vulnhuntr

Why we included this project

Vulnhuntr takes a different approach to finding remotely exploitable bugs: rather than match against known signatures, it lets a large language model walk the call chain from where untrusted input enters a Python codebase to where the server acts on it, reconstructing logic across files to surface multi-step flaws that conventional static analyzers miss. It has already earned its credibility, with the authors using it to find and responsibly disclose zero-days in popular projects such as ComfyUI, FastChat, and Ragflow. You point it at a local clone, optionally narrow it to specific entry-point files, and it returns a report with reasoning, confidence scores, and a proof-of-concept exploit for each candidate. The trade-offs are worth knowing before you run it: it targets a defined set of vulnerability classes rather than every possible bug, and scanning a large repository can rack up a serious LLM API bill. For teams reviewing input-handling code by hand, that scoped, evidence-backed output makes it a useful second pair of eyes.