application logs for source IP 75.3.231.105 to build the network-level picture around each API call. Search for IAM write events from the session ( CreateUser , CreateRole , CreateAccessKey , and AttachRolePolicy ) that indicate an attempt to establish persistence beyond the temporary credentials. The failed adm1n CreateUser call is the known starting point; confirm nothing similar succeeded. Watch for ongoing or hidden impact: Review Amazon Bedrock usage in CloudWatch and your billing data for invocation spikes or unexpected token consumption that fall outside normal workload patterns. Inspect the invocation logs for signs of sensitive data being processed or extracted through the model. Check the same logs for prompt injection attempts, where the input tries to override the model’s instructions or extract system prompts. Key takeaways This scenario reveals how a single application vulnerability can cascade into broad unauthorized access when multiple security controls are missing. The following takeaways highlight the key defensive gaps and hardening priorities. Least-privilege IAM for workload roles : The webdev role’s access to Amazon Bedrock across multiple Regions had no business justification for a web application workload, which the customer confirmed during the investigation. Apply least-privilege principles to EC2 instance roles by scoping permissions to only the services and actions the application requires. Use AWS IAM Access Analyzer to identify unused permissions and tighten policies proactively. Overly permissive roles transform a single application vulnerability into broad lateral movement across unrelated services. IMDSv1 compared to IMDSv2 : Organizations must immediately switch to IMDSv2 and disable IMDSv1 across their entire cloud infrastructure. The ec2RoleDelivery: “1.0" field in the logs explicitly confirms the use of IMDSv1, which permits credential retrieval without an authentication token. This architectural weakness makes SSRF-based credential theft trivial, because a web application flaw that can make an outbound request is enough to read the role’s temporary credentials with no further authentication. Transitioning to IMDSv2 mitigates this attack surface by enforcing local, session-based tokens, effectively breaking the threat actor’s exploitation chain. In this scenario, IMDSv2 alone would have stopped the attack at its first step. Region-based defense evasion signals a deliberate operator : The shift from us-east-1 to us-east-2 for Amazon Bedrock access wasn’t incidental. Threat actors move between Regions because monitoring, alerting, and access controls are often configured inconsistently across them, and activity in a secondary Region is more likely to go unnoticed. This kind of cross-Region movement is a marker of operational security awareness rather than opportunistic access, and it should raise the priority of an investigation. Treat consistent detection coverage across all Regions, including the ones you do not actively use, as a baseline requirement. Interface switching and permission probing reveal the threat actor’s method : This event chain reveals a threat actor comfortable moving between AWS interfaces and testing boundaries before committing. The failed CreateUser attempt was systematic probing to understand the scope of the harvested credentials, and when IAM actions were denied, the threat actor pivoted to a service the role could actually reach. The combination of programmatic access through the AWS CLI and interactive console access demonstrates the same adaptability. Recognizing this pattern of probe, adapt, and pivot helps responders anticipate the next move instead of reacting to each event in isolation. AI services need visibility beyond CloudTrail : Amazon Bedrock and other AI services are high-value targets, and CloudTrail alone doesn’t capture the whole story. CloudTrail records who called Amazon Bedrock and whether the call succeeded, but not what was asked or answered. Enable Amazon Bedrock model invocation logging to capture full prompts and responses for compliance auditing. For agent-based workloads, Amazon Bedrock AgentCore Observability , built on AWS Distro for OpenTelemetry (ADOT), provides session-level traces showing tool execution order and latency. Consider also enabling Amazon GuardDuty AI Protection , which analyzes Amazon Bedrock-related CloudTrail activity to detect anomalous invocations, cost harvesting, and prompt injection attempts. Correlating these signals—CloudTrail, Model Invocation Logging, and agent telemetry—gives investigators the complete picture. For implementation guidance, see Monitoring and Auditing AI Workloads on AWS . Advanced forensic indicators and evasion techniques Beyond the specific attack patterns in this scenario, investigators should be aware of several evasion techniques that threat actors use to confuse defenders and blend into legitimate activity. The top three that we observe across incident response with customers are: Root user compared to IAM user named root : When you first create an AWS account, you begin with a single sign-in identity that has complete access to all AWS services and resources in the account. This identity is called the AWS account root user. In some previous investigations, threat actors have also created IAM users in an AWS account named root . The difference is visible in the type field of the userIdentity element of the CloudTrail log record, which indicates the type of user that logged the record. Role and user name imitation : Threat actors attempt defense evasion by creating names for IAM users and roles that imitate those reserved for use by AWS. For example, the service-linked role AWSServiceRoleForSupport is a unique IAM role linked directly to AWS Support. Threat actors have created roles with the name AWSServiceRoIeforSupport (note the use of an upper-case letter I instead of a lower-case letter l in Role ) in an attempt to trick users into thinking actions taken by this role have been performed by AWS Support. Users named HIDDEN_DUE_TO_SECURITY_REASONS : The userName field contains the string HIDDEN_DUE_TO_SECURITY_REASONS when the recorded event is a console sign-in failure caused by incorrect user name input. CloudTrail doesn’t record the contents in this case because the text could contain sensitive information. However, threat actors have used this string as an actual username to trick investigators into thinking the name has been obfuscated. This technique is usually associated with a corresponding CreateUser or CreateRole CloudTrail event. Conclusion and next steps CloudTrail event fields help security teams identify identities with unintended access, track threat actor actions, and remediate affected resources. Understanding fields like userIdentity , eventName , and sourceIPAddress improves incident investigation and threat detection. Implementing best practices such as enabling comprehensive logging, using Amazon Athena for analysis, securing logs, and automating responses helps ensure that CloudTrail serves as a robust forensic and incident response tool. If you suspect unauthorized activity in your AWS environment, AWS Security Incident Response is available to help. The service continuously monitors and triages findings from Amazon GuardDuty and third-party security tools integrated through AWS Security Hub , automatically filtering alerts to surface the most relevant events. In addition to proactive triage, customers can initiate security cases through the service. You can choose to handle these cases internally or receive support from the Security Incident Response Team (SIRT), a dedicated group of security experts available at all times to assist with investigation, containment, and recovery throughout the incident lifecycle. Additional resources The following resources provide further guidance on securing your AWS environment and strengthening your investigative capabilities. AWS Security Best Practices The Agentic AI Security Scoping Matrix: A framework for securing autonomous AI systems CloudTrail Log Event Reference AWS Security Incident Response Guide If you have feedback about this post, submit comments in the Comments section below. Oscar E Diaz Cordovez Oscar is a Senior Technical Account Manager specializing in cloud operations and security. His passion for technology and innovation drives his expertise in cloud-focused architectures, DevOps practices, and automation. Steve de Vera Steve is a manager for the AWS Security Incident Response service with a focus on threat research and threat intelligence. He is passionate about American-style BBQ and is a certified competition BBQ judge. He has a dog named Brisket. Jennifer Paz Jennifer is a Security Engineer Manager with over a decade of experience, for the AWS Security Incident Response service. Jennifer enjoys helping customers tackle security challenges and implementing complex solutions to enhance their security posture. When not at work, Jennifer is an avid runner, pickleball enthusiast, traveler, and foodie, always on the hunt for new culinary adventures.
aws.amazon.comSep 3, 2026extracted
Securing Claude Code: The New Compliance API, Local Visibility, and Identity Governance
Claude Code reads files, runs shell commands, invokes MCP tools, and acts through the credentials available on a developer’s machine. Anthropic’s new Compliance API endpoints give security teams their clearest view yet into that activity. They also expose a larger problem: activity logs alone cannot tell you whether an agent’s access is legitimate. AI has moved from the browser tab to the endpoint with harnesses like Claude Code. They run on developers' machines, execute bash commands locally, and connect to third parties via MCP servers, skills, and plugins. All this so the user can outsource labor to the machine and focus on designing, thinking, and creating. Local agents are not a niche category. They account for 68.6% of the AI agents Token Security discovers in customer environments, and they often inherit the employee's credentials, network position, and permissions. The shift to the endpoint has major implications for security. With Claude Code, there is no centralized console to monitor endpoint agents across local configurations, identity and access, and runtime. Before August 2026, Anthropic's native controls had limited visibility into what those agents were actually doing, forcing teams to use third-party extensions just to achieve the bare minimum of governance. With the new local session transcript endpoints in the Anthropic compliance API, you can better govern your local agents while understanding which limitations still exist. A harness is not a chatbot A harness is a sophisticated orchestrator. It takes user input and sends it to the LLM along with the full session context. The LLM itself doesn’t maintain state; it receives everything it needs from the harness to respond on an ad hoc basis. The component that actually runs commands, authenticates to third parties, and connects to MCP servers is the harness, not the LLM. Compare an endpoint agent to a human body. The LLM is the brain: it processes the data and calls the shots. Everything else is the harness, from the hands and the legs to the sensory organs. It's a weird hybrid, and our security model has to adapt to fit it. The brain runs in Anthropic's cloud, but the hands run on your endpoints, and that is where your visibility and control have to live. Unorthodox design It’s en vogue to say that SaaS is dead, and it's a little SaaD, because classic SaaS took care of a lot of things for us. We expect a service to let us manage and monitor our enterprise from a central dashboard, control organizational policies, and clearly see what the agents within our enterprise can do. That is not the case with local harnesses. Claude Code challenges the classic shared-responsibility model and puts more load on admins. In a Token-commissioned Cloud Security Alliance survey of 418 IT and security professionals, 68% rated their visibility into AI agents as high. In the same survey, 82% had discovered an agent in the past year that security, IT, or governance did not know existed. Riddle me this: I live on your host as an agent, but I was here long before any LLM. I know more about your Claude Code than Anthropic does, because endpoints are my realm. Because much of Claude Code’s execution happens locally, endpoint telemetry can reveal processes, files, and configurations that cloud services cannot see. But EDR provides evidence, not a governance model. It cannot connect an agent’s activity to its owner, intent, credentials, and permissions. Anthropic's own tooling helps, but it isn't enough to prevent LLMs from performing destructive actions, even if those actions may be legitimate. There are three key layers for gathering data to govern local AI agents effectively. You need to understand what Anthropic gives you, what only an endpoint agent can collect, and what you need to do with the data. Layer 1: Managed settings, the policy baseline Anthropic's enforcement mechanism is managed settings. Every endpoint that installs Claude Code has a managed-settings record: a JSON file on Mac and Linux, and registry records on Windows. Its rules take precedence over global, project, and user settings, allowing you to enforce a baseline over every Claude Code session in the organization. On a Claude Code enterprise plan, you apply policies through the GUI; without one, your MDM can write the managed-settings record across endpoints. The available rules cover a lot: Allow and deny lists for specific MCP servers Regexes over bash commands Disabling skills from running commands, and more They help, but they take a lot of maneuvering room away from your developers, and static allow/deny policies aren't built for the pace of modern AI. Worse, they don't know context or intent. In effect, they're a big boulder in the middle of a river, disrupting the stream but not stopping it. Layer 2: The Compliance API Until recently, Anthropic's Compliance API mainly covered claude.ai actions, meaning activity from the web interface and Claude Desktop, with very thin coverage of Claude Code. On August 11, 2026, Anthropic introduced new endpoints for local sessions: These give you visibility into agents running on endpoints, based on their interaction with Anthropic's models. Whatever is communicated to the model is logged in three block types: text, tool_use, and tool_result. Between them, they cover user prompts, bash commands, reads and writes, and even MCP commands. The model holds no state server-side. The skill and plugin .md files only exist on the endpoint, so the harness resends the full context to the model on every turn. Anything that reaches the model reaches the Compliance API, which is pretty amazing for governance and monitoring. Parsed the right way, session transcripts let you log tool usage and build an inventory of your agents: each one's skills, the MCP servers it uses, and its plugins. The Compliance API also covers administrative actions, mostly at the organization level and less so for individual users changing configs. I expect that to widen over time. Why you might still need OpenTelemetry OpenTelemetry (or OTel) is an open-source standard for traces, metrics, and event logs, and every common harness has it built-in, only needing to be configured. Some actions on the endpoint never reach the LLM, so the Compliance API never sees them. Hooks are the clearest case: they run locally, between the model's decision and the tool actually running, and can block a tool from executing or a prompt from being sent. OTel also records tool-permission decisions and who made them, whether a policy, a hook, or the user waving it through. Also, permissions changes moved into bypassPermissions / auto mode will be logged in OTel but not the compliance API. Transcripts vs. logs OTel was built for logging atomic actions. Session transcripts are long, deeply descriptive JSON with no verbosity dial, and you have to process them to get the same logging outcome. If you don't want to collect and store extremely dense transcripts, OTel might be the easier tool (until a better one exists). And there’s a hard boundary: If you run Claude Code on a model that isn't Anthropic's, you get no Compliance API coverage at all, because it only logs interactions with Anthropic's models. Sessions running on Bedrock, Foundry, or Google Cloud won't be covered. One important note: local session transcripts can contain sensitive data, including PII, secrets, and customer data. Their storage becomes a sensitive data source in its own right. Treat it like one. Layer 3: What only the endpoint can tell you The Compliance API and OTel capture what agents DO. Neither can see what sits on disk: config files, installed skills and plugins and their .md files (unless they were used in a session), or processes launched outside a session. This is where an endpoint agent earns its keep. Harvest config files, retrieve skill and plugin .mds, and correlate EDR logs to catch risky bash commands coming from agents. Token Security finds an average of more than 10 configuration files per local agent, scattered across the endpoint. One more thing lives on disk that you can also pull from the Compliance API: session transcripts. Claude Code stores all session history locally for 30 days by default, so users can quickly resume previous work. A malicious actor who gains access to the endpoint can also read those files, so the same caution applies. Don't forget transcripts when you build a coverage plan. Start with responsible use: keep users from writing raw secrets into sessions, label projects and sessions that hold customer or sensitive data, and delete them on a schedule. Then add detection and response: find user prompts that contain cleartext secrets, and act on sessions that could compromise customer data. A little about parsing transcripts To get atomic-action logging out of your Claude Code sessions, you process the Compliance API transcripts. As above, each message is only text, tool_use, or tool_result, wrapped in fields like user, assistant (the LLM's responses), and more. Finding the actual plugins, skills, and MCP servers takes a few extra techniques. Bash commands The easy case: every command shows up as a tool_use with "name": "Bash", and the full command line sits in the input value. MCP servers These appear as a tool_use whose name is mcp . First-party servers use readable names, so you can often tell the type at a glance: Jira, Slack, Notion. User-connected servers show up as a UUID instead, and you recover the service from the command suffix (slack_send_message) or from a UUID-to-name map you maintain. Every one of these represents a standing credential on that endpoint, and roughly a third come from outside the vendor ecosystem: 35.1% of MCP servers Token Security discovers are community-built or of unknown origin. Skills Skills aren't named in a field the way MCP commands are, but you can infer them. When a skill fires, the LLM can't use it without context, so the harness sends the SKILL.md over the API, either by injecting the skill content directly or by issuing a Read on its path. That Read gives away both the skill name and its location: the tool_use input holds the path, and the tool_result text holds the skill's content. Plugins Plugins are harder, because a plugin isn't a single file. It bundles different extension types, including skills and scripts. You recover plugin names through path conventions, when one of a plugin's script or .md files is Read into context. All of this works without touching user prompts, on tool_use blocks and command lines alone. Managed settings + local session transcripts + endpoint collection = Good but not enough Claude Code's design creates challenges that no single layer can answer. Together, the three do a better job, but still fall short: Even all three are not enough because none of them captures the context of your enterprise, and they don’t go deep enough in tying access to intent. An admin reviewing the transcripts can't tell the difference between a malicious plugin pulled from the internet and a legitimate one written by an engineer. Closing that gap requires context from elsewhere in the organization, such as correlating the skills and plugins running on endpoints with those your internal repositories actually manage, which increases legitimacy. Once you have that context across the org, detecting a single malicious skill gives you a heat map of where it runs, and mitigation moves fast. Telemetry can show what happened. Governance requires connecting those signals to an agent’s owner, purpose, identities, credentials, permissions, and access paths. That context makes it possible to determine whether access is justified, right-size it to least privilege, and revoke it when the agent’s purpose ends. Identity is the control plane that turns endpoint and session data into enforceable AI agent security. Learn how Token secures AI agents across endpoints, cloud, SaaS, and developer environments with a quick demo, anytime. Note: This article has been expertly written and contributed by Dan Abramov, Security Researcher.
