Search/178[.]16[.]54[.]109
● IOC

178[.]16[.]54[.]109

Type
IP
Source
News extraction
First seen
Apr 22, 2026
Last seen
Aug 4, 2026
Look up on VirusTotal
Connections
2 relationships
Almost Half of Malware Samples Communicate Direct to IP
Malware samples often bypass DNS entirely, communicating directly to IP addresses instead. Our analysis of 4 million dynamic analysis reports indicates that almost half (45.32%) of malware samples with any command-and-control (C2) activity made at least one direct-to-IP (D2IP) address connection. Measured as a fraction of all C2 connection attempts, D2IP traffic accounts for 23.17% of the total. A wide variety of threats — including ransomware droppers, peer-to-peer (P2P) botnets and supply chain risks — communicate directly with hard-coded IP addresses, bypassing DNS entirely and evading DNS-based defenses altogether. This article introduces zero trust IP (ZT-IP), which is a network-level enforcement approach that applies zero trust principles to IP-based traffic. The enforcement approach verifies whether outbound connection destinations were ever sanctioned by a DNS response. We validate this approach against real-world network traffic and samples, demonstrating how ZT-IP successfully surfaces threats including: Phorpiex ransomware droppers connecting directly to C2 IP addresses A persistent data exfiltration campaign using a custom obfuscated HTTP GET request Mozi P2P botnet payloads delivered to internet-of-things (IoT) devices without DNS Palo Alto Networks customers are better protected from the threats discussed here through the following products and services: If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team. DNS security has become a cornerstone of enterprise threat defense. By monitoring and filtering DNS queries, security teams can block malware from reaching known-bad domains and use sinkholing to disrupt C2 communications before they establish a foothold. This approach is effective when malware plays by the rules of relying on DNS for domain resolution. However, many types of malware do not. For example, a backdoor malware sample made no DNS query at all before initiating a WebSocket connection directly to an IP address. Disassembly via Ghidra revealed why. The destination address wss://154.92.19[.]71:39989 was hard coded into the binary as a Unicode string. This is not an isolated edge case. Without the DNS resolution step in network communications, malware is invisible to DNS-based security controls, protective DNS sinkholing and DNS anomaly detection systems. The connection simply appears as raw IP traffic with no prior context. We analyzed over 4 million Advanced WildFire dynamic analysis reports for a 30-day period to quantify how prevalent this behavior truly is. We filtered out connections to common legitimate services, internal addresses and DNS resolvers. After doing that, we found that 20.11% of malware samples exhibited C2 activity. The contrast with benign samples is striking. Only 1% of benign samples establish connections to untrusted IP addresses after applying the same filtering criteria. Those that do average just 1.6 such connections per sample. This is a small fraction of the activity observed in malware. Among malware with C2 connections, TCP dominates (94.43% prevalence, averaging 4.17 unique C2 IP addresses per sample). UDP is present in 17.50% of cases but contacts far more IP addresses per sample (average 13.79%), consistent with scanning and P2P mesh behavior. Most critically for DNS-based defenses, 45.32% of malware samples with any C2 activity made at least one direct-to-IP (D2IP) address connection — a raw IP contact with no preceding DNS query. Even after excluding bulk port-scanning behavior, the figure remains 41.97%. Measured as a fraction of all C2 connection attempts, D2IP traffic accounts for 23.17% of the total. By searching for D2IP connections (ZT-IP analysis), we were able to uncover multiple threats. Among traffic flagged by ZT-IP, we observed the following suspicious HTTP GET request toward the destination IP address at 178.16.54[.]109 as shown below in Figure 1. The observed request exhibits multiple variations, including sequential numeric GET paths (e.g., /1 through /6) and specific file downloads (e.g., /sex/k/n.txt, /new.php). We found that these requests correlate with several malware samples (e.g., the binary retrieved from hxxp[:]//178.16.54[.]109/st.exe) associated with Phorpiex (aka Trik), a long-running malware family used primarily as