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Static scanners fail against adaptive evasions, but a new behavior-centric auditor can detect 97% of malicious skills with minimal false positives.
Coding agents guess their way through underspecified instructions, leading to alarming action-boundary violations that challenge the notion of safe autonomy.
AutoSpec achieves up to 4.8x higher F1 scores than traditional methods, transforming safety rule evolution into a precise, interpretable process for LLM agents.