thehackernews.comAug 31, 2026extracted
Halo-record: Open-source audit trails for AI agents
Halo-record: Open-source audit trails for AI agents Brian Kuan wrote halo-record, a small Python package that sits inside an AI agent and writes down the moves it makes: tool calls, model calls, data access, approvals. Each action becomes one line in a file that only ever gets appended to, and every line carries a hash of the line before it, a hash being a short fingerprint computed from content. Edit a record later and every fingerprint after it stops matching. The code is open source, and anyone can run that check with no key, no account and no permission from the vendor whose agent produced the log. A customer’s security team asks what your agent did with their data, and the answer they get is a paragraph you wrote about yourself. A hash-chained log replaces the paragraph with a file the customer can test. Wrapping an agent takes one line of Python, and adapters pull records in from OpenTelemetry spans, LangChain, MCP servers and gateway logs, so the evidence can come from tooling you already run. Raw arguments never enter a record. Values get hashed and kept as a redacted summary, and the redaction is pattern matching against common secret and personal-data formats. The package has no runtime dependencies and runs about 5,300 lines of Python, which matters when the recorder goes inside your own product. Nothing was edited is not the same as nothing is missing A chain you hold yourself proves one thing: no record was altered or reordered after it was written. It cannot prove that every record was written. Delete the embarrassing Tuesday, re-seal the chain, and the file stays internally consistent. Closing that hole takes a witness, some party outside the operator’s control that periodically stores two numbers: how many records exist and the hash at the head of the chain. A witness you run yourself proves integrity to you and nothing to your customer. A hosted witness service is also how Kuan intends to fund the work. Why a vendor would sign up for a log it cannot edit “Vendors volunteer because it closes deals,” Kuan told Help Net Security. An agent vendor walks into a security review today carrying a certification built for deterministic software, an expensive new audit standard, or its own logs. On the last of those, he said: “handing a prospect your own logs and asking them to trust you breaks the oldest rule in assurance. It’s why SOC 2 requires an independent AICPA-accredited auditor in the first place.” “It’s early. Adopters are mostly engineers and security researchers kicking the tires, but the mandate pressure is just starting to form, from standards like AIUC-1 that now require tamper-evident runtime logging, and from insurers starting to ask the same questions,” Kuan said. Pressure of a less voluntary kind is arriving too. “July’s Hugging Face intrusion was run by an autonomous agent – roughly 17,600 actions over five days – and their own write-up says reconstructing what it did by hand was impractical, and that only some of the agent’s logs could be retrieved,” Kuan said. Take the arithmetic from that number: a responder reading one action per minute needs twelve straight days to get through the week. The format is open, and that is on purpose Kuan controls the record format today, and it is versioned, public and small enough that anyone can implement it without asking. “If a big platform ships its own incompatible version, that mostly proves the point – the market agrees the evidence matters,” he said. “And honestly, the more the format gets copied, the better – the format was never the moat. Any variant still faces the same question: can anyone besides the vendor verify it?” Nobody gets certified by any of this. A report gives an assessor something checkable next to record-keeping language in the EU AI Act and the action receipts in the Cloud Security Alliance’s AARM model. Halo-record is available for free on GitHub. Must read: 20 open-source cybersecurity tools to keep your team ready for anything GitHub CISO on security strategy and collaborating with the open-source community Subscribe to the Help Net Security ad-free monthly newsletter to stay informed on the essential open-source cybersecurity tools. Subscribe here!
helpnetsecurity.comAug 31, 2026extracted
NCSC-2026-0326 [1.00] [M/H] Kwetsbaarheden verholpen in Keycloak
Red Hat heeft meerdere kwetsbaarheden verholpen in Keycloak. De kwetsbaarheid met CVE-2026-18963 kan een ongeauthenticeerde externe aanvaller in staat stellen om elke gebruikersaccount over te nemen door een wachtwoordreset af te dwingen. Door deze kwetsbaarheid valt het e-mailverificatieproces bij wachtwoordreset te omzeilen en wachtwoorden van gebruikers te wijzigen. In Keycloak zijn daarnaast kwetsbaarheden aanwezig in de Fine-Grained Admin Permissions (FGAP) v2, waardoor bepaalde beheerders met beperkte permissies metadata van verborgen groepen kunnen inzien en details van ongeautoriseerde oudergroepen kunnen blootstellen. Verder kunnen geauthenticeerde aanvallers via een legacy client-initiated account-linking endpoint CSRF-bescherming omzeilen en accounts overnemen. Ook kunnen gedelegeerde beheerders met alleen leesrechten client secrets uitlezen door een fout in de secret rotation. Voor OpenTelemetry Java was er een kwetsbaarheid waarbij het ontbreken van limieten op baggage headers leidde tot onbegrensd geheugen- en CPU-gebruik, wat Denial-of-Service kan veroorzaken. Bij Jackson-libraries is een bypass van de @JsonIgnore annotatie in Java Records vastgesteld, waardoor ongewenste toewijzing aan constructorparameters mogelijk is. Ook zijn er bypasses van @JsonIgnoreProperties, ontbrekende view guards op @JsonUnwrapped en ongeautoriseerde schrijfacties op velden met @JsonView. Oracle Database Server's Fleet Patching and Provisioning component is eveneens getroffen door een kwetsbaarheid die ongeautoriseerde data toegang of Denial-of-Service kan veroorzaken.
advisories.ncsc.nlAug 25, 2026extracted
CVE-2026-15737 - Sensitive content disclosure via OpenTelemetry spans in AgentCore Python SDK
CVE-2026-15737 - Sensitive content disclosure via OpenTelemetry spans in AgentCore Python SDK Bulletin ID: 2026-058-AWS Scope: AWS Content Type: Important (requires attention) Publication Date: 07/16/2026 10:00 AM PDT Description: Bedrock AgentCore Python SDK (bedrock-agentcore) is an open-source Python library that provides client tools for building AI agents on the Amazon Bedrock AgentCore platform. We identified CVE-2026-15737 in the OpenTelemetry instrumentation of the SDK. Affected versions wrote raw user prompts and complete agent responses into OpenTelemetry span attributes on every invocation without filtering or masking. These spans flow into the customer's aws/spans CloudWatch log group, where a local authenticated user with CloudWatch Logs read access could access the potentially sensitive content. Impacted versions: 1.4.8, 1.5.0 Resolution: This issue has been addressed in Bedrock AgentCore Python SDK version 1.5.1 or later. We recommend upgrading to the latest version and ensuring any forked or derivative code is patched to incorporate the new fixes. Users who ran affected versions should also review and purge sensitive content from their aws/spans CloudWatch log groups. Workarounds: No workarounds are available. Upgrade Bedrock AgentCore Python SDK to version 1.5.1 or later. References: Please email [email protected] with any security questions or concerns.
aws.amazon.comAug 20, 2026extracted
Future AGI: Open-source platform for shipping self-improving AI agents
Future AGI: Open-source platform for shipping self-improving AI agents Future AGI is an open-source platform for tracing, evaluating, simulating, and guardrailing LLM agents, licensed Apache 2.0 and self-hostable. Self-hosted instances register with Future AGI on first boot and send an instance ID, a version string, a deployment type, and the email addresses and domains of active admin users. That registration fires once, before anyone signs in to the dashboard. The opt-out is one environment variable, FUTURE_AGI_TELEMETRY_DISABLED=1, placed in .env ahead of the first start. Miss the window and the admin list has already left the network. Future AGI lists air-gapped and on-prem deployment as supported and phone-home free. Disabling telemetry leaves one census ping carrying instance ID, version, and deployment type, with emails withheld. Periodic heartbeats stop. Future AGI’s telemetry section closes with a line for operators: “turn networking off at the edge if you need full silence.” What the install holds The installer brings up ClickHouse, PostgreSQL, Redis, RabbitMQ, and Temporal behind a dashboard on localhost:3000. Spans land in ClickHouse. Prompt text, model output, and tool calls pass through the tracer on the way into that store. Instrumentation covers 50+ agent frameworks, including LangChain, LlamaIndex, CrewAI, and DSPy, through OpenTelemetry. A defender inherits the access controls on that database along with the traces. The gateway sits in front of everything The Agent Command Center is an OpenAI-compatible proxy fronting 100+ providers, with routing strategies, semantic caching, virtual keys, MCP, and A2A. Every provider credential in a deployment terminates there. Protect ships 18 built-in scanners for PII, jailbreak, and prompt injection, plus vendor adapters covering Lakera, Presidio, and Llama Guard. They run inline in the gateway or standalone through the SDK. Inline scanning holds P99 latency at or under 21 ms on the project’s benchmark harness. Future AGI is available for free on GitHub. Must read: 20 open-source cybersecurity tools to keep your team ready for anything GitHub CISO on security strategy and collaborating with the open-source community Subscribe to the Help Net Security ad-free monthly newsletter to stay informed on the essential open-source cybersecurity tools. Subscribe here!
helpnetsecurity.comAug 5, 2026extracted
AWS centralizes access, spending, and governance for Claude
AWS centralizes access, spending, and governance for Claude Claude apps gateway for AWS is a self-hosted control plane that gives organizations a single point of control over access, costs, and policies for Claude Code and Claude Desktop. It replaces per-developer cloud credentials, manual distribution of managed settings to developer laptops, and centralizes usage attribution and spending controls. The gateway can be deployed with Amazon Bedrock or Claude Platform on AWS, providing the same capabilities in either environment. Claude apps gateway architecture for AWS (Source: AWS) How the gateway works Built into the Claude Code CLI, the gateway lets developers use the same client they already have. It runs as a stateless service with a PostgreSQL database for authentication state and rate limiting. Managed settings are applied automatically during sign-in, and policies are enforced on every request. “Onboarding and offboarding follow your existing identity workflows. To grant access, add a developer to your identity provider (IdP). To revoke it, remove them, and their session expires within the configured token lifetime (one hour by default). No long-lived secrets live on developer machines,” the blog authors explained. Its five core functions are identity, policy, telemetry, routing, and spend caps. The gateway integrates with OpenID Connect (OIDC) identity providers for single sign-on and issues short-lived tokens to developers after authentication. Administrators can centrally manage policies, including model access, tool permissions, and settings based on user groups. It exports usage telemetry through OpenTelemetry to monitoring platforms, routes inference requests to Amazon Bedrock or Claude Platform on AWS, supports cross-Region and cross-account deployments, and enforces spending limits for organizations, groups, and individual users.
