Thought injection attacks succeed on reasoning agents
Original title: I Think, Therefore I Attack: Evaluating Thought Injection Attacks on Reasoning Agents
Thought injection via chain-of-thought reasoning traces achieves up to 50% ASR on agent objectives like reverse-shell exfiltration in production models.
Thought injection feeds untrusted text resembling model deliberation back to large reasoning models. Static prompts and indirect injection in containerized agents achieve up to 50% success on tasks such as repository backdooring. Thinking tags suppress disclosure but tag stripping is unreliable. Standard prompt injection fails where thought injection succeeds.
- Up to 50% ASR on reverse-shell and backdooring
- Models: GLM-5.3, MiniMax-M2.5, Qwen3.8-Flash-Next
- Thinking tags suppress agent disclosure
- Tag stripping unreliable as defense
- Model-specific effects of prompts and styles
From the original article
1 min read at arxiv.orgLarge Reasoning Models (LRMs) are one of the key components of current agentic systems. LRMs augment their output with an explicit chain-of-thought reasoning stage, introducing a new attack surface for thought injection, in which untrusted text resembling the model's own deliberation is fed back to the model.
By Parth Illendula · arxiv.org
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