Automatic Dialectic Jailbreak: A Framework for Generating Effective Jailbreak Strategies

Large language models (LLMs) can be jailbroken to produce malicious or unethical content with embedded jailbreaking prompts. Unfortunately, current jailbreak attack techniques suffer from adaptability issues due to reliance on the fixed evaluation models and incapability problems of surviving from a wide range of defense mechanisms. In this work, we propose to model the jailbreak attack problem as a Stackelberg multi-objective game between two LLMs engaged in a Hegelian-Dialectic-style debate enabling the automatic generation of jailbreak strategy (ADJ). In the ADJ, iterative thesis-antithesis-synthesis cycles of Hegelian dialectical reasoning are executed to guarantee that both attacker and defender can maximize their own utility while minimizing that of their opponent. We propose to map the optimization problem from the original parameter space into a Hilbert space via Haar wavelet transformation, for efficiently extracting localized and structurally significant information. In this functional space, we solve a convex multi-objective optimization problem to construct a common descent direction that better aligns with the objectives in the ADJ. In order to ensure sufficient descent for each objective in ADJ, we construct a subset of descent components and directly integrate them into the optimization objective. We theoretically validate the existence of a Pareto-Nash equilibrium achieved by our Automatic Dialectic Jailbreak method and demonstrate that our algorithm is able to converge to this Pareto-Nash equilibrium. Our source code is available at https://github. com/johnston1yu/ADJ Warning: This paper contains potentially harmful text.