helpnetsecurity.comJul 9, 2026extracted
Agent Beacon: Open-source telemetry layer for AI agents
Agent Beacon: Open-source telemetry layer for AI agents AI coding agents such as Claude Code, Codex CLI, Cursor, and Claude Cowork run on developer laptops, CI jobs, cloud environments, where they edit files, run commands, and call outside tools. Beacon, an open-source project from Asymptote Labs, configures telemetry for those runtimes and writes a normalized record of what each agent does across local, CI, and cloud-agent surfaces. What Beacon collects Beacon discovers supported local runtimes on a host and configures data collection for them, while also supporting additional CI and cloud-agent telemetry paths. It configures supported runtimes such as Claude Code and Codex CLI to export OpenTelemetry data to a collector running on localhost. For Cursor, it installs hooks that emit local endpoint events covering sessions, prompt submission, tool use, command execution, MCP-like tool activity, approval decisions, and file edits where Cursor exposes those payloads. A bundled collector converts the OpenTelemetry logs and hook events into a normalized JSON event log, and a local dashboard reads that log for inspection. Teams can also forward the data into supported customer-managed SIEMs. The software runs locally and works without an Asymptote account. It records full telemetry by default, with configurable retention modes for leaving out prompt text, raw attributes, command output, and raw diffs. Two added retention modes exist: a redacted mode that includes configured content fields after local redaction and size limits, and a third mode that keeps configured content fields in local or customer-controlled logs, subject to event size limits. The dashboard binds to loopback and reads the runtime log. Written mostly in Go and released under the MIT license, Beacon installs on macOS through a Homebrew tap. Claude Cowork support requires OpenTelemetry export configured by a Team or Enterprise admin in the Claude admin console, with an OTLP endpoint reachable over the public internet. From visibility toward governance Asymptote Labs calls Beacon the world’s first open-source telemetry layer for AI agents wherever they run (locally, in CI, or in the cloud), and its aim reaches past observation. CEO Justin D’Souza wants agent activity to become observable, understandable, and eventually governable across an enterprise, with the visibility layer coming first. “The precondition for governance is a visibility layer built on a normalized schema of agent activity across local + CI + cloud agent harnesses,” D’Souza told Help Net Security. “We believe we are the first to do this in a comprehensive way, significantly extending OpenTelemetry genAI standards.” D’Souza laid out the work ahead: “In practice, the governance layer builds on our open source foundation. It requires 3 key ingredients: 1) a detection rule standard to codify suspicious agent activity, 2) a policy layer for enforcing controls and gating agent actions in real time and 3) infrastructure scaffolding designed around a streaming-first architecture, so agent actions can be ingested and evaluated in near real time across enterprise environments.” He said a detection rule standard remains an open problem, which he ties to the industry’s lack of a normalized schema for agent activity. “This is what we’re pushing for, and what we think will differentiate our approach,” D’Souza said. A gap in endpoint detection D’Souza sees a gap in what endpoint detection and response tools collect. “Existing EDR tools miss understanding agent behaviour, e.g. what the agent is trying to do, what tools it invokes, what context it uses, and how those actions map to a broader task a user is trying to accomplish,” he said. He named CrowdStrike’s EDR and AIDR products as an example. “We’re currently working with early design partners who already use CrowdStrike EDR and Falcon in their security stack,” D’Souza said. “They’re using Agent Beacon as this missing agent visibility layer. That early signal gave us conviction to keep going in this direction.” Visibility and developer trust Recording prompt text and agent commands raises questions about developer privacy. D’Souza said security teams need visibility into agent activity “to do their job and protect the enterprise from catastrophic loss,” and that “developers need to trust that this visibility is being used responsibly.” He plans to give organizations control over how telemetry gets collected and used, including “redaction and policy-based controls for sensitive data fields like prompts” and limits on which user groups can reach that data. His goal for Beacon, D’Souza said, is “for developers and enterprises to feel safer about adopting AI.” The current public build supports agent runtimes across local endpoint, CI, and cloud-agent telemetry paths. It omits kernel and process monitoring, shell history collection, broad browser and SaaS telemetry, and general-purpose credential-use attribution. Agent Beacon is available for free on GitHub. Must read: 25 open-source cybersecurity tools that don’t care about your budget GitHub CISO on security strategy and collaborating with the open-source community Subscribe to the Help Net Security ad-free monthly newsletter to stay informed on the essential open-source cybersecurity tools. Subscribe here!
helpnetsecurity.comJun 22, 2026extracted
Tigera introduces unified control plane for Kubernetes-based AI agent security
Tigera introduces unified control plane for Kubernetes-based AI agent security Tigera has announced the general availability of Tigera Lynx, a unified control plane for Kubernetes-native AI agents. Lynx gives enterprises a single place to find every agent in their Kubernetes estate, tighten security posture, assign sandboxes, provide each agent with a cryptographic identity, enforce policy on every action it takes, audit agent activity, and detect anomalous behavior, all without changing a line of agent code. AI agents do not behave like the workloads enterprise security stacks were built to manage. They are autonomous and non-deterministic: acting on behalf of users, accessing tools, LLMs, and other agents, operating through delegation chains, and consuming untrusted input. As a result, three teams often view the same challenge from different perspectives. AI teams want to experiment with new technologies and move quickly. Platform engineering teams are measured on deployment velocity but cannot easily prove the platform is under control. Security teams are asked to approve agents without being able to confidently assess their security posture. A valid credential does not guarantee safe behavior, and the blast radius can change every time a new agent, tool, or platform update is introduced. Lynx sits in the path of every agent call, agent-to-agent, agent-to-tool, and agent-to-LLM, to authenticate, authorize, mediate, and audit each one. It plugs into the tools enterprises already run, including their identity provider (EntraID, Okta) or via SPIFFE/SPIRE, and existing observability systems, and is built on open standards rather than proprietary lock-in. One control plane, five capabilities Discovery, registration, and observability. A central registry catalogs every agent with its owner, purpose, and version, while eBPF-powered auto-discovery finds agents nobody registered. Shadow agents are flagged and quarantined, and any agent’s actions can be reconstructed end-to-end through OpenTelemetry traces. Configuration and posture management. AI-CSPM continuously evaluates every agent against a baseline, surfacing drift and over-permissions the moment they happen, with per-agent sandboxing and pre-built compliance packs mapping to GDPR, HIPAA, SOC 2, and financial services requirements. A Red Team Agent continuously probes for weaknesses in posture and misconfigurations. Identity and authentication. Every agent gets a verifiable cryptographic identity through integration into an enterprise’s identity provider (EntraID, Okta) or through SPIFFE/SPIRE, with no shared secrets. Long-lived API keys are replaced by short-lived and tightly scoped, auto-rotated tokens. A JWT token is minted for every hop in a multi-agent workflow. Policy definition and enforcement. A single default-deny policy governs LLM, MCP, and agent access using the Cedar policy language, enforced at the gateway before any call executes — with no agent code changes. Misbehaving agents can be quarantined instantly and high-stakes calls routed to a human. Anomalous behavior detection. eBPF and LSM watch every syscall, network call, and file access at a layer agents can’t tamper with, catching credential theft and lateral movement even when an action passes policy. This provides a forensic audit trail. Guardian Agent detects anomalous behavior and quarantines suspicious agents. “For over a decade, Tigera’s Calico platform has served Global 2000 companies running the largest Kubernetes platforms in the world, securing tens of millions of mission-critical transactions every day. AI agents are the next class of workloads: autonomous, distributed, and increasingly embedded in critical business processes. Lynx brings that same unified control and security rigor to AI agents. We’re building on our core competency — securing mission-critical workloads at scale on Kubernetes, in a highly performant way,” said Ratan Tipirneni, CEO of Tigera. “Control only matters if it’s enforced uniformly. Lynx gives every agent a cryptographic identity, scopes credentials to a single hop, and evaluates every LLM, MCP, and tool call against a default-deny policy at the gateway — with no agent code changes. Because we watch behavior with eBPF and LSM at the kernel, we can detect an agent going wrong even when it carries a valid credential, and produce a reproducible audit trail to prove it,” said Peter Kelly, Chief Technology Officer of Tigera.
helpnetsecurity.comJun 17, 2026extracted
New Relic expands observability into AI-assisted software development
New Relic expands observability into AI-assisted software development New Relic has announced AI Coding Observability, an open-source tool for monitoring AI-assisted software development workflows. As organizations adopt AI coding assistants, these tools often operate outside existing observability systems, limiting visibility into their use. AI Coding Observability extends monitoring into the software development process, enabling organizations to track, analyze, and audit AI-assisted coding activities. “You can’t manage what you can’t see. AI coding assistants are having a measurable impact on businesses, but without real-time oversight into how they’re behaving, organizations are scaling risk as fast as they’re scaling output,” said New Relic Chief Product Officer Brian Emerson. “New Relic AI Coding Observability will close this gap, removing barriers to quality innovation that succeeds in production.” Bringing enterprise-grade rigor to the AI coding phase AI coding tools are now producing more code than ever, as Gartner predicts that 90% of enterprise software engineers will use AI code assistants by 2028. However, engineering organizations rarely standardize on just one AI tool. Instead, developers leverage a highly fragmented mix of coding assistants depending on the task. New Relic AI Coding Observability is being designed to future-proof development strategies and introduce a unified, vendor-neutral pane of glass that normalizes telemetry across the major AI coding assistants and correlates it seamlessly with existing production infrastructure. Key features and benefits of New Relic AI Coding Observability will include: Gain insights into code development actions – Teams will be able to move away from blind trust in their AI coding tools to gaining comprehensive insights into how these tools are actually behaving as applications and services are developed. Exercise cost control – AI coding assistants are a rapidly growing line item, yet most organizations treat them as an unmonitored expense. New Relic AI Observability will allow teams to track AI spend and eliminate black box invoices, and forecast spend against budgets and alert before thresholds are hit. Enhance productivity metrics – To understand productivity gains from coding assistant use, the capability will replace anecdotal success stories with hard data and catch inefficiencies and hidden failure modes. Ensure security and compliance – Local-only / zero-outbound mode will run queries entirely within the user’s private network, guaranteeing data sovereignty, privacy and regulatory compliance. Receive strong code transparency – Will eliminate black-box AI skepticism by providing fully readable, open-source and source-available code, empowering engineering and security teams to independently verify data privacy protocols and AI reasoning with certainty. Avoid vendor lock-in – Backed natively by the OpenTelemetry protocol and Model Context Protocol (MCP), the feature will deliver true vendor-neutrality that allows organizations to seamlessly port their telemetry data and AI workflows across any cloud ecosystem or language model provider. Provides broad coding assistant coverage – Will support Claude Code, Cursor, GitHub Copilot, Windsurf and Amazon Q. New Relic AI Coding Observability will be available as an open-source feature on June 23 at no additional cost.
helpnetsecurity.comJun 8, 2026extracted
Qumulo NeuralProtect uses AI to detect and stop ransomware before encryption
Qumulo NeuralProtect uses AI to detect and stop ransomware before encryption Qumulo has unveiled Qumulo NeuralProtect, a ransomware resilience solution built to protect data at the storage layer by detecting and stopping threats before data is encrypted, corrupted, or lost. Integrated directly into the Qumulo Data Platform, NeuralProtect inspects every file at the precise point-of-write using a series of AI-driven analysis models to detect both known and zero-day threats, instantly isolating malicious activity and enabling rapid recovery. NeuralProtect shifts ransomware protection from reactive recovery to proactive prevention. As a native capability of Qumulo Core, Azure Native Qumulo (ANQ), and Cloud Native Qumulo (CNQ) deployments, NeuralProtect protects data consistently across on-premises, hybrid, and cloud-native Qumulo environments. It is even more compelling when combined with Cisco Hypershield and Splunk. Qumulo NeuralProtect and Cisco Hypershield enable an instant data-to-network response, quarantining malicious actors across the entire network. Operating with an industry-leading false-positive rate of less than 0.01%, this integrated system reduces detection-to-mitigation time to seconds. It isolates compromised systems and instantly notifies Splunk of the specific client, the malware variant, and the exact point of intrusion, preventing further infection and protecting crucial data for forensics and response resolution. “Ransomware doesn’t target your backups first; it targets your live data. NeuralProtect is the first solution built to stop attacks at exactly that point, at the data layer, before a single file is compromised, avoiding costly and lengthy recoveries,” said Kiran Bhageshpur, Qumulo CTO. “Combined with Cisco Hypershield and Splunk, we’ve built what the industry has long needed: a coordinated cybersecurity architecture that spans storage, infrastructure, and security operations, so enterprises can respond to known and emerging threats without sacrificing business continuity.” Deep File Inspection: Zero-day prevention at user-space speed Traditional protection strategies, such as backup platforms, endpoint security, and entropy-based storage detection, remain fundamentally reactive. They rely on indirect anomaly detection rather than guarding live enterprise data where the real risk exists. NeuralProtect performs Deep File Inspection on every file as it is written, neutralizing threats instantly before data can be lost or encrypted, all without any performance impact. NeuralProtect’s detection engine combines multiple AI models to identify every category of threat including: Deterministic AI model: Identifies known ransomware and malware variants with 100% accuracy Statistical AI model: Detects zero-day and novel attacks with greater than 95% success Temporal AI model: Surfaces stealth campaigns and slow-moving, partial-encryption attacks BitDefender Virus Detection Engine: Adds a proven commercial antivirus layer for comprehensive coverage When a threat is detected, NeuralProtect can respond autonomously and immediately. The offending user session is terminated, the user or IP address is blocked, defensive snapshots are created, and infected data is quarantined thereby stopping the spread of attacks and accelerating recovery to a clean state. Coordinated storage and network defense with Cisco Hypershield NeuralProtect is integrated with Cisco Hypershield to extend protection beyond the storage layer into coordinated, enterprise-wide threat containment. When NeuralProtect detects an attack, the joint solution triggers automated network quarantine, distributed enforcement across workloads and clouds, and rapid incident isolation within seconds. Telemetry from Qumulo and Cisco flows directly into Splunk via OpenTelemetry integration, giving security operations teams unified visibility and faster, more informed incident response across the entire infrastructure stack. Qumulo and Cisco unify storage intelligence and network enforcement to deliver a coordinated ransomware response that no single vendor can provide on its own.
