Summary of Transforming Computer Security and Public Trust Through the Exploration Of Fine-tuning Large Language Models, by Garrett Crumrine et al.
Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models
by Garrett Crumrine, Izzat Alsmadi, Jesus Guerrero, Yuvaraj Munian
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 paper investigates the misuse of large language models (LLMs) in creating malicious services, dubbed “Mallas,” which perpetuate cyber security threats. The researchers analyze pre-trained LLMs’ vulnerabilities and efficiency when used for nefarious purposes. They leverage a dataset from the Common Vulnerabilities and Exposures program to explore fine-tuning methods generating code and explanatory text related to identified vulnerabilities. This study aims to shed light on Mallas’ operational strategies, exploitation techniques, and the need for more secure AI applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how bad people are using big language models (LLMs) to make fake websites, send scams, and spread malware. It’s like a game where they try to find ways to use LLMs in clever or sneaky ways. The researchers took data from a place that keeps track of security problems and used it to see how well LLMs work when they’re used for bad things. They want people to know what’s going on so we can make the internet safer. |
Keywords
» Artificial intelligence » Fine tuning
