[Submitted on 22 Oct 2026]
Title:$M^3$: A Multi-LLM System for Multi-Turn Jailbreaking as a Markov Decision Process
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Abstract:Modern Large Language Models (LLMs) remain vulnerable to carefully orchestrated multi-turn interactions that progressively bypass safety mechanisms. However, many existing red-teaming approaches rely on heuristics or impose strong assumptions that limit their applicability in fully black box settings. We address this gap by introducing $M^3$, an automated red-teaming framework that formalizes multi-turn jailbreaking as a finite-horizon Markov Decision Process (MDP). In $M^3$, an attacker LLM interacts with a target LLM while operating over a compact internal state produced by a state LLM. Across two benchmarks and six target models, $M^3$ consistently outperforms state-of-the-art multi-turn red-teaming methods in Attack Success Rates (ASR), achieving improvements of up to 53% in ASR@5. Even with lightweight attacker, $M^3$ attains gains up to 26% in ASR@1 and 34% in ASR@5, compared to baselines that use higher capacity attackers. Beyond improved effectiveness, $M^3$ exhibits steeper risk escalation over conversational turns and achieves more consistent performance across risk categories (up to 4.82 improvement in Gini index) without relying on predefined risk taxonomy, providing a scalable and principled framework for multi-turn red-teaming and safety evaluation.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.31937 [cs.AI]
(or arXiv:2610.31937v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.31937
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuqing Huang [view email] [v1] Thu, 22 Oct 2026 21:35:21 UTC (10,595 KB)
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