helpnetsecurity.comMay 28, 2026extracted
The npm Threat Landscape: Attack Surface and Mitigations
The security of the npm ecosystem reached a critical inflection point in September 2025. The Shai-Hulud worm, a self-replicating malware that automated the compromise and redistribution of malicious packages, marked the end of the “nuisance” era of npm attacks and the beginning of a high-consequence threat landscape. Since that watershed moment, Unit 42 has tracked an aggressive acceleration in the frequency and technical depth of supply chain compromises. Attacks have evolved from a series of isolated typosquatting incidents into systematic campaigns by various threat actors to weaponize the trust that powers modern software development. April 2026 Campaigns We have seen two campaigns in April: the first started April 22, 2026 and included the string Shai-Hulud: The Third Coming. The second started April 29, 2026 and is known as Mini Shai-Hulud. May 2026 Campaigns In May 2026, the Mini Shai-Hulud campaign continued with two new waves attributed to TeamPCP. These campaigns introduced two unique elements. One campaign used a credential-free initial access technique. The other campaign generated the highest single-hour package count of any Shai-Hulud worm to date. Copycat activity has made future attribution to TeamPCP more difficult. June 2026 Campaign A new supply chain attack on June 1, 2026 compromised at least 32 packages published under the @redhat-cloud-services npm namespace. The attacker bypassed code review entirely, pushing a payload named Miasma. July 2026 Campaign Attackers compromised the release pipelines of four core AsyncAPI GitHub repositories on July 14, 2026. In a campaign calling itself miasma-train-p1, they published five trojanized packages to npm: @asyncapi/[email protected] @asyncapi/[email protected] @asyncapi/[email protected] @asyncapi/[email protected] @asyncapi/[email protected] The payload appears to be a descendant of the Miasma remote access Trojan (RAT). The Shai-Hulud incident proved that the npm registry could be used as a force multiplier for malware distribution. In the months following, we have observed three core shifts in adversary TTPs: Wormable propagation: Malicious payloads now prioritize the theft of npm tokens and GitHub Personal Access Tokens (PATs) to automatically infect and republish legitimate packages, as seen in the March 2026 Axios compromise. Infrastructure-level persistence: Attackers are no longer just stealing data; they are embedding themselves into continuous integration/continuous delivery (CI/CD) pipelines to attain long-term, undetectable access to enterprise environments. Multi-stage payloads: Following the September 2025 template, current attacks often deploy dormant “sleeper” dependencies that only activate under specific environmental conditions to evade automated scanners. npm Attacks Seen As a Whole npm compromises have common themes. In the post-Shai-Hulud era, we believe it is helpful to consider the attack surface as a whole. This article will combine: Details of major incidents: Real-time analysis of significant package compromises (e.g., Shai-Hulud 2.0, Axios, Chalk/Debug) Cross-campaign correlation: Identifying common infrastructure or code snippets that link disparate attacks to the same threat actors Remediation playbooks: Actionable guidance for rotating credentials and purging malicious dependencies from local and cloud-based caches Shai-Hulud: A New Wave A malicious npm package published as @bitwarden/cli version 2026.4.0 was identified as part of a broader supply-chain campaign attributed to TeamPCP. The package impersonates the legitimate Bitwarden command-line interface (CLI) password manager. Upon installation, it executes a multi-stage payload that steals credentials from cloud providers, CI/CD systems and developer workstations. It then self-propagates by backdooring every npm package the victim can publish. It has been noted that inside public GitHub repositories that were published contained the string “Shai-Hulud: The Third Coming.” Attackers deployed the same payload across multiple Checkmarx distribution channels, indicating a coordinated campaign to weaponize compromised developer tooling credentials to maximize the area of impact: Docker Hub images GitHub Actions VS Code extensions Palo Alto Networks customers are better protected from the threats described in this article through the following products and services: The Unit 42 Incident Response team can also be engaged to help with a compromise or to provide a proactive assessment to lower your risk. July 2026 - Miasma Expansion or Potential Copycat? On July 14, 2026, attackers compromised the release pipelines of four core AsyncAPI GitHub repositories, publishing five trojanized packages to npm: @asyncapi/[email protected] @asyncapi/[email protected] @asyncapi/[email protected] @asyncapi/[email protected] @asyncapi/[email protected] The campaign calls itself miasma-train-p1, and the payload appears to be a descendant of the same Miasma RAT deployed in the June 2026 Red Hat supply chain operation. However, the initial access was different this time. Rather than a compromised employee account compromising the GitHub repository, the attackers exploited a process gap in the CI/CD pipeline itself. The AsyncAPI repositories maintained strict branch protections and peer-review mandates on their primary main branches. However, pre-production release branches, specifically next and schema, were left unprotected. The threat actors pushed malicious commits directly to these shadow release branches that bypassed all human review (e.g., Commit 3eab3ec9304aa26081358330491d3cfeb55cc245 by attacker GitHub ID 148100). This commit triggered automated GitHub Actions build and release workflows. The injected code ran inside the Continuous Integration (CI) runner itself, harvesting NPM_TOKEN and GITHUB_TOKEN environment secrets, then used the stolen npm token to programmatically publish backdoored package versions to the trusted @asyncapi scope on the public registry. In contrast to the previous Miasma payload, this version’s architecture has changed. When a developer runs npm install on a compromised package, a backdoored source file executes, such as index.js, validator.js or utils.js. To evade static code-integrity audits, the file exports a legitimate-looking schemas object with version-keyed JSON references. However, upon import, its main() function triggers a detached child process running an obfuscator.io-obfuscated script. That script determines the operating system via process.platform and creates a platform-specific persistence directory disguised as a legitimate NodeJS data folder: %LOCALAPPDATA%\NodeJS on Windows ~/Library/Application Support/NodeJS on macOS ~/.local/share/NodeJS on Linux ~/.config/node as a fallback The payload then fetches the Stage-2 Miasma RAT from the InterPlanetary File System (IPFS) via a hard-coded content identifier (CID) Qmet4fhsAaWMBUxNDfREHwgiyDeSWy4YSYs9wiKUW5jGyf or QmQobZSp1wRPrpSEQ56qnyq7ecZh5Bg5k1fnjt4SUwwHb9. It then writes the RAT as sync.js and spawns it with detached: true, stdio: 'ignore' and windowsHide: true. The parent process calls child.unref() and exits cleanly, giving the perception that nothing is wrong. The RAT is now running headlessly in the background. Once executing, the Miasma RAT writes an operational lockfile, ~/.config/.miasma/run/node.lock. It does so to prevent redundant instances and to establish persistence via a user-scoped systemd service (miasma-monitor.service on Linux and macOS, or a miasma-monitor Run key on Windows). To bypass automated system audits, the RAT writes its host-tracking identity cache to paths that mimic legitimate OS application storage: ~/Library/Application Support/com.apple.spotlight/index-v2.cache on macOS (mimicking the Spotlight search index) ~/.cache/mesa_shader_cache/gl_cache.bin on Linux (mimicking the Mesa OpenGL shader cache) %HOME%\AppData\Roaming\Microsoft\CryptnetUrlCache\Content\msrt.dat on Windows (mimicking the Cryptnet certificate cache) Credential theft targets are consistent with the June campaign: GitHub tokens npm tokens SSH keys Cloud provider credentials Kubernetes service-account tokens CI/CD secrets Perhaps the most notable escalation is the command-and-control (C2) infrastructure. During the June Red Hat incident, the variant used a straightforward central C2 server. This campaign layers a decentralized fallback network on top of its primary C2 at 85.137.53[.]71. The RAT regularly polls hxxp://85.137.53[.]71:8080/api/v1/beacon for task commands. It also exfiltrates harvested credentials via HTTP POSTs to hxxp://85.137.53[.]71:8080/api/v1/file-result, forwarded to a dedicated upload listener on port 8081. If the primary C2 IP address was blocked, the RAT accessed the public Ethereum RPC gateway at ethereum-rpc[.]publicnode[.]com to examine an on-chain smart contract (0x12c37A86a0Ed0beBe5d1d6a43E42f07860eAc710), which functioned as a decentralized registry for the active C2 address. This contract was launched by the adversary wallet 0x92d4C5413e4F7B258a114964101F9e1C6d64C6Ba and included a backup contract located at 0x1969ab05d67b67fdcaa26240f738ccb077e1cd84. If that was also blocked, the RAT established communication with public Nostr relays (wss://relay.damus[.]io and wss://relay.nostr[.]com/) as out-of-band backup channels. Finally, it implemented a BitTorrent DHT bootstrap routine (using router.bittorrent[.]com:6881 and dht.transmissionbt[.]com:6881) to join a peer-to-peer network and discover alternate controllers. This was a highly redundant, resilient and evasive C2 architecture designed to survive standard domain and IP-level mitigation strategies. Blocking any single layer does not kill the implant. On July 14, 2026, Unit 42 researchers analyzed a compromised macOS developer machine on which the developer opened a project workspace in GitHub Copilot. When the copilot parsed the workspace and triggered automated dependency loading, it imported the trojanized @asyncapi/[email protected] package. This silently launched the Stage-1 loader, which fetched Miasma sync.js from IPFS and spawned it under Homebrew Node.js v22, initiating active beaconing to 85.137.53[.]71:8080. The developer did not run npm install manually. The tooling did it for them. The campaign also features self-attributed configurations that suggest this is merely the first wave of a broader, phased rollout. The worm's self-propagation capability is limited to four hops via a hard-coded generation cap (maxGen = 4). Additionally, its rollout approach leverages a default canary deployment strategy (batch.defaultStrategy = CANARY), which initially infects 5% of potential targets before scaling up in waves of 100. This is not runaway malware. It is rate-limited by design to manage exposure and avoid triggering widespread detection during the initial spread. Attribution remains the same open question as with the June 2026 campaign. The Miasma campaign shares infrastructure hosting patterns with TeamPCP, including the same Dutch autonomous system (AS43641/VSYS-AMS) used in prior operations against AntV, TanStack and Red Hat. With the addition of this miasma-train-p1 campaign identifier it can be tied directly to the lineage that includes Mini Shai-Hulud and the June Red Hat compromise. It is difficult to determine whether TeamPCP threat actors are directly behind this campaign. The Mini Shai-Hulud source code has been public since May 12 and nothing in this operation requires insider access to TeamPCP tooling. On June 1, 2026, a new supply chain attack compromised at least 32 packages published under the @redhat-cloud-services npm namespace, with the malicious versions cumulatively averaging approximately 80,000 weekly downloads. The root cause was a compromised Red Hat employee GitHub account, used to push malicious orphan commits to multiple RedHatInsights repositories, bypassing code review entirely. The attacker triggered GitHub Actions workflows to request OpenID Connect (OIDC) tokens, publishing Trojanized packages with valid SLSA provenance. The certificate was accurate, the packages really were built by that pipeline. It just also happened to have malware injected into it at the time. The payload is called Miasma. It is named after the description that the malware stamps on attacker-created GitHub repositories, "Miasma: The Spreading Blight." The threat is derived from the Mini Shai-Hulud malware open-sourced by TeamPCP on May 12, with substantially identical tradecraft. The analyzed sample replaced a normal approximately 200 KB index.js with a 4.29 MB obfuscated payload. This is a 25x size increase that is itself a reliable detection signal. Stolen credentials include: GitHub tokens, npm tokens and SSH keys AWS, GCP and Azure credentials and cloud identities Kubernetes service-account tokens and HashiCorp Vault secrets CI/CD secrets from GitHub Actions, CircleCI and related platforms Attribution remains uncertain. The TTPs are consistent with TeamPCP, but the public release of the Mini Shai-Hulud source code means any competent actor can replicate the same attack. What is certain is the trend, which is that one compromised account and one CI pipeline delivered 32 trojanized packages automatically to every developer who ran npm install. Then the registry does the rest. May 2026 - Mini Shai-Hulud Continues Two further waves in May 2026 continued the Mini Shai-Hulud campaign: The first introduced a fundamentally new initial-access technique that requires no stolen credential and produced the first malicious npm packages with valid supply chain levels for software artifacts (SLSA) provenance The second demonstrated the largest single-hour package count of any Shai-Hulud wave Both are attributed to TeamPCP, though the public release of the worm's source code on May 12 has already spawned separate copycat activity, complicating future attribution. May 11, 2026: Mini Shai-Hulud Strikes Again On May 11, TeamPCP launched a coordinated supply chain attack across the npm and PyPI ecosystems. The initial vector was TanStack's GitHub Actions CI pipeline. Within six minutes, 84 malicious package artifacts were published across 42 @tanstack/* packages. The worm's self-propagation mechanism then expanded rapidly. By end of day, we had documented 373 malicious versions across 169 npm packages plus compromised PyPI packages. The affected scope went well beyond TanStack. The worm's self-propagation spread the compromise to packages across multiple industries and ecosystems: Enterprise infrastructure: @opensearch-project/opensearch (the official OpenSearch JavaScript client; versions 3.5.3–3.8.0) and 57 @uipath/* enterprise automation packages AI tooling: @mistralai/mistralai and its Azure/GCP variants, which is the official Mistral AI TypeScript client Specialized ecosystems: 19 @squawk/* aviation data packages, [email protected] (customer messaging) and dozens of others across @tallyui, @draftlab, @beproduct, @mesadev and several unscoped packages @tanstack/react-router alone receives over 12.7 million weekly downloads. We estimate 520 million cumulative downloads were in the affected window. Palo Alto Networks provides XDR and XQL queries to detect this activity. A New Initial Access: No Stolen Credential Required Every prior Shai-Hulud wave began with a stolen or phished credential. The TanStack attack needed neither. Instead, three GitHub Actions weaknesses were chained, none of which was sufficient alone. Step 1: Pwn Request On May 10, the attacker created a fork of TanStack/router under the account zblgg/configuration, deliberately named to avoid appearing in fork-list searches. A malicious commit was authored under the spoofed identity claude , impersonating the Anthropic Claude GitHub App, and prefixed [skip ci] to suppress automated CI on push. A pull request (PR #7378) against TanStack/router#main then triggered bundle-size.yml — a workflow that used the pull_request_target trigger and checked out the fork's merge ref. This gave the fork's code execution in the base repository's runner context, with full access to its cache scope. The threat actors used Bun, which is a lightweight JavaScript runtime and package manager alternative to Node.js and npm as shown in Figure 1. The attack used Bun to execute the malicious payload tanstack_runner.js. This in turn attempted to enumerate the system for sensitive credentials, including