a dropper and spam botnet. Figure 2 illustrates the many malicious samples hosted on 178.16.54[.]109 in a flow chart of this activity. This traffic aligns with Phorpiex's staged payload delivery mechanism. This mechanism is characterized by initially fetching configuration, and then retrieving additional malicious components such as a ransomware payload. A key indicator of malicious D2IP activity was the absence of DNS queries preceding the connections to 178.16.54[.]109. Since the request bodies appear benign, they often bypass standard heuristic detectors. One of the most distinctive findings was a persistent campaign using a non-standard HTTP request we call the \GET protocol. This is an obfuscated request format designed to evade security detection while exfiltrating data. Unlike standard HTTP requests, these requests begin with \GET (backslash-GET) rather than standard GET followed by a backslash. The \GET is followed by a long encoded string ranging from 250–666 characters, as shown in Figure 3. Statistical analysis of the encoded payloads reveals a structured encoding scheme: The first and last four characters: alphanumeric (0-9 and A-Z), evenly distributed Middle characters: hex-like (0-9 and A-F), evenly distributed The payload length ranges from 250–666 characters Disassembly of associated malware samples (e.g., cc43cdbe8eb9874f55fffbe23b560b673eb9f31fb9a953926bba29464fd2dd07) confirms there are no hex-format IP addresses hard coded in the binaries. Instead, multiple hard-coded strings follow the same encoding rule, consistent with the payload lengths observed. The C2 infrastructure is hosted on public cloud infrastructure in Brazil. Samples that point to it were found impacting high-value sectors including government, airlines and universities. Because the threat leverages shared cloud resources, simple IP-based blocking is ineffective. Furthermore, the malware employs a notable operational security measure. It rotates both its destination port and IP address on a regular schedule, which evades static IP-blocking defenses. We observed two separate attackers operating infrastructure at 87.120.107[.]33 and 194.76.227[.]94, respectively, deploying the same malware family of SectopRAT against educational institutions. Both actors leverage SectopRAT's in-browser proxy capability to silently mirror all victim browser traffic to attacker-controlled servers in real time. The proxy operates via two endpoints. The first, /churl (shown in Figure 4), relays every URL the victim visits, including: Authenticated session pages Single sign-on (SSO) redirects Learning management system content The second, /fsave (shown in Figure 5), exfiltrates form fields including usernames and plaintext passwords at the moment of submission. An identifier (pcid/clid) in every request allows each actor to track individual compromised devices across sessions. Both attackers’ campaigns communicate entirely over D2IP connections, bypassing DNS-based visibility layers entirely. IoT malware families often exploit network vulnerabilities to self-propagate, relying on D2IP communication that makes them invisible to DNS-based security layers. Mozi is a Mirai-derived P2P botnet known for targeting IoT devices with MIPS, ARM and x86 architectures. We observed its characteristic exploitation payload embedded directly in HTTP requests. Figure 6 shows an example of the exploitation payload for Mozi. In ASCII text, this translates to the three commands shown in Figure 7. The Mozi campaign showed highly distributed patterns with thousands of source and destination IP addresses, consistent with the Mozi botnet's P2P mesh structure. However, because this IoT malware propagates by making D2IP connections without DNS resolution, blocking this type of connection can effectively prevent it from spreading within sensitive environments. Similarly, we identified a Mirai variant that we named Boatnet, which was distributed from the IP address 2.26.98[.]67. Boatnet demonstrates thorough platform targeting, covering at least 14 different architectures. Beyond standard IoT architectures, Boatnet includes binaries for m68k (Motorola 68000). This processor family is common in legacy industrial control systems (ICS) and aging embedded hardware, which current malware typically overlooks. Unlike generic variants, Boatnet fragments its ARM coverage into three discrete sub-variants (ARM5, ARM6 and ARM7) to ensure optimal performance across different hardware generations. Binaries are hosted under a /hiddenbin/ directory, as noted in Figure 8, showing a deliberate attempt to mimic legitimate system paths and evade basic file-path detection. We observed infected devices fetching payloads with a User-Agent string of Wget/1.13.4 (released in 2012). This serves as a significant fingerprint, suggesting the botnet is specifically harvesting older, unpatched hardware. Boatnet also demonstrates more operational maturity than “vanilla” Mirai through its use of HTTP Range headers. D2IP communication from malware won’t be detected by approaches that focus specifically on securing requests at the time a hostname is resolved. This is illustrated by threats like the Phorpiex ransomware dropper, the obfuscated \GET exfiltration campaign and the P2P Mozi botnet. ZT-IP closes this critical gap by applying strict zero trust principles to outbound D2IP connections, ensuring only DNS-sanctioned traffic is permitted. It is a necessary network-level enforcement mechanism to block what DNS-based detection cannot see. Palo Alto Networks customers are better protected from the threats discussed here through the following products and services: Advanced WildFire machine-learning models and analysis techniques have been reviewed and updated in light of the IoCs shared in this research. Advanced URL Filtering and Advanced DNS Security identify known domains and URLs associated with this activity as malicious. Cortex XDR and XSIAM help to prevent the threats described in this article, by employing the Malware Prevention Engine. This approach combines several layers of protection, including Advanced WildFire, Behavioral Threat Protection and the Local Analysis module, to prevent both known and unknown malware from causing harm to endpoints. If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call: North America Toll-Free: 866.486.4842 (866.4.UNIT42) EMEA: +31.20.299.3130 APAC: +65.6983.8730 Japan: +81.50.1790.0200 Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance. Malicious destination IP addresses 2.26.98[.]67 62.60.179[.]230 87.120.107[.]33 91.92.243[.]29 103.245.236[.]146 178.16.54[.]31 178.16.54[.]109 194.76.227[.]94 206.189.229[.]43 Malware samples 01a96eeafb72042b3f69afd21b4c9155dbfe7f97ab3dca392972ad531a075ac2 9639f7ebc6a6d69d7bf5b8bc869e7783a1406088f192868624ad8919e9bfd1d4 bf24277400cc453d530e4277d3bd24e96c5e409adef6970518bdc59205aa0241 e310476c41ae4f6e3c4ed9bb88303ee6e5e1455bd7afe51cf48965ea7599e6e5 e3513922666c202c1ae5c06eea277ba10477868d6d89ce2819f4f8ff9070bc85 e5715e6611ef6bcb233f5d2098510dab3db408abbb728b00e1821bb255829373 Phorpiex (Malware Family) – Malpedia Old Wine in the New Bottle: Mirai Variant Targets Multiple IoT Devices – Palo Alto Networks Unit 42 It Was Not Me! Malware-Initiated Vulnerability Scanning Is on the Rise – Palo Alto Networks Unit 42 ZT-IP, which we have also called No-DNS or D2IP detection, extends the zero trust security model to IP-based traffic. This ensures that network services only connect to IP addresses previously resolved and approved by a trusted DNS service. Outbound connections to an IP address without a prior, valid DNS query are flagged as suspicious and blocked. In Figure 9, ZT-IP secures the network and firewall level through a simple enforcement mechanism: The mechanism: Tracking legitimate IP addresses: The system monitors all valid DNS responses to learn which IP addresses are approved for outbound connections Building a trust list: These approved IP addresses are tracked in a secure list, along with a time limit for how long they remain valid Enforcement: Whenever a device attempts an outbound connection to an IP address, the firewall checks this list Blocking bypass attempts: The connection is only allowed if the destination IP address was recently approved by DNS. If an IP address is not on the list or its approved time limit has expired, the connection is immediately flagged as a potentialNo-DNS threat and blocked. Because some legitimate network protocols (such as DNS, Neighbor Discovery Protocol, VoIP, P2P and certain IoT services) connect directly to IP addresses by design, ZT-IP incorporates mechanisms to prevent false positives. It uses allowlists that exempt private intranet IP addresses, specific trusted network protocols and well-known benign public IP addresses from these strict DNS checks. ZT-IP delivers the following key advantages for network defense: Stops patient zero attacks: It proactively blocks unknown C2 callbacks and malware attempting to phone home using hard-coded IP addresses Secures shared infrastructure: It differentiates between legitimate traffic and malicious connections targeting the same shared cloud hosting environments, securely blocking the malicious traffic because it lacks the necessary DNS context Network-level protection: By operating at the firewall level, ZT-IP secures environments like IoT and operational technology (OT) without requiring endpoint agents to be installed on every single device
