Summary of Self-evaluation As a Defense Against Adversarial Attacks on Llms, by Hannah Brown et al.
Self-Evaluation as a Defense Against Adversarial Attacks on LLMs
by Hannah Brown, Leon Lin, Kenji Kawaguchi, Michael Shieh
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 research introduces a novel defense mechanism against adversarial attacks on Large Language Models (LLMs), utilizing self-evaluation. The proposed method does not require fine-tuning of the models, instead leveraging pre-trained LLMs to evaluate inputs and outputs of generator models. This approach significantly reduces implementation costs compared to finetuning-based methods. The study demonstrates that this defense can effectively reduce attack success rates on both open-source and closed-source LLMs, surpassing the reductions achieved by Llama-Guard2 and common content moderation APIs. The analysis also explores the resilience of the proposed method against various attacks, showcasing its robustness. Furthermore, the authors will provide code and data at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research protects language models from bad actors using a special way to check what they’re doing. The approach doesn’t need to change the model itself; instead, it uses existing models to look at what’s going in and coming out. This makes it cheaper to use than other methods that require changing the model. The study shows that this defense can stop many attacks on language models, including those on popular platforms. It also tests how well the defense works against different types of attacks and finds that it’s very good at keeping them out. |
Keywords
» Artificial intelligence » Fine tuning » Llama
