Summary of Human-interpretable Adversarial Prompt Attack on Large Language Models with Situational Context, by Nilanjana Das et al.
Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context
by Nilanjana Das, Edward Raff, Manas Gaur
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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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 new study explores the vulnerabilities in Large Language Models (LLMs) using adversarial attacks with innocuous human-understandable malicious prompts. Researchers convert nonsensical suffix attacks into sensible prompts via situation-driven contextual rewriting, allowing them to better understand possible risks. The approach uses an independent adversarial insertion and situations derived from movies to trick LLMs, demonstrating successful attacks on both open-source and proprietary models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of scientists are trying to figure out if big language computers can be tricked into giving wrong answers. They take a special kind of attack that is usually used against these computers and make it more understandable by using movie scenes. They tested this new way of attacking the computers on different types, both free and not free, and found that just one attempt was often enough to get the computer to give a bad answer. |
