An attention-guided RL reward combined with diverse persuasion strategies produces higher attack success rates against large reasoning models than prior jailbreak methods.
Chain-of-thought hijacking
4 Pith papers cite this work. Polarity classification is still indexing.
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A new backdoor technique called TSBH uses reverse tree search to create malicious chain-of-thought data and injects it in two stages to hijack LLM reasoning upon trigger activation.
Safety degradation in large reasoning models occurs only after chain-of-thought is enabled; adding pre-CoT safety signals from a BERT classifier on safe models improves safety while preserving reasoning ability.
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
citing papers explorer
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Attention-Guided Reward for Reinforcement Learning-based Jailbreak against Large Reasoning Models
An attention-guided RL reward combined with diverse persuasion strategies produces higher attack success rates against large reasoning models than prior jailbreak methods.
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Unreal Thinking: Chain-of-Thought Hijacking via Two-stage Backdoor
A new backdoor technique called TSBH uses reverse tree search to create malicious chain-of-thought data and injects it in two stages to hijack LLM reasoning upon trigger activation.
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Towards Safer Large Reasoning Models by Promoting Safety Decision-Making before Chain-of-Thought Generation
Safety degradation in large reasoning models occurs only after chain-of-thought is enabled; adding pre-CoT safety signals from a BERT classifier on safe models improves safety while preserving reasoning ability.
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Security Considerations for Multi-agent Systems
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.