invoking the GitHub CLI to capture the GitHub authentication token (gh auth token). Kubernetes In a Linux-hosted Kubernetes environment, Unit 42 observed legitimate runc create container activity associated with a build pipeline that subsequently retrieved and executed the compromised JavaScript package. The runc create invocation itself was legitimate Kubernetes runtime activity associated with the containerized build process. During the build: The project again dynamically retrieved and executed the Bun runtime through its pnpm dependency chain It subsequently executed the malicious JavaScript payload tanstack_runner.js Then proceeded as in the Windows environment The process lineage is shown in Figure 2. Step 2: GitHub Actions Cache Poisoning The fork's code wrote a 1.1 GB poisoned pnpm store under the exact cache key that release.yml would later look up. The key was pre-computed from the public pnpm-lock.yaml using the same hashFiles() formula the workflow uses. The poisoned cache entry then sat dormant for eight hours. A critical detail: actions/cache@v5's post-job save uses a runner-internal token, not the workflow GITHUB_TOKEN, so setting permissions: contents: read on the workflow does not prevent the cache write. Step 3: OIDC Token Extraction From Runner Memory When a legitimate maintainer pushed to main, release.yml triggered, restored the poisoned cache and executed attacker-controlled binaries during the build phase. Those binaries read /proc/ /mem and extracted the OIDC token — minted lazily in runner memory only when id-token: write is set — then POSTed it directly to registry.npmjs.org. The workflow's own Publish Packages step was never reached; tests failed and that step was skipped. npm received 84 valid, signed, provenance-attested package publishes anyway. This is the same /proc-memory extraction technique documented in the tj-actions/changed-files compromise of March 2025 and reused in the April 2026 SAP and Bitwarden waves. The SLSA Provenance Problem This is the first documented case of a worm publishing malicious npm packages with valid SLSA Build Level 3 provenance. Sigstore correctly attested that the packages were built by release.yml from refs/heads/main of TanStack/router — because they were. SLSA provenance confirms which pipeline built a package, not whether that pipeline's internal state was clean. The root cause is the same OIDC trust-scope misconfiguration exploited in the April 29 @cap-js wave. The trusted-publisher binding trusted the entire repository rather than a specific workflow on a protected branch. Provenance verification is necessary but no longer sufficient. This is why behavioral analysis at install time is essential. Payload and Propagation The malicious payload, router_init.js (2.3 MB obfuscated), was not delivered via a preinstall hook on the compromised packages themselves. Instead, each tarball received an injected optionalDependencies entry pointing to an orphaned commit in the attacker's fork. This is a commit that GitHub surfaces under the legitimate TanStack/router URL due to shared fork-network commit object storage: The dependency is designed to fail silently during installation. The malicious code executes in the background while the install process appears normal, leaving near-zero trace in logs. The payload uses multiple layers of obfuscation and encryption to resist automated analysis. It shares the same custom cipher documented in the April 22 Bitwarden and April 29 SAP sections, confirming shared authorship across all three waves. For secondary victims infected via worm propagation (e.g., UiPath, Mistral AI and OpenSearch), the delivery mechanism reverted to the familiar preinstall hook from the April SAP wave. The pattern is now well-established. Once the worm gains a foothold in one ecosystem, it uses stolen credentials to republish itself into every other package the victim maintains, rapidly expanding its reach across unrelated projects and organizations. The Dead-Man's Switch: A Critical Remediation-Order Warning The May 11 payload installs a persistent background service that polls api.github.com/user with the stolen GitHub token every 60 seconds. If the token is revoked (HTTP 40x), the service executes rm -rf ~/ — destroying the user's home directory. The daemon auto-exits after 24 hours. May 12, 2026: Public Release of the Worm On the evening of May 12, 2026, the fully weaponized Mini Shai-Hulud source code was published to public GitHub repositories before being taken down. The toolchain including the CI cache-poisoning scripts, OIDC token extractor and the credential stealer with its propagation logic is now publicly available. Mini Shai-Hulud is no longer scoped to TeamPCP. Future incidents using this toolchain may not share TeamPCP's infrastructure or tradecraft and should not be attributed solely on the basis of worm lineage. May 19, 2026: @antv Wave On May 19, 2026, the npm maintainer account atool was compromised as part of a new Mini Shai-Hulud wave. In approximately one hour, 639 malicious package versions were published across 323 unique packages. This is the largest single-hour package count of any Shai-Hulud wave to date. The affected scope spans the @antv data visualization ecosystem and related libraries: @antv/g2, g6, x6, l7, s2, f2, g, g2plot, graphin, data-set and s2-vue Packages outside the @antv namespace including echarts-for-react ( approximately 1.1 million weekly downloads), timeago.js, size-sensor and canvas-nest.js The potential area of impact across data visualization, graphing, mapping, charting and React component ecosystems is significant. Infection Mechanism Unlike the TanStack wave's pipeline-hijack technique, this wave returns to a simpler model. This involves compromising a maintainer account and using it to republish packages directly. This is the same approach seen in the September and November 2025 campaigns. The attacker modified each package's package.json in three ways: Adding a preinstall hook ("preinstall": "bun run index.js") that executes the malicious payload via the Bun runtime Bundling Bun as a dependency to ensure it's available on any machine Inserting a git-based optional dependency pointing to an orphaned commit in the legitimate antvis/G2 repository as a backup execution path To ensure the malicious versions reached as many targets as possible, the attacker also bumped version numbers beyond the latest legitimate release (e.g., @antv/s2-vue jumped from the real version 2.2.0 to 2.4.0). Any project using a permissive version range like ^2.x would automatically pull the malicious version on its next install. Payload Capabilities The 499 KB obfuscated payload runs six credential collectors in parallel, sweeping a broad range of targets: Developer credentials: GitHub tokens, npm tokens, SSH keys, Git credentials and private keys Cloud and infrastructure: AWS credentials and parameters, Kubernetes service-account tokens, HashiCorp Vault secrets and Docker authentication CI/CD platforms: Tokens from 18-plus platforms including GitHub Actions, GitLab CI, CircleCI, Vercel and Netlify Third-party services: Database connection strings, Stripe, Slack and Twilio API keys Password managers (new to this wave): The payload directly queries 1Password, Bitwarden, pass and gopass via their local CLIs All stolen data is encrypted and sent to a C2 endpoint disguised as OpenTelemetry trace ingestion (t.m-kosche[.]com), meaning network monitoring tools may classify the traffic as legitimate observability telemetry. A fallback channel exfiltrates data to GitHub repositories created under the victim's account, using Dune-themed names and the reversed campaign marker Shai-Hulud: Here We Go Again as the description. Late April Mini Shai-Hulud Wave As of April 29, 2026, a new supply chain attack wave (dubbed Mini Shai-Hulud) is actively targeting the SAP developer ecosystem via four compromised npm packages. The affected versions are: @cap-js/[email protected] @cap-js/[email protected] @cap-js/[email protected] mbt @1.2.48 Combined, these packages carry approximately 570,000 weekly downloads, with @cap-js/sqlite and @cap-js/db-service each pulling around 250,000 and 260,000 downloads, respectively. All four packages are part of SAP's Cloud Application Programming (CAP) Model and multitarget application (MTA) build toolchain. This makes the targets of this attack enterprise developers and CI/CD pipelines with access to cloud credentials, GitHub tokens and deployment secrets. The campaign is a close structural continuation of the @bitwarden/[email protected] compromise earlier in April 2026. It uses the same toolchain, same obfuscation and same propagation logic, which is now turned against the SAP ecosystem. Attack Mechanism Each compromised package received two new files: setup.mjs execution.js These files arrived along with a modified package.json that adds a preinstall lifecycle hook ("preinstall": "node setup.mjs"). This means the malicious code executes automatically during the npm install process, before the installation is complete. The setup.mjs bootstrapper detects the host OS and architecture, then performs the following activities: Downloading the Bun JavaScript runtime (v1.3.13) from the official github[.]com/oven-sh/bun releases Extracting the runtime to a temporary directory Immediately using it to execute execution.js Payload Capabilities The 11.7 MB single-file, obfuscated credential stealer, execution.js, is a propagation framework. It performs the following activities: Using a custom string scrambling layer labeled ctf-scramble-v2 to hide sensitive strings from static analysis Including a Russian locale killswitch (exiting silently if the system locale is set as ru) Daemonizing itself on non-CI machines to run in the background It harvests the following information: GitHub tokens (including gh auth token output) npm tokens from .npmrc Full environment variable blocks GitHub Actions secrets AWS STS identity Secrets Manager and SSM parameters Azure Key Vault secrets GCP Secret Manager values Kubernetes service account tokens Claude and MCP configuration files Electrum wallets VPN configs A particularly aggressive CI path uses an embedded Python helper that reads the /proc memory of the GitHub Actions Runner.Worker process to extract masked secret values. All collected data is: Compressed AES-256-GCM encrypted with a key wrapped under an embedded RSA public key Exfiltrated to freshly created public GitHub repositories with randomized Dune-themed names and the description A Mini Shai-Hulud has Appeared Propagation and GitHub Dead Drop The campaign uses GitHub's public commit search API as a covert command and control (C2) channel. The malware performs the following activities: Searching for commits containing the keyword OhNoWhatsGoingOnWithGitHub Decoding matching commit messages as a token dead-drop to recover stolen GitHub tokens Using them to spread Once a usable token is obtained, the payload: Copies itself into execution[.]js Writes setup.mjs Sets "preinstall": "node setup.mjs" in package.json Increments the patch version Repacks the tarball for publishing The malware also pushes the following files directly into victim repositories: .vscode/setup.mjs .claude/execution.js .claude/settings.json The malware pushes the above files using commits authored as claude with the message chore: update dependencies. The three forensic links to @bitwarden/[email protected] are precise enough to indicate shared authorship or a directly reused toolchain. 1. The setup.mjs preinstall bootstrapper. In the Bitwarden campaign, setup.mjs was the self-replication artifact the worm (bw1.js) injected into every npm package the victim could publish. The SAP packages use that same filename as their bootstrapper, and the two share clear common lineage: same Bun version (1.3.13), same Alpine/musl detection logic and the same redirect-following download approach. 2. The decodeScramble / ctf-scramble-v2 obfuscation method. The Bitwarden payload encodes all sensitive strings using a custom seeded ASCII shuffle cipher. This is a Fisher-Yates shuffle over a 128-character ASCII table driven by a linear congruential PRNG seeded with 0x3039 (12345). The SAP execution.js uses a layer explicitly labeled ctf-scramble-v2, which is the same deterministic substitution scheme. This is not a library, it is a bespoke implementation. It is reused across both payloads. 3. The GitHub commit dead-drop pattern. The Bitwarden malware used GitHub's public commit search API as a covert C2 channel. It embedded stolen tokens in commit messages matching LongLiveTheResistanceAgainstMachines: and used them to bootstrap new exfiltration channels without attacker-controlled infrastructure. This wave applies the exact same pattern under a new keyword (OhNoWhatsGoingOnWithGitHub) with matching commit messages decoded as a token dead-drop. The mechanism is identical in implementation: Search the GitHub API for commits containing the keyword Parse the commit message body Decode the embedded token Validate it for repository access Rotating the keyword while keeping the technique intact is a hallmark of the same operator updating a reused codebase. According to Checkmarx's official security update, this npm package is one component of a broader supply-chain campaign that simultaneously compromised multiple Checkmarx distribution channels: Docker Hub: Poisoned checkmarx/kics images (v2.1.20, v2.1.21, latest, alpine, debian) GitHub Actions: Malicious checkmarx/ast-github-action v2.3.35 VS Code extensions: Backdoored checkmarx/ast-results (v2.63, v2.66) and checkmarx/cx-dev-assist (v1.17, v1.19) npm: The @bitwarden/cli package analyzed in this report Per Checkmarx's disclosure, all artifacts share the same C2 infrastructure (audit.checkmarx[.]cx), the same obfuscation techniques and the same credential harvesting and propagation logic. The VS Code extension variant delivered its payload (mcpAddon.js) from a backdated orphan commit in Checkmarx's own GitHub repository, making the download URL appear trustworthy. TeamPCP (@pcpcats) publicly took credit for the compromise. Per Socket's analysis, the group had previously targeted Checkmarx infrastructure in March 2026, along with Trivy and LiteLLM, suggesting an ongoing campaign against security tooling vendors. Table 1 shows the attributes of the attack. Table 1. Attributes of the attack. The Bitwarden security team provided the following information. They identified and contained the malicious package described in Table 1, which was briefly distributed through the npm delivery path for @bitwarden/[email protected] between 5:57 PM and 7:30 PM EST on April 22, 2026, in connection with the broader supply chain incident. Their investigation found no evidence that end user vault data was accessed or at risk, or that production data or production systems were compromised. Once the issue was detected, they: Revoked compromised access Deprecated the malicious npm release Immediately initiated remediation steps The issue affected the npm distribution mechanism for the CLI during that limited window, not the integrity of the legitimate Bitwarden CLI codebase or stored vault data. People who did not download the package from npm during that window were not affected. Bitwarden completed a review of internal environments, release paths and related systems. They found no additional impacted products or environments at this time. A CVE for Bitwarden CLI version 2026.4.0 is being issued in connection with this incident. The package.json provides two execution paths for the malicious script, as shown in Figure 3. The preinstall hook runs automatically during npm install. The bin field registers bw_setup.js as the bw command, symlinking it into the user's PATH. Since the legitimate Bitwarden CLI also uses bw as its binary name, this serves as a secondary trigger. Even if preinstall is blocked (e.g., via --ignore-scripts), the malware executes the next time the user or any script invokes bw. The shebang line #!