atlas-detect vs AutoProber

atlas-detect is an open-source Rust tool that maps MITRE ATLAS techniques to real-time detection rules for LLM systems and AI agents. MITRE ATLAS is the adversarial threat landscape framework for AI — think ATT&CK but for machine learning systems — and atlas-detect is the first practical, deployable detection engine built on top of it. It ships with 97 pre-built detection rules covering 16 adversarial tactics, from prompt injection and model inversion to training data poisoning. The engine is written in Rust and designed for single-pass regex scanning, making it fast enough for inline deployment in API gateways or agent middleware. You feed it prompt-response pairs (or full conversation logs) and it returns matched technique IDs, severity ratings, and structured evidence. Think of it as a Snort/Suricata ruleset, but for the semantic attack surface of LLMs. With only 4 stars as of today, atlas-detect is an extremely early project — but it's filling a gap that no major security vendor has meaningfully addressed. As enterprises deploy AI agents with real tool access and real consequences, ATLAS-aligned detection will become a compliance requirement. This is the seed of that tooling.

AutoProber is an open-source hardware security research platform that puts an LLM agent in control of a physical CNC machine to autonomously probe circuit boards. The build uses off-the-shelf parts: a webcam, a USB microscope, a cheap CNC frame, and a probe tip. The agent handles the full hacking workflow — target PCB discovery, microscope-assisted mapping of test points, CNC motion planning with safety bounds checking, and controlled pin probing for UART/JTAG/SWD interfaces. The software stack is pure Python. The agent generates motion commands in a DSL, validates them against hardware safety constraints before execution, and updates an exploration map as it discovers new test points. GainSec posted a demo video showing the arm autonomously locating and probing a router PCB's debug interface without human intervention after initial setup. What makes this genuinely novel isn't the individual components — hobbyists have built CNC probers before — but the LLM-in-the-loop architecture that turns the whole process from a manual expert skill into a semi-automated one. Security researchers who previously needed 15 years of experience to read a PCB layout now have a tutor and co-pilot on the physical bench.