PsychoPass: Geometric Profiling of Multi-Turn Adversarial LLM Conversations
Multi-turn jailbreak attacks on large language models (LLMs) reveal a mismatch in current guardrails: they operate on individual turns, while attacks unfold as trajectories across conversations. We propose a shift from content to dynamics, modeling conversations as paths in representation space and asking whether adversarial intent is encoded early in their geometry. We introduce PsychoPass, a framework that extracts geometric features from conversation trajectories in embedding space to predict a potential attack before harmful content is produced. These features achieve near-perfect performance in naïve classifiers, which is largely explained by the inclusion of number of turns as a fea...
| Authors | Muberra Ozmen, Subhabrata Majumdar |
|---|---|
| Primary category | cs.CR |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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