/usr/bin/env node at the top of bw_setup.js ensures it runs as a Node.js script when called directly. The bootstrap script performs three actions: Platform detection: Identifies the OS and architecture (Linux, macOS, Windows; x64 or arm64), including musl versus glibc detection on Linux. Bun runtime download: Downloads the Bun JavaScript runtime (v1.3.13) from the official github[.]com/oven-sh/bun releases. This is needed because the main payload uses Bun-specific APIs (shell execution, file I/O, gzip) not available in Node.js. Payload execution: Runs bw1.js using the freshly downloaded Bun binary. A custom ZIP extraction implementation is included to avoid any dependencies, making the bootstrap entirely self-contained. The payload is an approximately 10 MB single-line JavaScript file containing approximately 285,000 lines when formatted. It bundles legitimate software developer kits (SDKs) (e.g., AWS SDK, Google Cloud client libraries, Azure Identity, Octokit, jsonwebtoken, tar) alongside the malicious orchestration code. The code employs multiple layers of obfuscation: String table rotation: A function _0x214e resolves hex indices to strings from a large rotated array (_0x1ee1), breaking simple static string analysis. Seeded ASCII shuffle cipher: Sensitive strings (domains, file paths, shell commands) are encoded as arrays of numeric indices into a Fisher-Yates-shuffled ASCII table. The shuffle uses a linear congruential PRNG seeded with 0x3039 (12345), as shown below in Figure 4. The 128-character ASCII set is shuffled deterministically, producing a substitution table where an index with the hex value 0x42 maps to the ASCII character a, 0x6e to u. For example, the C2 domain is stored as [0x42, 0x6e, 0x36, 0x4b, 0x2b, 0x5c, 0xd, 0x57, 0x0, 0xd, 0x7, 0x26, 0x42, 0x3, 0x2a, 0x5c, 0xd, 0x2a], which decodes to an ASCII string for the domain audit.checkmarx[.]cx. Gzip and Base64 embedded payloads: Several blobs are stored as gzip-compressed Base64 strings, including an RSA public key, a GitHub Actions workflow YAML, the worm's setup.mjs loader and a manifesto string Mangled identifiers: All variable and function names are replaced with hex patterns such as _0x3865d8 Credential Harvesting The payload deploys multiple provider classes, each targeting a different credential source. Every provider scans its results with regex patterns to extract npm and GitHub tokens, as shown in Figure 5. Reads sensitive files from the developer's workstation, with per-OS path lists decoded via the scrambler as shown below in Table 2. Table 2. OS path lists from the malware. Files larger than approximately 5 MB are skipped. All others are read in full and included in the exfiltration payload. Runs gh auth token via execSync to capture the GitHub CLI's active token, and then harvests the full process.env environment block. Both are returned for token-regex scanning and exfiltration. Unlike the cloud providers below, the Shell Provider does not use any SDK — it relies solely on the single shell command and environment variables. Detects CI/CD environments via process.env.GITHUB_ACTIONS === "true" and extracts all configured secrets by parsing {"value":"...","isSecret":true} patterns from the Actions runner context. Uses the bundled AWS SDK (SSMClient, DescribeParameters, GetParameter) to enumerate and read parameters from AWS Systems Manager Parameter Store. Uses Azure Identity and Key Vault SDK to call getSecretsFromVault, listing and reading secrets from all accessible vaults. Uses the Google Cloud Secret Manager client to listSecrets and accessSecretVersion, reading all accessible secrets across projects. All harvested credentials are processed through a two-tier exfiltration system. Results are serialized to JSON and compressed with gzip A random 32-byte AES-256-GCM symmetric key is generated The data is encrypted with AES-256-GCM The symmetric key is RSA-encrypted with a hard-coded 4096-bit public key (Fr) The encrypted envelope is sent via POST hxxps[:]//audit.checkmarx[.]cx:443/v1/telemetry. Using stolen GitHub tokens, the malware: Creates a new public repository under the victim's account with the description "Checkmarx Configuration Storage" and a Dune-themed name (e.g., gesserit-melange-813) Commits encrypted result files to a results/ directory Embeds stolen tokens in commit messages using the format LongLiveTheResistanceAgainstMachines: This dual exfiltration ensures data reaches the attacker even if the C2 domain is blocked. When the malware obtains a GitHub token with repo and workflow scopes, class u_ validates the scopes and then: Enumerates repositories the victim can push to, prioritizing recently active ones Checks whether each repo has configured GitHub Actions secrets (skips repos without them) Creates a new branch, commits .github/workflows/format-check.yml — a malicious workflow that dumps all secrets, as shown below in Figure 6. Downloads the resulting artifact containing all serialized secrets Deletes the branch and workflow run to cover its tracks This is the self-replicating component. When the malware finds an npm token (npm_...): Calls the npm tokens API with Authorization: Bearer , verifies the token has publish (write) permission on the package scope and that it bypasses two-factor authentication (2FA). Using the validated token: Fetches the npm username Iterates through the token's allowed resources (orgs, scopes, specific packages) Queries /-/org/ /package for writable packages Falls back to /-/v1/search?text=maintainer: for public packages For each target package: Downloads the latest tarball from the npm registry Extracts it to a temporary directory Copies the Bun binary into the package Rewrites package.json: - Clears all scripts - Sets "preinstall": "node setup.mjs" (decoded from the scrambler) - Bumps the patch version (e.g., 1.2.3 → 1.2.4) Writes setup.mjs with the full worm payload (K$, a gzip+Base64 encoded copy of the malware) Re-packs the tarball Writes a .npmrc with the stolen token's auth line (//registry.npmjs[.]org/:_authToken= ) and runs bun publish . Every newly infected package will trigger the same preinstall chain on any developer who installs it, creating an exponential propagation vector. The malware implements a fall back mechanism for C2 resilience using GitHub's public search API as a covert command channel. If the primary C2 server (audit.checkmarx[.]cx) is unreachable, the malware: Searches GitHub commits for the keyword beautifulcastle via the public Search API Finds a matching commit in helloworm00/hello-world (commit bc544f4, dated April 21 - one day before the main attack) Parses the commit message body with the regex /beautifulcastle ([A-Za-z0-9+/=]{1,30})\.([A-Za-z0-9+/=]{1,700})/ Decodes the first capture group from Base64 to get a domain Verifies the second capture group as a digital signature (512-byte RSA-4096) against the hard-coded public key Er, ensuring only the attacker can direct the malware to a new C2 server At the time of analysis, the dead drop contained a placeholder value (hxxps[:]//example[.]com), indicating it was pre-staged for future use. The attacker can rotate C2 infrastructure at any time by creating a new signed commit on any public GitHub repository. A second fall back (Hr function) searches for commits matching LongLiveTheResistanceAgainstMachines: to recover previously stolen GitHub tokens from exfiltration repos, bootstrapping new GitHub-based exfiltration channels without any attacker-controlled infrastructure. Russian locale killswitch: The mz0 function checks Intl.DateTimeFormat().resolvedOptions().locale, LC_ALL, LC_MESSAGES, LANGUAGE and LANG for Russian (ru). If any match, the malware calls process.exit(0) immediately without executing Daemonization: The Ml0 function re-spawns the process detached with __DAEMONIZED=1 in the environment, allowing the parent to exit cleanly while the payload runs in the background PID-based lock file: $l0 writes process.pid to a temp file and checks whether a previous instance is still alive via process.kill(pid, 0), preventing multiple instances from running simultaneously Signal handlers: Catch SIGINT/SIGTERM with no-op callbacks (() => {}) to prevent interruption Temp directory cleanup: After tarball manipulation removes forensic artifacts All sensitive strings: Encoded via the scrambler or gzip+Base64 Silent error handling: Failures are caught and suppressed Innocuous naming: The C2 path v1/telemetry mimics legitimate analytics endpoints Block the C2 domains and IPs listed above at the network perimeter. Rotate all credentials that may have been exposed: npm tokens, GitHub PATs, AWS/Azure/Google Cloud keys, SSH keys and CI/CD secrets. Audit npm packages you maintain for unauthorized version bumps or new preinstall hooks. Review GitHub for unauthorized repository creation, unexpected workflow files and artifact downloads. Search for the format-results artifact in GitHub Actions logs across your organization. Hunt for unexpected Bun process execution and outbound connections to the IoC infrastructure. Pin dependencies to known-good versions using lockfiles and integrity hashes. Unit 42 Managed Threat Hunting Queries The Unit 42 Managed Threat Hunting team continues to track any attempts to exploit this CVE across our customers, using Cortex XDR and the XQL queries below. Cortex XDR customers can also use these XQL queries to search for signs of exploitation. The following XQL query has been used to successfully identify execution of a JavaScript file through Bun that subsequently calls the GitHub CLI in a likely attempt to collect locally stored authentication tokens. While Bun is legitimate in many developer environments, its use as a runtime for package install malware in this campaign makes this behavior worth investigating when observed with credential access commands such as gh auth token: Unit 42 has witnessed a shift since the September 2025 Shai-Hulud incident, proving that it wasn’t a temporary spike but the new baseline for software supply chain risk. In an ecosystem where code is shared at the speed of thought, a single compromised dependency can trigger a global cascade. Ultimately, npm compromises share commonalities and organizations can navigate this volatility by keeping particular best practices in mind. As we continue to monitor, analyze and update our findings related to npm packages, we encourage you to move beyond static defenses and embrace a culture of continuous verification. The supply chain may be the new primary target, but with collective intelligence and relentless visibility, it doesn’t have to be the primary vulnerability. Palo Alto Networks customers are better protected by our products, as listed below. We will update this threat brief as more relevant information becomes available. Mitigations for Compromised npm Packages Enforce Cooldown Periods Implement a policy (via a private registry or proxy like Artifactory) that blocks any package version published within the last 24 to 72 hours. Most malicious packages are identified and removed from the public registry within this window. Disable Lifecycle Scripts Many compromises rely on preinstall or postinstall hooks to exfiltrate secrets. Use the following in your .npmrc: ignore-scripts=true. Version Pinning and npm ci Use package-lock.json and ensure your CI/CD pipelines use npm ci instead of npm install. This prevents the "hidden" update of dependencies during a build. Private Registry Proxying Never allow developer machines or CI runners to talk directly to registry.npmjs[.]org. Route all traffic through a private registry. Namespace Shadowing (Prevention of Dependency Confusion) Attackers often publish packages with the same name as your internal libraries to the public registry. Always use scoped packages (e.g., @myorg/internal-lib) and configure your private registry to only resolve that scope internally. Provenance Verification Verify the OpenID Connect Attestation. Many major packages provide "provenance," proving the code was built on a specific GitHub/GitLab runner. Use tools like slsa-verifier to check these during the build. Egress Filtering in CI/CD Most npm-based malware attempts to send ~/.npmrc tokens or ~/.ssh keys to a C2 server. Apply strict egress network policies to your CI runners. Only allow connections to your private registry and known deployment targets. Software Bill of Materials (SBOM) Automatically generate an SBOM for every production release. This allows your security team to perform instant impact analysis when a new zero-day is announced. Palo Alto Networks customers can leverage a variety of product protections and updates to identify and defend against this threat. If you think you might have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call: North America: Toll Free: +1 (866) 486-4842 (866.4.UNIT42) UK: +44.20.3743.3660 Europe and Middle East: +31.20.299.3130 Asia: +65.6983.8730 Japan: +81.50.1790.0200 Australia: +61.2.4062.7950 India: 000 800 050 45107 South Korea: +82.080.467.8774 The Advanced WildFire machine-learning models and analysis techniques have been reviewed and updated in light of indicators associated with npm compromises, including the malicious Bitwarden package. Advanced URL Filtering and Advanced DNS Security identify known domains and IP addresses associated with this activity as malicious. Cortex Cloud’s Application Security Module (ASPM) supports the scanning of npm packages installed on cloud resources as well as monitoring audit logs from third party SaaS vendors, including GitHub as discussed within this article. Cortex Cloud prioritizes alerts, issues, policies and assets based on ingested applications as well as their usage. This allows security teams to maintain security awareness across their on-premises and cloud environment by identifying and remediating impacted cloud resources and actively responding to associated runtime operations from the threats discussed within this article through Cortex Cloud’s XDR Agent and serverless operations. For additional details about how to protect against this threat using Cortex Cloud, please see their blog. Koi Agentic Endpoint Security Koi Agentic Endpoint Security allows customers to delay automatic updates for all installed packages by a set time window, allowing newly pushed versions to establish reputation and undergo public scrutiny before being deployed in your environment. Indicators From July 14, 2026 Miasma Activity Indicators From April 29, 2026 Activity Affected Packages @cap-js/[email protected] @cap-js/[email protected] @cap-js/[email protected] [email protected] SHA256 Hashes setup.mjs: 4066781fa830224c8bbcc3aa005a396657f9c8f9016f9a64ad44a9d7f5f45e34 execution.js: 6f933d00b7d05678eb43c90963a80b8947c4ae6830182f89df31da9f568fea95 URLs hxxps[:]//github[.]com/oven-sh/bun/releases/download/bun-v1.3.13/ (Bun download) hxxps[:]//api.github[.]com/search/commits?q=OhNoWhatsGoingOnWithGitHub (dead drop) Indicators From April 22, 2026 Activity Table 3 lists the network indicators from this activity. Table 3. Network indicators. Table 4 lists the GitHub indicators from this activity. Table 4. GitHub indicators. Table 5 lists the file and process indicators from this activity. Table 5. File and process indicators. Table 6 lists the npm indicators from this activity. Table 6. npm indicators. Indicators From June 1, 2026 Activity Repository/GitHub Attacker-created repository description: "Miasma: The Spreading Blight" Network Bun runtime download: github.com/oven-sh/bun/releases/download/bun-v1.3.13/ Checkmarx Security Update: April 22, 2026 – Checkmarx Malicious Checkmarx Artifacts Found in Official KICS Docker Repository and Code Extensions – Socket Weaponizing the Protectors: TeamPCP’s Multi-Stage Supply Chain Attack on Security Infrastructure Threat Brief: Widespread Impact of the Axios Supply Chain Attack – Unit 42, Palo Alto Networks "Shai-Hulud" Worm Compromises npm Ecosystem in Supply Chain Attack (Updated November 26) – Unit 42, Palo Alto Networks Bitwarden CLI Impersonation Attack Steals Cloud Credentials and Spreads Across npm Supply Chains – Cortex Cloud, Palo Alto Networks Updated April 27, 2026 at 2:15 p.m. PT to add information about Bitwarden and link to the Cortex Cloud article in the Additional References section. Updated May 1, 2026 at 4:55 p.m. PT to add information on the Mini Shai-Hulud campaign. Updated May 20, 2026 at 12:30 p.m. PT to update the Executive Summary with information on two new waves and add a new section on Mini-Shai Hulud May 2026 waves. Updated May 21, 2026 at 8:45 a.m. PT to add managed threat hunting queries and additional product protection information. Updated June 2, 2026 at 11:22 a.m. PT to add section on the Red Hat supply chain attack. Added affected packages in the Indicators of Compromise section. Updated July 15, 2026 at 4:10 p.m. PT to add section on July campaign using a Miasma variant. Updated the Indicators of Compromise section.
