AutoProber vs Mozilla 0DIN AI Scanner

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.

Mozilla's AI security team — 0DIN (Zero Day Investigation Network) — open-sourced their internal LLM vulnerability scanner on April 10, 2026. Unlike synthetic red-teaming tools, this is built on real attack knowledge: 0DIN researchers have spent two years getting paid to break every major frontier model, discovering and reporting thousands of verified vulnerabilities. Those discoveries are now encoded as reproducible probes. Built on NVIDIA's GARAK open-source framework, the 0DIN Scanner adds a graphical interface, automated scan scheduling, cross-model comparative analysis, and enterprise reporting. It ships with 179 community probes covering 35 vulnerability families — prompt injection, jailbreaks, data leakage, harmful content generation, and more — all aligned to the OWASP LLM Top 10. Six specialty probes target advanced threat categories. For any team deploying LLMs in production — RAG systems, agents with tool access, customer-facing chatbots — this is now the baseline for security auditing. The Apache 2.0 license means enterprise deployment without legal headaches. With LLM security audits running $50K-$200K from specialist firms, this democratizes access to professional-grade testing.