Summary of Stochastic Monkeys at Play: Random Augmentations Cheaply Break Llm Safety Alignment, by Jason Vega et al.
Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignment
by Jason Vega, Junsheng Huang, Gaokai Zhang, Hangoo Kang, Minjia Zhang, Gagandeep Singh
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the effectiveness of simple random augmentations to input prompts in bypassing safety alignment in Large Language Models (LLMs) such as Llama 3 and Qwen 2. The authors study how different models respond to various augmentation types, model sizes, quantization, fine-tuning-based defenses, and decoding strategies, including sampling temperature. They find that low-resource attackers can significantly improve their chances of bypassing alignment with just 25 random augmentations per prompt. This demonstrates the potential for unsophisticated attackers, or “stochastic monkeys,” to successfully evade safety alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models can be tricked into doing things they weren’t designed to do. The researchers want to see if it’s easy for someone with limited resources and skills (like a “monkey” trying to use the model) to get around the rules that keep the model safe. They tested different ways of tweaking the input prompts and found that even simple changes can make it harder for safety controls to work. This means that malicious users might not need advanced knowledge or powerful computers to evade safety measures. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Llama » Prompt » Quantization » Temperature
