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This work identifies and formalizes this failure mode, which is term destructive resource preemption: obtaining the resources required for a requested task by terminating, overwriting, evicting, or degrading an incumbent task.
Through harness-policy co-evolution, SafeEvolve converts safety experience into an evolved runtime harness and improved policy behavior, and experiments show that SafeEvolve achieves a stronger safety-utility tradeoff than existing baselines.
Safety Harness Evolution (SHE) is proposed, a framework that learns evolving safe boundaries from rollout trajectories and introduces an attribution-guided evolution loop that converts trajectory failures into structured diagnoses, learns artifact-specific boundary refinements, and selects evolved harnesses through saf...
This work presents SkillSafetyBench, a runnable benchmark for evaluating skill-facing safety failures, and suggests that agent safety depends not only on model-level alignment, but also on how agents interpret skills, trust workflow context, and act through executable environments.
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