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Detecting Jailbreaks from Within: Entropy Dynamics Across LLM Layers Reveal Harmful Intent
New research analyzes token-level predictive entropy trajectories across LLM layers to detect jailbreak attacks encoded in the model's internal representations.
A study published on arXiv investigates how harmful intent is encoded within LLM internal representations, offering a new approach to jailbreak detection. While most defenses operate at the prompt or output level, this work examines token-level predictive entropy trajectories across layers of a frozen LLM.
The findings show that harmful intent has distinct encoding patterns in the model's middle layers, visible through entropy dynamics that differ from benign queries. This enables detection at the internal representation level before policy-violating output is generated.
Published on arXiv cs.CL on June 25, 2026, this research opens a new dimension for LLM safety beyond input filtering and output monitoring, potentially enabling earlier and more robust jailbreak detection in deployed systems.
Why it matters
Introduces an internal-representation-based approach to jailbreak detection, expanding LLM safety beyond traditional input/output filtering.