New framework analyzes LLM jailbreaks using information theory

Researchers have developed an information-theoretic framework to analyze and understand "intent-hiding jailbreaks" in large language models. These attacks work by embedding harmful requests within larger, seemingly benign queries, making the model more likely to comply. The framework models this by associating tasks with probabilities of being judged harmful, and the goal is to select auxiliary tasks that maintain the estimated intent even when a harmful target is included. The study explores both query-independent and query-dependent settings, finding that compositional queries can indeed elicit target behaviors beyond direct requests within certain search budgets, though larger bundle sizes can decrease target preservation in some models. AI

IMPACT This research provides a theoretical framework for understanding and potentially mitigating sophisticated jailbreak attacks on LLMs.

RANK_REASON Academic paper on LLM safety research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Compositional Attacks
• cs.LG
• Intent-Hiding Jailbreaks

• paper
• safety

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