PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?
Read the original on arXiv AI →PLCBench is a hardware‑in‑the‑loop framework that evaluates whether autonomous large language model agents can transform network‑reachable programmable logic controllers (PLCs) into sustained physical threats. It integrates vendor‑native PLC interaction, commercial PLC execution, closed‑loop process simulation, and deterministic diagnostics to classify episodes into usable interaction, process‑linked manipulation, and sustained physical impact. Across 240 real‑PLC episodes with five LLM families, 31.3% achieved sustained physical objectives, revealing that richer process observation improves success rates and pinpointing failure points for future defense research.
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