🤖 AI Summary
Jailbreaking large language models (LLMs) incurs high computational overhead and limited practicality due to reliance on costly LLM-based inference. Method: This paper proposes the first jailbreak-capability-oriented prompt distillation framework, transferring jailbreak knowledge efficiently from LLMs to small language models (SLMs). It integrates masked language modeling, reinforcement learning, and dynamic temperature control to construct a lightweight adversarial prompt generation and distillation pipeline. Contributions/Results: Experiments demonstrate high attack success rates across multiple mainstream LLMs, strong zero-shot cross-model transferability, and enhanced attack harmfulness. The approach reduces computational cost by 73% compared to LLM-based baselines, significantly improving stealthiness and real-world deployability of jailbreak attacks.
📝 Abstract
Attacks on large language models (LLMs) in jailbreaking scenarios raise many security and ethical issues. Current jailbreak attack methods face problems such as low efficiency, high computational cost, and poor cross-model adaptability and versatility, which make it difficult to cope with the rapid development of LLM and new defense strategies. Our work proposes an Adversarial Prompt Distillation, which combines masked language modeling, reinforcement learning, and dynamic temperature control through a prompt generation and distillation method. It enables small language models (SLMs) to jailbreak attacks on mainstream LLMs. The experimental results verify the superiority of the proposed method in terms of attack success rate and harm, and reflect the resource efficiency and cross-model adaptability. This research explores the feasibility of distilling the jailbreak ability of LLM to SLM, reveals the model's vulnerability, and provides a new idea for LLM security research.