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Harness-policy co-evolution can reduce adverse safety responses by 3x while simultaneously boosting benign utility in LLM agents.
Evolving safety harnesses using trajectory data can reduce agent safety risks by over 3x while enhancing overall utility.
Multi-turn jailbreaks exploit user intent in ways that traditional safety measures fail to detect, revealing a critical vulnerability in LLM interactions.
AgentDoG 1.5 proves you can achieve GPT-5.4-level agent safety with open-source models trained on just 1k samples, slashing deployment overhead by two orders of magnitude.