Measuring Safety Alignment Effects in Autonomous Security Agents

Do stock safety-aligned language models and their uncensored or abliterated derivatives behave differently when run as autonomous security agents? Single-turn refusal benchmarks cannot answer this question: security agents must inspect repositories, call tools, and produce vulnerability evidence inside authorized sandboxes. We present a trace-based benchmark of 30 local vulnerability-analysis tasks with fixed tools, deterministic success predicates, redaction rules, and grounding checks, and com

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ethics.ai (19 May 2026), “Measuring Safety Alignment Effects in Autonomous Security Agents,” evidence record 4029, https://ethics.ai/record/4029 (originally published by arXiv).

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