GuardrailAgent defends LLMs against adaptive jailbreak attacks
Multi-agent framework updates knowledge base from attack summaries to achieve 93.1% detection on over ten jailbreaks.
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Multi-agent framework updates knowledge base from attack summaries to achieve 93.1% detection on over ten jailbreaks.
Assess, Decide, Respond is trained with reinforcement learning and led multi-turn safety on 14 benchmarks across two model families.
Researchers introduce Decoy Direction Optimization, a cheap weight-editing defense that blinds refusal-direction ablation attacks against open-weight LLM safety guardrails.
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