Summary of Can Llms Be Fooled? Investigating Vulnerabilities in Llms, by Sara Abdali et al.
Can LLMs be Fooled? Investigating Vulnerabilities in LLMs
by Sara Abdali, Jia He, CJ Barberan, Richard Anarfi
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The abstract discusses the limitations and vulnerabilities of Large Language Models (LLMs) in Natural Language Processing (NLP). Despite their impressive capabilities, LLMs can leak personal patient data when prompted surreptitiously. The study identifies multiple vulnerabilities: model-based, training-time, and inference-time issues, and proposes mitigation strategies such as “Model Editing” and “Chroma Teaming”. This paper synthesizes findings from each vulnerability section and suggests new research directions to develop more robust and secure LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are powerful tools in NLP, but they have some big flaws. For example, a medical summary-making AI could accidentally reveal patient information if asked the right way. Researchers are trying to understand why these issues happen so we can make better AIs that are more secure and reliable. This study looks at different kinds of problems and offers ways to fix them. |
Keywords
» Artificial intelligence » Inference » Natural language processing » Nlp