unit42.paloaltonetworks.comApr 24, 2026extracted
Preparing for agentic AI: A financial services approach
Preparing for agentic AI: A financial services approach Deploying agentic AI in financial services requires additional security controls that address AI-specific risks. This post walks you through comprehensive observability and fine-grained access controls—two critical capabilities for maintaining explainability and accountability in AI systems. You will learn seven design principles and get implementation guidance for meeting regulatory requirements while deploying secure AI solutions. Financial institutions navigating this landscape face a dual challenge. They must comply with an evolving regulatory environment—including frameworks such as SR 11-7 in the US, SS1/23 in the UK, and ECB guidelines in the EU—while simultaneously addressing the unique security considerations that agentic AI introduces. Unlike traditional software systems, agentic AI can make autonomous decisions and take actions that potentially impact customers, operations, and institutional reputation. This autonomy demands a security approach that goes beyond conventional controls. Organizations aligning to established frameworks such as ISO 27001 and the NIST Cybersecurity Framework have a strong foundation, but you need to add AI-specific security measures to address the unique risks of agentic AI. Using agentic AI requires augmentation of these traditional controls with AI-specific security measures. The non-deterministic nature of AI systems, their ability to act with significant autonomy, and the complexity of multi-agent interactions introduce new dimensions of risk that must be carefully managed. In financial services, you need clear explainability and accountability for effective model risk management. When AI agents make decisions or take actions on behalf of your organization, you need clear visibility into what was done, why it was done, and who—or what—was responsible. This transparency helps you maintain trust, meet regulatory obligations, and deploy AI responsibly according to AWS Responsible AI practices. This post outlines security principles for implementing agentic AI in financial services. We focus on two critical enablers: comprehensive observability of agentic workflows and fine-grained control over agent tool access permissions. Together, these capabilities provide the foundation for explainability and create a control environment that facilitates accountability. Our approach is structured in two parts. First, we explore seven core design principles that serve a dual purpose: guiding solution architects during the design of AI systems and providing risk management teams with a framework for identifying critical risk factors. Second, we provide detailed implementation guidance with practical insights to help you deploy secure and compliant AI solutions in the financial sector. Consult with your compliance and legal teams to determine specific requirements for your situation. Regulatory requirements establish minimum baselines, but organizational risk considerations—including reputational risk and potential customer harm—often require additional controls. Core design principles for agentic AI observability and tool access control In this section, we outline key design principles for agentic AI systems in financial services. These principles help address fundamental observability and access control requirements for secure AI implementations. While specific compliance requirements vary by jurisdiction and use case, these design principles provide a foundation for secure and governable AI systems. Human-AI security homology Understand the human controls and determine applicability to agents and agentic workflows and document the agent personas. Implement agent identities in addition to traditional role and attribute-based permissions, logging and behavioral monitoring. Ensure critical actions have supervision (agentic or human), define agent scope in workflows and consider change and incident management, segregation of agent duties for actions and tool usage, maker-checker verification, logging, and behavior monitoring. Modular agent workflow architecture Use specialized sub-agents in workflows. Attach fine-grained permissions to narrow the focus of sub-agents to reduce the scope of permissions each agent requires, increase modularity and reusability, simplify maintenance, and enhance observability and therefore explainability. Workflow and agent logging and tracing Implement comprehensive logging and tracing to track decisions, actions, activity workflows, specific caller context, and reasoning steps to enable better explainability. AI systems explainability—and monitoring of inter-agent interactions, context sharing, and emergent behaviors—enables a holistic observability of the multi-agent workflow operations and dynamics. Segregated AI least-privilege Enforce least privilege and segregation of duties in automated workflows through clearly defined operational boundaries for AI agents. The boundaries should be supported by authorization controls, bound to the caller permissions with support for contextual verification, and supported by circuit breakers such as human oversight. This approach helps ensure that agents operate within appropriate limits and boundaries while enabling necessary human intervention, balancing automation with security and control. Governance integration Integrate agent observability into existing governance frameworks through alignment with established risk management and compliance processes, while implementing standardized evaluation frameworks and test harnesses that measure agent performance, compliance, and business value alignment. Agentic operational controls Provide business-friendly guardrails for defining and managing agent behavior policies while maintaining comprehensive cost controls through monitoring and optimization of resource utilization at both individual and workflow levels. Through this approach, business users can manage agent constraints. Risk management and compliance Integrate comprehensive activity tracing (see Workflow and agent logging and tracing) with existing governance frameworks to support audit requirements, regulatory compliance, and align with established risk management processes. This approach helps ensure thorough oversight and compliance across all agent activities within the organization’s existing governance structure. Implementation guidance In the following sections, we provide specific implementation guidance and recommendations that directly help you apply the seven core design principles discussed above. With these guidelines in place, your solution architects can design and deploy secure AI agent systems on AWS. Our aim is not to provide exhaustive technical solutions, but rather to guide implementations. We recommend that enterprise customers work closely with their assigned AWS Solutions Architect to discuss and develop detailed implementation practices tailored to their specific architecture. Monitor and understand AI agent behavior The following guidance aligns with the Human-AI security homology and Workflow and agent logging and tracing principles in the previous section. Implement end-to-end agent workflow visibility Document agent workflows and personas, implement comprehensive tracing of agent workflows that captures inputs, reasoning steps, outputs, and tool usage. Amazon Bedrock AgentCore Observability provides purpose-built solutions for tracing, debugging, identifying, and monitoring agent performance in production environments. This visibility provides data for understanding how agents interact with critical financial systems and data. Implement a tagging strategy for agents to enhance visibility, use descriptive role names for intended use cases for agents, and anticipate use of these tags to be used in downstream agentic permissions and upstream workflows. Establish agent activity dashboards Deploy operational dashboards, with Amazon CloudWatch for example, that provide a view into agents’ operational health. These dashboards should track key metrics including successful completions, failures, latency, and resource utilization. Integrate agent telemetry with existing monitoring systems Use common standards such as OpenTelemetry (OTel) using AWS Distro for OpenTelemetry to integrate agent telemetry with your existing monitoring infrastructure. Common standards mean that financial institutions can maintain their investments in monitoring tools while extending visibility to agent activities. Establish agent server-side (tool-side) checks Implement a standardized access control for agents using tools to apply consistent controls following AWS security best practices for tool integration. Ensure validation and sanitization checks are in place on inputs at the point of tool usage to detect unintended behavior for both user-to-agent and agent-to-agent interactions. Manage change in agentic AI environments The following guidance aligns with the Governance integration and Agentic operational controls principles. Adapt change management for AI agent workflows Modify your current change management processes to accommodate the dynamic nature of AI agents while maintaining appropriate controls. Review approval processes integrated into agent deployment pipelines, adapt those approval processes to an iterative nature of small increment improvements, implement human in the loop breaks into agentic operations. Monitor for agent behavior drift Implement continuous monitoring of agent traces into a test harness with tools such as Amazon CloudWatch and AWS X-Ray to watch for changes in agent behavior patterns that might indicate execution drift or unexpected learning. This is particularly important for maintaining compliance with regulatory requirements that might dictate specific operational boundaries. Implement the principle of least privilege for AI agents The following guidance aligns with the Human-AI security homology and Segregated AI least-privilege principles. Define granular permission boundaries for agent actions Implement fine-grained permission controls to limit permissions to those necessary for agent functions using, for example, Amazon Bedrock AgentCore Policy. Consider separation of duties for agents by segregating functions and tool access permissions to reduce the scope of impact of unintended actions. Ensure user prompt-driven agent actions and agent-to-agent actions are separately identifiable. Implement authorization monitoring Deploy observability tools, such as Amazon Bedrock AgentCore Observability, that continuously monitor agent authorization patterns and flag anomalous access attempts. Establish agent action audit trails Maintain comprehensive audit trails of agent actions that affect production systems and sensitive data. These audit trails should be immutable and integrated with existing compliance reporting systems. This is to help ensure that agent-to-agent actions and the original source and lineage of the request be preserved. Implement guardrails and behavioral controls for AI agents The following guidance aligns with the Agentic operational controls and Risk management and compliance principles. It’s recommended to take a crawl, walk, run approach to control implementation, start with the minimum required set of controls and evolve over time using feedback from monitoring. Deploy policy configuration Implement user-friendly interfaces for defining agent behavioral policies and compliance rules. Through such interfaces, risk and compliance teams can directly manage agent guardrails without technical intervention. Enable real-time guardrail enforcement Deploy automated systems that validate agent actions against defined policies in real time. For example, use Amazon Bedrock Guardrails to implement content filtering, personally identifiable information (PII) detection, and response validation before agent outputs reach production systems. Establish human oversight workflows Implement review workflows as control points for critical agent actions with clear escalation paths. Define triggers for human review and maintain feedback collection mechanisms for continuous improvement. Detecting and recovering from AI agent failures The following guidance aligns with the Workflow and agent logging and tracing and Risk management and compliance. Implement agent health and performance monitoring Deploy comprehensive health and performance monitoring for AI agents that can detect both hard failures and degraded performance. Amazon Bedrock AgentCore Observability provides specialized capabilities for understanding agent health beyond traditional application metrics. Establish agent failure recovery procedures As in traditional deployment pipelines, develop and test automated recovery and manual behavioral modification procedures for different types of agent failures. These procedures should include appropriate circuit breakers and human escalation paths for scenarios requiring judgment. Monitor for reasoning quality degradation Implement monitoring for subtle degradation in agent reasoning quality that might not trigger traditional failure alerts. Ensuring consistent agent performance across environments The following guidance aligns with the Workflow and agent logging and tracing, Agentic operational controls, and Risk management and compliance principles. Implement modular agents with performance baselines Implement modular, reusable agents with established performance baselines for agent operations across development, testing, and production environments. Monitor for deviations that might indicate environment-specific issues. Deploy canary testing for agent behavior monitoring and resilience testing Implement small-scale release testing practices for agent changes, with comprehensive observability to detect unexpected behavior changes before full deployment and at runtime. Use positive testing where valid inputs produce expected outputs and negative testing where improper inputs still achieve the desired output. Test agent resilience under various failure conditions. Use change control for canary tests to ensure tracking over time as testing expands and matures. Managing multi-agent workflow interactions The following guidance aligns with Modular agent workflow architecture and Workflow and agent logging and tracing principles. Monitor agent collaboration patterns Implement monitoring for agent-to-agent communications within predefined workflows and dynamic workflows, context sharing, and collective behaviors. Track interaction patterns to identify potential risks or inefficiencies. Detect emergent behaviors Deploy behavioral trace tests to identify unexpected patterns or outcomes arising from multi-agent interactions. Establish baselines for normal collaborative behavior and alert on deviations, include extreme scenarios in testing. Maintain interaction audit trails Record non-repudiable and comprehensive logs of agent interactions, including context exchanges, handoffs, and outputs. Ensuring agent-to-agent actions, the original source and lineage of the request must be preserved. Optimize AI agent resource utilization & costs The following guidance aligns with the Agentic operational controls principle. Implement agent resource and cost consumption monitoring Deploy monitoring for agent resource consumption patterns, including compute, memory, API usage, and costs using (for example) Amazon CloudWatch. This enables optimization of resource allocation and cost management. Establish agent performance metrics Define and monitor key performance indicators specific to agent operations, such as reasoning step performance efficiency, tool usage patterns, and completion times. Implement agent performance anomaly detection Deploy anomaly detection for agent performance metrics to identify potential issues before they impact business operations. Conclusion The adoption of agentic AI in financial services requires carefully balancing innovation with control. The seven core design principles outlined here provide a framework for implementing explainable, governable, and in general responsible AI systems that align with existing security and compliance requirements. By treating AI agents with the same security rigor applied to human employees—through robust access control, role and attribute-based permissions, and comprehensive monitoring—organizations can safely deploy these systems while maintaining compliance with key regulatory frameworks. The purpose-built solutions available from AWS provide the technical foundation, but success also requires clear policies and human oversight mechanisms. Financial institutions that establish these fundamental security and governance capabilities will be well-positioned to leverage agentic AI while contributing to the security, explainability and compliance with industry standards of their systems. If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS Security, Identity, & Compliance re:Post or contact AWS Support.
