Summary of Human-readable Adversarial Prompts: An Investigation Into Llm Vulnerabilities Using Situational Context, by Nilanjana Das et al.
Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
by Nilanjana Das, Edward Raff, Manas Gaur
First submitted to arxiv on: 20 Dec 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 The abstract presents research on strengthening adversarial attacks against large language models (LLMs) by developing more realistic and potent threats using human-readable prompts. The authors propose three key contributions: situation-driven attacks leveraging movie scripts, adversarial suffix conversion, and AdvPrompter with p-nucleus sampling to generate diverse, human-readable adversarial suffixes. These advancements improve attack efficacy in models like GPT-3.5 and Gemma 7B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to deceive large language models by creating more realistic attacks using movie scripts and other human-readable prompts. The researchers develop new methods to make these attacks more effective, including converting nonsensical adversarial suffixes into meaningful text. This work could help improve the security of AI systems like language translation tools. |
Keywords
» Artificial intelligence » Gpt » Translation
