Vulnerability management is always a race. Attackers move quickly, scans take time, and if your scanner can’t keep up, you’re left exposed. That’s why Intruder’s security team kicked off a research project: could AI help us build new vulnerability checks faster, without dropping our high standards for quality? After all, speed is only useful if the detections are solid - a check that fires false positives (or worse, misses real issues) doesn’t help anyone. In this post, we’ll share how we’ve been experimenting with AI, what’s working well, and where it falls short. One-shot vs. Agentic Approach We started simple: drop prompts into an LLM chatbot and see if it could write Nuclei templates. The results were messy. Outputs referenced features that didn’t exist, spat out invalid syntax, and…