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The account motivates complementary defenses: strengthen resistance and recovery alongside prevention of spontaneous deviations, and audit and restrict unintended communication paths that can turn local failures into collective loss of control.
These results show how a latent loss of control can become an external violation: the task objective remains intact, but an executable opportunity can turn a missing control boundary into consequential action.
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.
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.
