Summary of Defending Jailbreak Prompts Via In-context Adversarial Game, by Yujun Zhou et al.
Defending Jailbreak Prompts via In-Context Adversarial Game
by Yujun Zhou, Yufei Han, Haomin Zhuang, Kehan Guo, Zhenwen Liang, Hongyan Bao, Xiangliang Zhang
First submitted to arxiv on: 20 Feb 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 A newly proposed framework called In-Context Adversarial Game (ICAG) addresses the security concerns of Large Language Models (LLMs) by introducing an adversarial game-based approach that dynamically extends knowledge to defend against jailbreak attacks without requiring fine-tuning. ICAG leverages agent learning to iteratively improve both defense and attack agents, ultimately strengthening defenses against newly generated prompts. Empirical studies demonstrate the effectiveness of ICAG in reducing jailbreak success rates across various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a special game that helps keep Large Language Models (LLMs) safe from hackers. This game is called In-Context Adversarial Game or ICAG for short. It’s like a training program that teaches LLMs how to defend themselves against bad guys trying to break in. The cool thing about ICAG is that it gets better and better over time, making it harder for hackers to succeed. Scientists tested ICAG and found that it really works – LLMs protected by ICAG were much safer than those without it. |
Keywords
* Artificial intelligence * Fine tuning
