AI-SPM vs AutoProber

AI-SPM (AI Security Posture Management) is an open-source infrastructure layer for securing LLM pipelines running in production. It targets three attack surfaces that traditional application security doesn't cover: prompt injection (including obfuscated and multi-step variants), tool abuse via unvalidated structured outputs, and data exfiltration through PII leakage in model responses. The architecture layers a gateway intercept layer over incoming prompts, runs context inspection before the LLM sees any input, enforces policies via Open Policy Agent (OPA) for declarative, auditable rules, then pipes all events through Apache Kafka and Apache Flink for real-time streaming analysis. This means security posture can be monitored and enforced at scale without blocking the inference path. The project is genuinely fresh — posted as a Show HN today. Early community feedback pointed to capability-based token models (similar to OS kernel permission rings) as a complementary approach to content-scanning, which the author acknowledged as a meaningful gap. The timing is right: as companies push AI agents from demos to production, the security tooling layer is largely underdeveloped. AI-SPM is one of the first OSS projects to tackle it at the infrastructure layer rather than with prompt-level guardrails alone.

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.