Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models
- URL: http://arxiv.org/abs/2406.00628v1
- Date: Sun, 2 Jun 2024 06:10:31 GMT
- Title: Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models
- Authors: Garrett Crumrine, Izzat Alsmadi, Jesus Guerrero, Yuvaraj Munian,
- Abstract summary: "Mallas" are malicious services that exploit large language models (LLMs) for nefarious purposes.
This paper delves into the proliferation of Mallas by examining the use of various pre-trained language models and their efficiency and vulnerabilities.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval [20.959848710829878]
Large language models (LLMs) have brought significant advancements to code generation and code repair.
However, their training using unsanitized data from open-source repositories, like GitHub, raises the risk of inadvertently propagating security vulnerabilities.
We aim to present a comprehensive study aimed at precisely evaluating and enhancing the security aspects of code LLMs.
arXiv Detail & Related papers (2024-07-02T16:13:21Z) - Threat Modelling and Risk Analysis for Large Language Model (LLM)-Powered Applications [0.0]
Large Language Models (LLMs) have revolutionized various applications by providing advanced natural language processing capabilities.
This paper explores the threat modeling and risk analysis specifically tailored for LLM-powered applications.
arXiv Detail & Related papers (2024-06-16T16:43:58Z) - Generative AI and Large Language Models for Cyber Security: All Insights You Need [0.06597195879147556]
This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs)
We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection.
We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA.
arXiv Detail & Related papers (2024-05-21T13:02:27Z) - Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress.
Our investigation exposes a critical oversight in this belief.
By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
arXiv Detail & Related papers (2024-04-16T13:22:54Z) - CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs.
Our studies reveal a new and universal safety vulnerability of these models against code input.
We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
arXiv Detail & Related papers (2024-03-12T17:55:38Z) - The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative [55.08395463562242]
Multimodal Large Language Models (MLLMs) are constantly defining the new boundary of Artificial General Intelligence (AGI)
Our paper explores a novel vulnerability in MLLM societies - the indirect propagation of malicious content.
arXiv Detail & Related papers (2024-02-20T23:08:21Z) - Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and
Vulnerabilities [14.684194175806203]
Large language models (LLMs) can be misused for fraud, impersonation, and the generation of malware.
We present a taxonomy describing the relationship between threats caused by the generative capabilities of LLMs, prevention measures intended to address such threats, and vulnerabilities arising from imperfect prevention measures.
arXiv Detail & Related papers (2023-08-24T14:45:50Z) - RatGPT: Turning online LLMs into Proxies for Malware Attacks [0.0]
We present a proof-of-concept where ChatGPT is used for the dissemination of malicious software while evading detection.
We also present the general approach as well as essential elements in order to stay undetected and make the attack a success.
arXiv Detail & Related papers (2023-08-17T20:54:39Z) - Not what you've signed up for: Compromising Real-World LLM-Integrated
Applications with Indirect Prompt Injection [64.67495502772866]
Large Language Models (LLMs) are increasingly being integrated into various applications.
We show how attackers can override original instructions and employed controls using Prompt Injection attacks.
We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
arXiv Detail & Related papers (2023-02-23T17:14:38Z) - CodeLMSec Benchmark: Systematically Evaluating and Finding Security
Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks.
Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities.
This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
arXiv Detail & Related papers (2023-02-08T11:54:07Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.