$M^3$ framework uses Markov Decision Process for advanced LLM jailbreaking

Researchers have developed a new framework called $M^3$ that treats multi-turn jailbreaking of large language models as a Markov Decision Process. This approach aims to improve the effectiveness of red-teaming by allowing an attacker LLM to interact with a target LLM, guided by an internal state from a separate state LLM. In evaluations across two benchmarks and six target models, $M^3$ demonstrated superior Attack Success Rates compared to existing methods, achieving up to a 53% improvement in ASR@5. AI

IMPACT This research introduces a novel, principled framework for evaluating and improving LLM safety by systematically exploring multi-turn jailbreaking vulnerabilities.

RANK_REASON The cluster contains a research paper detailing a new methodology for evaluating LLM safety. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Attack Success Rates
• large-language models
• Markov Decision Process

• paper
• safety

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