Summary of Llmstinger: Jailbreaking Llms Using Rl Fine-tuned Llms, by Piyush Jha et al.
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
by Piyush Jha, Arnav Arora, Vijay Ganesh
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel approach, LLMStinger, leverages Large Language Models to generate adversarial suffixes for jailbreak attacks. Unlike traditional methods requiring complex prompt engineering or white-box access, LLMStinger uses a reinforcement learning loop to fine-tune an attacker LLM. This method outperforms existing red-teaming approaches, achieving significant improvements in Attack Success Rate (ASR) on various models, including LLaMA2-7B-chat, Claude 2, GPT-3.5, and Gemma-2B-it. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMStinger is a new way to make computer systems vulnerable by using language models to create tricks for hackers. It’s better than other methods because it doesn’t need special access or complex setup. The system works well on many different models, making it a powerful tool for finding weaknesses. |
Keywords
» Artificial intelligence » Claude » Gpt » Prompt » Reinforcement learning