aws.amazon.comMar 26, 2026extracted
New infosec products of the month: February 2026
New infosec products of the month: February 2026 Here’s a look at the most interesting products from the past month, featuring releases from Aikido Security, Avast, Armis, Black Duck, Compliance Scorecard, Fingerprint, Gremlin, Impart Security, Portnox, Redpanda, Socure, SpecterOps, Veza, and Virtana. Gremlin launches Disaster Recovery Testing for zone, region, and datacenter failovers Gremlin, the proactive reliability platform, launched Disaster Recovery Testing: a new product built to safely and efficiently test zone, region, and datacenter evacuations and failovers. These large-scale tests ensure businesses maintain digital resilience and business continuity when faced with cloud migrations, compliance concerns, and catastrophic events. Fingerprint enables enterprises to tell trusted AI agents apart from bots and scrapers Fingerprint has released Authorized AI Agent Detection, its new ecosystem of AI agents, including OpenAI, AWS AgentCore, Browserbase, Manus and Anchor Browser. The ecosystem enables enterprises to detect authorized agentic AI traffic with 100% certainty, allowing organizations to distinguish trusted, permissioned automation from malicious bots and scrapers. Socure unifies identity, fraud, and program integrity for government at scale Socure has released Socure for Government (SocureGov) RiskOS to help public sector organizations deliver simpler, faster, and more transparent digital identity verification and fraud prevention at scale. Avast brings deepfake scam detection to Windows PCs and mobile devices Avast announced the full international availability of Avast Scam Guardian and Scam Guardian Pro on mobile devices, alongside the launch of Avast Deepfake Guard on Windows PCs, a new AI-powered feature designed to proactively analyze and detect malicious audio in video content. Together, these launches expand the Avast scam protection ecosystem, extending coverage across mobile and PC to help protect people from scams across text messages, calls, and video platforms. Armis Centrix brings unified, AI-driven application security to the SDLC Armis has announced Armis Centrix for Application Security, which unifies application security across an organization’s software development lifecycle. The technology helps security teams secure code as a next-generation attack vector to fortify organizations’ defenses and protect against cyber threats. BloodHound Scentry helps organizations reduce identity risk and close attack paths SpecterOps has announced BloodHound Scentry, a new service designed to help customers accelerate their APM practice and reduce identity risk. BloodHound Scentry combines the power of BloodHound Enterprise with SpecterOps tradecraft experts and practitioners to provide customers with tailored guidance for attack path remediation, advanced analysis for emerging threats, and privilege zone design to protect critical assets. Portnox expands ZTNA with passwordless access for RDP, SSH, and enterprise consoles Portnox has unveiled a major expansion of its zero trust network access (ZTNA) solution, extending credential-free access beyond web and on-premises applications to include enterprise console-based applications. Black Duck expands Polaris platform with unified, automated security across all major SCMs Black Duck has announced the availability of a set of enhanced Black Duck Polaris Platform integrations across all major source code management (SCM) platforms, including GitHub, GitLab, Azure DevOps, and Bitbucket. The Polaris Platform is an integrated, software-as-a-service application security platform powered by the static application security testing, software composition analysis, and dynamic application security testing engines. Impart enables safe, in-app enforcement against AI-powered bots Impart Security has launched Programmable Bot Protection, a runtime approach to bot defense that brings detection and enforcement together within the application. Impart makes enforcement operational by enabling teams to see what would be blocked before turning it on. Compliance Scorecard v10 delivers context-driven AI for explainable compliance decisions Compliance Scorecard announced the release of v10, introducing governed, audit-ready AI designed to support defensible compliance decision-making for managed service providers (MSPs). Compliance Scorecard v10 applies AI only within a structured system of validated context and controls. The platform is built on a simple premise: AI can only be trusted in compliance if the required context already exists. As a result, v10 treats AI as a governed system of decision support, not a conversational interface. Virtana expands MCP Server to bring full-stack enterprise context to AI agents Virtana announced the latest version of its Model Context Protocol (MCP) Server, bringing full-stack enterprise visibility directly to AI agents and LLMs so machines can understand enterprise operations as complete systems rather than isolated signals. Redpanda brings identity, policy control, and data governance to AI agents Redpanda announced the availability of new core capabilities in the Redpanda Agentic Data Plane (ADP), including a centralized AI gateway, AI observability and evaluation via OpenTelemetry, AI agents, and unified authentication and authorization. Together, these features form a unified governance layer that allows enterprises to securely connect AI agents and Model Context Protocol (MCP) servers to live enterprise data with visibility and control. Aikido Infinite introduces continuous, self-remediating AI penetration testing Aikido Security has unveiled Aikido Infinite, a continuous AI penetration testing solution that autonomously validates and remediates vulnerabilities. Infinite reduces risk with every release by testing software changes as they move through deployment, confirming exploitability, and fixing vulnerabilities within the same workflow. Veza expands platform with AI Access Agents for enterprise identity governance Veza expanded its platform with Veza Access Agents, a set of purpose-built AI agents designed to automate complex identity and access governance tasks for enterprises. Veza also announced advancements to its AI Agent Security product, providing organizations with deeper visibility into agent risks and greater control over third-party AI agents, large language models (LLMs), AI apps, and AI infrastructure (MCP).
helpnetsecurity.comFeb 27, 2026extracted
New infosec products of the week: February 20, 2026
New infosec products of the week: February 20, 2026 Here’s a look at the most interesting products from the past week, featuring releases from Compliance Scorecard, Impart Security, Redpanda, and Virtana. Impart enables safe, in-app enforcement against AI-powered bots Impart Security has launched Programmable Bot Protection, a runtime approach to bot defense that brings detection and enforcement together within the application. Impart makes enforcement operational by enabling teams to see what would be blocked before turning it on. Compliance Scorecard v10 delivers context-driven AI for explainable compliance decisions Compliance Scorecard announced the release of v10, introducing governed, audit-ready AI designed to support defensible compliance decision-making for managed service providers (MSPs). Compliance Scorecard v10 applies AI only within a structured system of validated context and controls. The platform is built on a simple premise: AI can only be trusted in compliance if the required context already exists. As a result, v10 treats AI as a governed system of decision support, not a conversational interface. Virtana expands MCP Server to bring full-stack enterprise context to AI agents Virtana announced the latest version of its Model Context Protocol (MCP) Server, bringing full-stack enterprise visibility directly to AI agents and LLMs so machines can understand enterprise operations as complete systems rather than isolated signals. Redpanda brings identity, policy control, and data governance to AI agents Redpanda announced the availability of new core capabilities in the Redpanda Agentic Data Plane (ADP), including a centralized AI gateway, AI observability and evaluation via OpenTelemetry, AI agents, and unified authentication and authorization. Together, these features form a unified governance layer that allows enterprises to securely connect AI agents and Model Context Protocol (MCP) servers to live enterprise data with visibility and control.
helpnetsecurity.comFeb 20, 2026extracted
Redpanda brings identity, policy control, and data governance to AI agents
Redpanda brings identity, policy control, and data governance to AI agents Redpanda announced the availability of new core capabilities in the Redpanda Agentic Data Plane (ADP), including a centralized AI gateway, AI observability and evaluation via OpenTelemetry, AI agents, and unified authentication and authorization. Together, these features form a unified governance layer that allows enterprises to securely connect AI agents and Model Context Protocol (MCP) servers to live enterprise data with visibility and control. As organizations move from AI experimentation to production, the challenge has shifted from building agents to governing them. “This is why the enterprise agentic AI market is struggling while consumer AI flourishes,” said Tyler Akidau, CTO of Redpanda. “Building an agent is remarkably easy. Running one safely in a company, with access to sensitive systems and data, remains genuinely hard. Redpanda is focused on bringing control to the connectivity layer – rather than configuring access policies individually at each data source, enterprises need a central point through which all agent interactions flow: an agentic data plane.” “AI agents don’t fail because models are bad; they fail because systems lack control,” said Alex Gallego, founder and CEO of Redpanda. “With Redpanda Agentic Data Plane, we’re giving enterprises a practical way to operate agentic systems safely, with identity, policy enforcement, and observability built in from day one.” Available Redpanda ADP capabilities First introduced in October, Redpanda’s ADP exists to make AI agents trustworthy at scale. Acting as both a governance layer and a control plane, it governs how agents authenticate, access data, and take action while recording intent, inputs, and outputs for complete auditability. With AI Gateway, open agent interoperability, unified identity, and full observability, enterprises gain the controls needed to safely run agents on live data without sacrificing speed or visibility. “Redpanda addresses the right requirements with this release, as our research shows a strong need for both strong AI governance and real-time data inputs,” says Kevin Petrie, VP Research and Head of Data Management Practice of BARC. “AI adopters feel overwhelmed with the complex task of meeting these requirements while safely integrating powerful GenAI models into their business processes. Redpanda’s approach will help make data and AI leaders’ lives easier.” AI Gateway Redpanda AI Gateway provides a unified access layer between applications, AI models, and MCP services. It centralizes routing, policy enforcement, cost controls, and observability across all AI traffic. Enterprises can define token budgets, set spending limits, and optimize usage through deferred tool loading, bringing operational discipline to complex agent workflows. The AI Gateway also serves as a central point to aggregate and govern MCP servers, with an admin-controlled registry of approved servers and YAML-based configuration for rapid deployment. AI agents Redpanda ADP is designed to work with any AI agent framework. Enterprises can run and govern agents built on their existing frameworks, as well as use Redpanda’s built-in AI agents when they want a fully-managed option. All agents, whether hosted by Redpanda or external, interact with data and tools through open standards like the A2A protocol, and integrate with ADP’s unified authentication, authorization, and observability services. Agents can also be triggered through Redpanda Connect pipelines, enabling real-time, event-driven and human-in-the-loop workflows. Unified authentication and authorization All components of the Redpanda Agentic Data Plane are secured through OIDC-based identity and fine-grained authorization policies. Every request, whether from a user, service account, or agent, is authenticated and governed, eliminating long-lived credentials and reducing the risk of uncontrolled agent access. End-to-end observability and evaluation Redpanda ADP emits full-fidelity metrics, traces, logs, and transcripts using the OpenTelemetry Protocol (OTLP). Enterprises can inspect agent behavior directly in the Redpanda console or export traces to external observability platforms, enabling debugging, compliance, and post-incident analysis. The Redpanda Agentic Data Plane is built on a low-latency streaming foundation, enabling real-time data access, continuous context updates, and event-driven agent execution. With more than 300 connectors available through Redpanda Connect, enterprises can expose data from databases, SaaS platforms, data streams, and data lakes without moving data or breaking existing architectures.
helpnetsecurity.comFeb 19, 2026extracted
Critical Node.js Vulnerability Can Cause Server Crashes via async_hooks Stack Overflow
Node.js has released updates to fix what it described as a critical security issue impacting "virtually every production Node.js app" that, if successfully exploited, could trigger a denial-of-service (DoS) condition. "Node.js/V8 makes a best-effort attempt to recover from stack space exhaustion with a catchable error, which frameworks have come to rely on for service availability," Node.js's Matteo Collina and Joyee Cheung said in a Tuesday bulletin. "A bug that only reproduces when async_hooks are used would break this attempt, causing Node.js to exit with 7 directly without throwing a catchable error when recursions in user code exhaust the stack space. This makes applications whose recursion depth is controlled by unsanitized input vulnerable to denial-of-service attacks." At its core, the shortcoming stems from the fact that Node.js exits with code 7 (denoting an Internal Exception Handler Run-Time Failure) instead of gracefully handling the exception when a stack overflow occurs in user code while async_hooks is enabled. Async_hooks is a low-level Node.js API that allows developers to track the lifecycle of asynchronous resources, such as database queries, timers, or HTTP requests. The issue, Node.js said, impacts several frameworks and Application Performance Monitoring (APM) tools, including React Server Components, Next.js, Datadog, New Relic, Dynatrace, Elastic APM, and OpenTelemetry, owing to the use of AsyncLocalStorage, a component built atop the async_hooks module that makes it possible to store data throughout the lifetime of an asynchronous operation. It has been addressed in the following versions - Node.js 20.20.0 (LTS) Node.js 22.22.0 (LTS) Node.js 24.13.0 (LTS) Node.js 25.3.0 (Current) The problem also impacts all Node.js versions from 8.x, which was the first version with async_hooks, to 18.x. It's worth noting that Node.js version 8.0.0, codenamed Carbon, was released on May 30, 2017. However, these versions remain unpatched as they have reached end-of-life (EoL) status. The fix put in place detects stack overflow errors and re-throws them to user code instead of treating them as fatal. This is being tracked under the CVE identifier CVE-2025-59466 (CVSS score: 7.5). Despite the significant practical impact, Node.js said it's treating the fix as only a mitigation owing to a couple of reasons - Stack space exhaustion is not part of the ECMAScript specification The V8 JavaScript engine does not treat it as a security issue Limitations with the "uncaughtException" handler, which is designed to be used as a last resort mechanism for exception handling "Although it is a bug fix for an unspecified behavior, we chose to include it in the security release because of its widespread impact on the ecosystem," Node.js said. "React Server Components, Next.js, and virtually every APM tool are affected. The fix improves developer experience and makes error handling more predictable." In light of the severity of the vulnerability, users of the frameworks/tools and server hosting providers are recommended to update as soon as possible. Maintainers of libraries and frameworks are being recommended to apply more robust defenses to counter stack space exhaustion and ensure service availability. The disclosure comes as Node.js also released fixes for three other high-severity flaws (CVE-2025-55131, CVE-2025-55130, and CVE-2025-59465) that could be exploited to achieve data leakage or corruption, read sensitive files using crafted relative symbolic link (symlink) paths, and trigger a remote denial-of-service, respectively.
thehackernews.comJan 14, 2026extracted
AI hype hits a wall when the data doesn’t deliver
AI hype hits a wall when the data doesn’t deliver Companies are pouring money into AI for IT operations, but most projects are still far from maturity. A global survey of 1,200 business leaders, IT leaders, and technical specialists found that while spending and confidence are rising, only 12% of AI initiatives have been fully deployed. The report, authored by Riverbed, suggests that optimism at the executive level is colliding with challenges in data quality, tool complexity, and everyday IT performance. Most AI projects remain in early stages, with only 12% fully deployed across the organization. Big budgets, slow rollouts Organizations nearly doubled their AI investments over the past year. Many leaders and technical specialists agree the spending is delivering returns, with most saying outcomes have met or exceeded expectations. Even so, most projects remain stuck in pilot or development phases. Executives often express confidence in their strategies, while technical teams highlight ongoing issues with data, integration, and scaling. This split helps explain why progress feels uneven. Budgets are growing, but systems are not yet ready to run AI at scale. Tool consolidation accelerates The survey found that organizations use an average of 13 observability tools from nine vendors, creating fragmented environments. Leaders acknowledge this patchwork adds overlap and slows teams when problems arise. Almost all respondents are consolidating, and 93% say they are willing to switch vendors to make it happen. The main drivers are integration, productivity, and alignment with business strategy. That level of openness suggests the vendor landscape could look very different in the next few years, as companies move toward platforms that cover more ground with less complexity. Unified communications under pressure Hybrid work has made unified communications tools central to daily operations. Employees spend a large share of their week on video calls, chat, and collaboration platforms. Despite that reliance, performance remains a frequent complaint. Users continue to report dropped calls, connectivity problems, and inconsistent quality. These issues show up in help desk queues, where UC tickets take longer than average to resolve. Responsibility for UC systems is often divided across multiple teams, which makes it harder to identify problems or apply consistent fixes. OpenTelemetry gains traction OpenTelemetry, the open-source observability framework, is moving toward becoming a standard. A large majority of companies report at least partial adoption, and most expect vendor support to be mandatory within two years. Business leaders are more likely to call it a strategic priority, pointing to its value in improving visibility and preparing for automation. Networks carry the weight The ability to move and share AI data is another pressure point. Respondents named cost, security, and performance as their top concerns when shifting workloads across cloud, edge, and on-premises environments. While public cloud and edge storage are expected to grow modestly by 2028, use of on-premises data centers is likely to decline.
helpnetsecurity.comOct 3, 2025extracted