Summary of Imposter.ai: Adversarial Attacks with Hidden Intentions Towards Aligned Large Language Models, by Xiao Liu et al.
Imposter.AI: Adversarial Attacks with Hidden Intentions towards Aligned Large Language Models
by Xiao Liu, Liangzhi Li, Tong Xiang, Fuying Ye, Lu Wei, Wangyue Li, Noa Garcia
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach for extracting harmful information from large language models (LLMs) like ChatGPT is introduced. The study reveals three strategies for decomposing and rewriting malicious questions into seemingly innocent or benign-sounding ones, enabling the extraction of harmful responses. This method outperforms conventional attack methods on GPT-3.5-turbo, GPT-4, and Llama2. The paper highlights the importance of discerning the intent behind a dialogue in the face of these novel attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models like ChatGPT are powerful tools that can be used for many things. But they can also be tricked into saying harmful or bad things if someone knows how to ask them. This study shows how someone could do this by asking clever questions that seem harmless but actually get the model to say something mean. The researchers tested their method on three different models and found it was very good at getting them to say something harmful. Now we need to figure out how to tell if someone is trying to trick us with these attacks. |
Keywords
» Artificial intelligence » Gpt
