Evading Chain-of-Thought Monitoring Through Model Poisoning
Chain-of-thought (CoT) monitoring is an increasingly important component of AI safety stacks but relies on the assumption that a model's reasoning trace is informative about its actions. This work studies the limits of CoT monitoring through the lens of model poisoning. We demonstrate that backdoors can be implanted into reasoning models to elicit an attacker-chosen behavior while their CoT traces appear entirely benign. We find that these CoT-Hidden backdoors can be induced through simple fine-tuning recipes across reasoning-model architectures and sizes. When direct poisoning is ineffective, we introduce a curriculum training approach that progressively teaches the model to produce an a...
| Authors | Giorgio Severi, Shujaat Mirza, Blake Bullwinkel et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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