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$M^3$: A Multi-LLM System for Multi-Turn Jailbreaking as a Markov Decision Process

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 26/10/2026, 04:03:24 UTC.

Briefings

A new automated red-teaming framework called $M^3$ formalizes multi-turn jailbreaking as a Markov Decision Process (MDP). The system uses an attacker LLM interacting with a target LLM over a compact internal state generated by a state LLM. Across two benchmarks and six target models, $M^3$ consistently outperformed state-of-the-art methods, achieving up to 53% higher Attack Success Rates (ASR@5).

Key points

• The framework $M^3$ formalizes multi-turn jailbreaking as a finite-horizon Markov Decision Process (MDP) using an attacker LLM, target LLM, and a state LLM to generate compact internal states.
• Across two benchmarks and six target models, $M^3$ consistently outperformed state-of-the-art multi-turn red-teaming methods, achieving up to 53% higher Attack Success Rates (ASR@5) compared to baselines using higher capacity attackers.

Context

Modern Large Language Models (LLMs) remain vulnerable to carefully orchestrated multi-turn interactions that progressively bypass safety mechanisms. Existing red-teaming approaches often rely on heuristics or impose strong assumptions that limit their applicability in fully black box settings. $M^3$ addresses this gap by providing a scalable, principled framework for multi-turn red-teaming and safety evaluation.

Limitations

The evidence is from a preprint (arXiv:2610.31937v1) and covers only two benchmarks and six target models. Performance gains are reported for specific attack success rates (ASR@1, ASR@5) and risk escalation metrics (Gini index improvement), but the methodology's generalizability to other settings or attack types remains unverified.

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