LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs

• URL: http://arxiv.org/abs/2411.08862v1
• Date: Wed, 13 Nov 2024 18:44:30 GMT
• Title: LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
• Authors: Piyush Jha, Arnav Arora, Vijay Ganesh,
• Abstract summary: We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Our method significantly outperforms existing red-teaming approaches, achieving a +57.2% improvement in Attack Success Rate (ASR) on LLaMA2-7B-chat and a +50.3% increase on Claude 2.
• Score: 13.36946005380889
• License:
• Abstract: We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional methods, which require complex prompt engineering or white-box access, LLMStinger uses a reinforcement learning (RL) loop to fine-tune an attacker LLM, generating new suffixes based on existing attacks for harmful questions from the HarmBench benchmark. Our method significantly outperforms existing red-teaming approaches (we compared against 15 of the latest methods), achieving a +57.2% improvement in Attack Success Rate (ASR) on LLaMA2-7B-chat and a +50.3% ASR increase on Claude 2, both models known for their extensive safety measures. Additionally, we achieved a 94.97% ASR on GPT-3.5 and 99.4% on Gemma-2B-it, demonstrating the robustness and adaptability of LLMStinger across open and closed-source models.

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