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.
Darkmind: Latent chain-of-thought backdoor in customized llms
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ShadowCoT introduces a reasoning-level backdoor attack on LLMs achieving 94.4% attack success rate and 88.4% hijacking success rate with 0.15% parameter updates via internal state conditioning and reasoning chain pollution.
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
citing papers explorer
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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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ShadowCoT: Cognitive Hijacking for Stealthy Reasoning Backdoors in LLMs
ShadowCoT introduces a reasoning-level backdoor attack on LLMs achieving 94.4% attack success rate and 88.4% hijacking success rate with 0.15% parameter updates via internal state conditioning and reasoning chain pollution.
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Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.