unit42.paloaltonetworks.comAug 4, 2026extracted
Malicious trading website drops malware that hands your browser to attackers
During our threat hunting, we found a campaign using the same malware loader from our previous research to deliver a different threat: Needle Stealer, data-stealing malware designed to quietly harvest sensitive information from infected devices, including browser data, login sessions, and cryptocurrency wallets. In this case, attackers used a website promoting a tool called TradingClaw (tradingclaw[.]pro), which claims to be an AI-powered assistant for TradingView. TradingView is a legitimate platform used by traders to analyze financial markets, but this fake TradingClaw site is not part of TradingView, nor is it related to the legitimate startup tradingclaw.chat. Instead, it’s being used here as a lure to trick people into downloading malware. What is Needle Stealer? Needle is a modular infostealer written in Golang. In simple terms, that means it’s built in pieces, so attackers can turn features on or off depending on what they want to steal. According to its control panel, Needle includes: Needle Core: The main component, with features like form grabbing (capturing data you enter into websites) and clipboard hijacking Extension module: Controls browsers, redirects traffic, injects scripts, and replaces downloads Desktop wallet spoofer: Targets cryptocurrency wallet apps like Ledger, Trezor, and Exodus Browser wallet spoofer: Targets browser-based wallets like MetaMask and Coinbase, including attempts to extract seed phrases The panel also shows a “coming soon” feature to generate fake Google or Cloudflare-style pages, suggesting the attackers plan to expand into more advanced phishing techniques. Analysis of the TradingClaw campaign In this campaign, the malware is distributed through a fake website advertising TradingClaw as an AI trading tool. The site itself behaves selectively. In some cases, visitors are shown the fake TradingClaw page, while in others they are redirected to a different site (studypages[.]com). This kind of filtering is commonly used by attackers to avoid detection and only show the malicious content to intended targets. Search engines, for example, see the Studypages version: If a user proceeds, they are prompted to download a ZIP file. This file contains the first stage of the infection chain. Like in the previous campaign, the attack relies on a technique called DLL hijacking. In simple terms, this means the malware disguises itself as a legitimate file that a trusted program will load automatically. When the program runs, it unknowingly executes the malicious code instead. In this case, the DLL loader (named iviewers.dll) is executed first. It then loads a second-stage DLL, which ultimately injects the Needle Stealer into a legitimate Windows process (RegAsm.exe) using a technique known as process hollowing. The stealer is developed in Golang, and most of the functions are implemented in the “ext” package. What the malware does Once installed, the Needle core module can: Take screenshots of the infected system Steal browser data, including history, cookies, and saved information Extract data from apps like Telegram and FTP clients Collect files such as .txt documents and wallet data Steal cryptocurrency wallet information One of the more concerning features is its ability to install malicious browser extensions. Malicious browser extensions The stealer also supports the distribution of malicious browser extensions, giving attackers a powerful way to take control of the victim’s browser. We identified multiple variations of these extensions, each with slightly different file structures and components. Behind the scenes, the malware uses built-in Golang features to unpack a hidden ZIP archive (often named base.zip or meta.zip) that contains the extension files, along with a configuration file (cfg.json). Partial cfg.json config file: { "extension_host": {}, "api_key": "… "server_url": "https://C2/api/v2", "self_destruct": true, "base_extension": true, "ext_manifest": { "account_extension_type": 