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Extreme fidelity loss reveals critical vulnerabilities in long-horizon tasks that standard accuracy metrics overlook.
Indirect prompt injection can compromise AI systems like DeepSeek Harness, with attack success rates reaching up to 25.5% under certain conditions.
SkillJack reveals that self-evolving agents can unknowingly incorporate malicious skills, making traditional safety measures ineffective against persistent threats.
Despite high rationale identification, search-augmented models struggle with refusal, achieving only 42.9% correct halting on unanswerable multi-hop questions.
EviSD achieves state-of-the-art performance in question-answering tasks by leveraging privileged evidence, outperforming existing methods while maintaining efficiency in response generation.
AI-Infra-Guard reveals that a unified security framework can effectively address the diverse attack surfaces of AI agents, making it a game-changer for AI safety.
