Summary of Jailbreak Instruction-tuned Llms Via End-of-sentence Mlp Re-weighting, by Yifan Luo et al.
Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting
by Yifan Luo, Zhennan Zhou, Meitan Wang, Bin Dong
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 delves into the safety mechanisms of large language models (LLMs) that are fine-tuned for specific instructions. Researchers find that re-weighting neural network layers in these models can compromise their safety, particularly when making end-of-sentence predictions. The study hypothesizes that LLMs assess the harmfulness of prompts during this process and identifies the MLP layer’s critical role. Two novel white-box jailbreak methods are developed to exploit vulnerabilities: a prompt-specific method that optimizes attacks in real-time and a prompt-general method that generalizes to unseen harmful prompts. These methods demonstrate robust performance across 7 popular open-source LLMs, ranging from 2B to 72B parameters. The study provides insights into the safety vulnerabilities of instruction-tuned LLMs and deepens our understanding of their internal mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that language models are safe and don’t do bad things when we give them instructions. Researchers found that if they change how certain parts of the model work, it can be tricked into doing things it shouldn’t. They think this happens because the model is trying to figure out what kind of prompts (or cues) might cause problems. To test this idea, they developed two new ways to make the model do bad things: one that focuses on specific prompts and another that can work with many different prompts. These methods worked well on 7 popular language models. The study helps us understand how these models work and where we need to be careful. |
Keywords
» Artificial intelligence » Neural network » Prompt