0, "active_permissions": { "api": [ "history", "notifications", "storage", "tabs", "webNavigation", "declarativeNetRequest", "scripting", "declarativeNetRequestWithHostAccess", "sidePanel" ], "explicit_host": [ " " ], "manifest_permissions": [], "scriptable_host": [ " " ] }, "commands": { "_execute_action": { "was_assigned": true } }, … This configuration file is key. It tells the malware where to send stolen data (the command-and-control server), which malicious extension to install, and which features to enable. The stealer extension is dropped in a random folder in the path %LOCALAPPDATA%\Packages\Extensions. The folder contains three main files popup.js, content.js, and background.js. The extensions analyzed have Google-related names. What the malicious extensions can do The extension gives attackers near full control over the browser, with capabilities that go far beyond typical malware. It can: Connect to a remote server using a built-in API key and regularly check in for instructions. It can also switch to backup domains if the main server goes offline. Generate a unique ID to track the infected user over time. Collect full browsing history and send it to a remote server (/upload ). Monitor what you’re doing in real time, including which sites you visit, and apply attacker-controlled redirect rules. This allows it to silently send you to different websites or alter what you see on a page, including injecting or hiding content. Intercept downloads, cancel legitimate files, and replace them with malicious ones from attacker-controlled servers. Inject scripts directly into web pages, enabling further data theft or manipulation. Display fake browser notifications with attacker-controlled text and images. How it communicates with attackers The stealer and its extension communicate with command-and-control (C2) servers using several API endpoints. These are essentially different “channels” used for specific tasks: /backup-domains/active —retrieves backup servers to stay connected if the main one is blocked /upload —sends stolen data back to the attackers /extension —receives instructions for redirects, downloads, and notifications /scripts —downloads malicious code to inject into web pages How to stay safe Scammers are increasingly using AI-themed tools to make fake websites look legitimate. In this case, a supposed “AI trading assistant” was used to trick people into installing malware. To reduce your risk: Download software only from official websites. If a tool claims to work with a well-known platform, check the platform’s official site to confirm it’s real. Check who created the file before running it. Look at the publisher name and avoid anything that looks unfamiliar or inconsistent. Review your browser extensions regularly. Remove anything you don’t recognize, especially extensions you didn’t knowingly install. What to do if you think you’ve been affected If you think you may have downloaded this infostealer: Check EDR and firewall logs for communications with the C2s listed in the IOCs part. From a different, clean device, sign out of every active session on your important accounts: Google, Microsoft 365, any banking portal, GitHub, Discord, Telegram, Steam, and your crypto exchange. Change all passwords and enable 2FA for accounts you have accessed from this machine. Check the folder %LOCALAPPDATA%\Packages\Extensions and suspicious browser extensions. If you have cryptocurrency wallets on the machine, move the funds from a clean device immediately. This is what these operators monetize first. Run a full scan with Malwarebytes. Indicators of Compromise (IOCs) HASH 95dcac62fc15e99d112d812f7687292e34de0e8e0a39e4f12082f726fa1b50ed 0d10a6472facabf7d7a8cfd2492fc990b890754c3d90888ef9fe5b2d2cca41c0 Domains Tradingclaw[.]pro: fake website Chrocustumapp[.]com: related to malicious extension Chrocustomreversal[.]com: related to malicious extension google-services[.]cc: related to CountLoader/DeepLoad Coretest[.]digital: C2 panel Reisen[.]work: C2 panel IPs 178[.]16[.]55[.]234: C2 panel 185[.]11[.]61[.]149: C2 panel 37[.]221[.]66[.]27: C2 panel 2[.]56[.]179[.]16: C2 panel 178[.]16[.]54[.]109: C2 panel 37[.]221[.]66[.]27: C2 panel 209[.]17[.]118[.]17: C2 panel 162[.]216[.]5[.]130: C2 panel Researcher update April 28, 2026 Updated to specify the malware family: CountLoader/DeepLoad. From reporting threats to removing them. Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.
malwarebytes.comApr 22, 2026extracted