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When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
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The honesty of large language models (LLMs) is a critical alignment challenge, especially as advanced systems with chain-of-thought (CoT) reasoning may strategically deceive humans. Unlike traditional honesty issues on LLMs, which could be possibly explained as some kind of hallucination, those models' explicit thought paths enable us to study strategic deception--goal-driven, intentional misinformation where reasoning contradicts outputs. Using representation engineering, we systematically induce, detect, and control such deception in CoT-enabled LLMs, extracting "deception vectors" via Linear Artificial Tomography (LAT) for 89% detection accuracy. Through activation steering, we achieve a 40% success rate in eliciting context-appropriate deception without explicit prompts, unveiling the specific honesty-related issue of reasoning models and providing tools for trustworthy AI alignment.
Forward citations
Cited by 4 Pith papers
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Faithful reasoning and safety pull in opposite directions in current reasoning models, and the two behaviors are controlled by anti-correlated internal vectors that can be steered independently.
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The study introduces TruthfulnessEval and reports that 4-bit quantization preserves simple true/false accuracy, but explicit 'lie' prompts make quantized and full-precision LLMs output falsehoods even when internal pr...
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Steering 112 manually identified 'deception features' in Qwen3-4B changed whether the model revealed a hidden word, but the same steering test was used to pick the features.
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Adversarial Activation Patching: A Framework for Detecting and Mitigating Emergent Deception in Safety-Aligned Transformers
A framework that borrows activation patching to adversarially induce and measure deception, supported only by an underspecified toy network simulation.
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