Summary of Coercing Llms to Do and Reveal (almost) Anything, by Jonas Geiping et al.
Coercing LLMs to do and reveal (almost) anything
by Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, Tom Goldstein
First submitted to arxiv on: 21 Feb 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 A newly discovered vulnerability in large language models (LLMs) allows attackers to manipulate the models into producing harmful content. This paper expands on previous findings by exploring a broader range of potential attacks on LLMs, including misdirection, model control, denial-of-service, and data extraction. The authors provide concrete examples and categorize these attacks to better understand their capabilities and potential impact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are smart AI systems that can generate text. But some bad people have found a way to trick them into saying mean things. This paper talks about all the different ways that someone could use these models in an unfair way, like making them say something silly or taking information from them without permission. The authors want us to understand how this works and what we can do to stop it. |
