Title: Let me PASS: Formalization Driven Prompt Jailbreaking via Reinforcement Learning
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Prompt Jailbreaking Attacks, LLM security, reinforcement learning, formalization

Abstract: Large language models (LLMs) have demonstrated remarkable capabilities, yet they also introduce novel security challenges. For instance, prompt jailbreaking attacks involve adversaries crafting sophisticated prompts to elicit responses from LLMs that deviate from human values. To uncover vulnerabilities in LLM alignment, we propose the PASS framework (Prompt Jailbreaking via Semantic and Structural Formalization). Specifically, PASS employs reinforcement learning to transform initial jailbreak prompts into formalized descriptions, which enhances stealthiness and enables bypassing existing alignment defenses. The jailbreak outputs are then structured into a GraphRAG system that, by leveraging extracted relevant terms and formalized symbols as contextual input alongside the original query, strengthens subsequent attacks and facilitates more effective jailbreaks. We conducted extensive experiments on common open-source models, demonstrating the effectiveness of our attack.

TL;DR: We propose PASS, a reinforcement learning-based framework that formalizes jailbreak prompts and leverages GraphRAG to stealthily and effectively bypass LLM alignment defenses.