Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917744391225344 |
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| author | Crumrine, Garrett Alsmadi, Izzat Guerrero, Jesus Munian, Yuvaraj |
| author_facet | Crumrine, Garrett Alsmadi, Izzat Guerrero, Jesus Munian, Yuvaraj |
| contents | Large language models (LLMs) have revolutionized how we interact with machines. However, this technological advancement has been paralleled by the emergence of "Mallas," malicious services operating underground that exploit LLMs for nefarious purposes. Such services create malware, phishing attacks, and deceptive websites, escalating the cyber security threats landscape. This paper delves into the proliferation of Mallas by examining the use of various pre-trained language models and their efficiency and vulnerabilities when misused. Building on a dataset from the Common Vulnerabilities and Exposures (CVE) program, it explores fine-tuning methodologies to generate code and explanatory text related to identified vulnerabilities. This research aims to shed light on the operational strategies and exploitation techniques of Mallas, leading to the development of more secure and trustworthy AI applications. The paper concludes by emphasizing the need for further research, enhanced safeguards, and ethical guidelines to mitigate the risks associated with the malicious application of LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models Crumrine, Garrett Alsmadi, Izzat Guerrero, Jesus Munian, Yuvaraj Computation and Language Cryptography and Security Computers and Society Machine Learning B.8.0; I.2.7; I.2.8; I.2.11; J.0; K.4.2; K.4.1 Large language models (LLMs) have revolutionized how we interact with machines. However, this technological advancement has been paralleled by the emergence of "Mallas," malicious services operating underground that exploit LLMs for nefarious purposes. Such services create malware, phishing attacks, and deceptive websites, escalating the cyber security threats landscape. This paper delves into the proliferation of Mallas by examining the use of various pre-trained language models and their efficiency and vulnerabilities when misused. Building on a dataset from the Common Vulnerabilities and Exposures (CVE) program, it explores fine-tuning methodologies to generate code and explanatory text related to identified vulnerabilities. This research aims to shed light on the operational strategies and exploitation techniques of Mallas, leading to the development of more secure and trustworthy AI applications. The paper concludes by emphasizing the need for further research, enhanced safeguards, and ethical guidelines to mitigate the risks associated with the malicious application of LLMs. |
| title | Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models |
| topic | Computation and Language Cryptography and Security Computers and Society Machine Learning B.8.0; I.2.7; I.2.8; I.2.11; J.0; K.4.2; K.4.1 |
| url | https://arxiv.org/abs/2406.00628 |