Summary of Spin: Self-supervised Prompt Injection, by Leon Zhou et al.
SPIN: Self-Supervised Prompt INjection
by Leon Zhou, Junfeng Yang, Chengzhi Mao
First submitted to arxiv on: 17 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 A novel approach to safeguarding Large Language Models (LLMs) from adversarial attacks is proposed in this paper. The Self-supervised Prompt INjection (SPIN) system detects and reverses various attacks, ensuring the model produces safe and reliable responses. This defense mechanism operates at inference-time, maintaining compatibility with existing alignment methods. Benchmark results demonstrate a significant reduction in attack success rates of up to 87.9%, while preserving performance on benign requests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very smart computers that can understand and generate human-like text. But some bad people might try to trick these models into saying things they shouldn’t say. This paper introduces a new way to keep LLMs safe from these attacks. It’s called Self-supervised Prompt INjection, or SPIN for short. SPIN can detect when someone is trying to hack the model and make it do something bad. It does this by looking at what the model is saying and making sure it’s okay. The good news is that SPIN works really well – it can stop up to 87.9% of attacks! And the best part is that it doesn’t slow down the model or affect how it performs when asked to do something normal. |
Keywords
» Artificial intelligence » Alignment » Inference » Prompt » Self supervised
