arXiv:2606.03136v1 Announce Type: cross Abstract: 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 prod
As LLMs become more integrated into critical systems, the sophistication of adversarial attacks necessitates more advanced and proactive defense mechanisms, moving beyond simple content filters.
Proactive detection of adversarial intent in multi-turn LLM conversations is crucial for maintaining AI safety, preventing misuse, and ensuring the reliability of AI applications in sensitive contexts.
This research shifts LLM security from reactive content filtering to predictive analysis of conversational dynamics, enabling earlier intervention against jailbreak attempts.
- · LLM developers
- · AI safety researchers
- · Organizations deploying LLMs
- · Malicious actors
- · Black-box jailbreak methods
Improved guardrails and safety features for cutting-edge large language models.
Increased trust and broader adoption of LLMs in high-risk applications due to enhanced security.
A potential arms race between geometric profiling defenses and increasingly sophisticated multi-turn adversarial attack methodologies.
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Read at arXiv cs.CL