Bibliographic Information: Chen, X., Nie, Y., Guo, W., & Zhang, X. (2024). When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
Research Objective: This paper investigates the application of DRL in developing more efficient and effective black-box jailbreaking attacks against LLMs. The authors aim to overcome the limitations of existing stochastic search-based attacks by introducing a guided search approach driven by a DRL agent.
Methodology: The researchers formulate the jailbreaking attack as a Markov Decision Process (MDP) and train a DRL agent to navigate the search space of potential jailbreaking prompts. The agent learns to select appropriate prompt structure mutators based on a customized reward function that evaluates the relevance of the target LLM's response to the harmful question. The training process involves refining the agent's policy to maximize the accumulated reward, indicating successful jailbreaking attempts.
Key Findings: The study demonstrates that RLbreaker consistently outperforms existing jailbreaking attacks, including genetic algorithm-based and in-context learning-based methods, across various LLMs, including large-scale models like Llama2-70b-chat. RLbreaker exhibits superior effectiveness in bypassing LLM alignments, particularly for challenging harmful questions. Moreover, the trained RL agents demonstrate promising transferability across different LLM models.
Main Conclusions: The research concludes that DRL provides a powerful framework for developing efficient and transferable jailbreaking attacks against LLMs. The guided search approach employed by RLbreaker significantly improves attack effectiveness compared to stochastic methods. The authors emphasize the importance of this research in understanding and mitigating the vulnerabilities of aligned LLMs.
Significance: This work contributes significantly to the field of LLM security and alignment by introducing a novel and highly effective jailbreaking technique. The findings highlight the potential risks associated with malicious prompt engineering and emphasize the need for robust defenses against such attacks.
Limitations and Future Research: The authors acknowledge the potential for false negatives in their reward function and suggest exploring alternative strategies to mitigate this limitation. Future research directions include expanding the action space to incorporate more sophisticated jailbreaking techniques, investigating the applicability of RLbreaker to multi-modal models, and developing robust defenses against DRL-driven attacks.
A otro idioma
del contenido fuente
arxiv.org
Ideas clave extraídas de
by Xuan Chen, Y... a las arxiv.org 10-17-2024
https://arxiv.org/pdf/2406.08705.pdfConsultas más profundas