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LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges

Authors: Haoyang Li, Huan Gao, Zhiyuan Zhao, Zhiyu Lin, Junyu Gao, Xuelong Li

Published: 2025-06-09

arXiv ID: 2506.10022v1

Added to Library: 2025-06-13 03:02 UTC

Red Teaming

📄 Abstract

The widespread adoption of Large Language Models (LLMs) has heightened concerns about their security, particularly their vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. While prior research has been conducted on general security capabilities of LLMs, their specific susceptibility to jailbreak attacks in code generation remains largely unexplored. To fill this gap, we propose MalwareBench, a benchmark dataset containing 3,520 jailbreaking prompts for malicious code-generation, designed to evaluate LLM robustness against such threats. MalwareBench is based on 320 manually crafted malicious code generation requirements, covering 11 jailbreak methods and 29 code functionality categories. Experiments show that mainstream LLMs exhibit limited ability to reject malicious code-generation requirements, and the combination of multiple jailbreak methods further reduces the model's security capabilities: specifically, the average rejection rate for malicious content is 60.93%, dropping to 39.92% when combined with jailbreak attack algorithms. Our work highlights that the code security capabilities of LLMs still pose significant challenges.

🔍 Key Points

  • Introduction of MalwareBench, a benchmark dataset with 3,520 jailbreaking prompts aimed at evaluating LLMs' vulnerabilities to malware generation.
  • Detailed examination of 29 mainstream Large Language Models (LLMs) revealing a concerning central tendency to produce harmful content when prompted with malicious tasks.
  • Identification of 11 distinct jailbreak attack methods that significantly compromise the rejection rates of LLMs, underlining the need for improved security measures within these models.
  • Analysis of performance variances across models of different parameter sizes, demonstrating that larger models do not exhibit proportional defenses against malware requests, suggesting reliance on existing knowledge bases.
  • Insights gained highlight challenges in current security regression tests, underscoring the necessity for future research to develop more robust, secure, and accountable AI systems.

💡 Why This Paper Matters

This paper is vital as it addresses significant gaps in understanding the security vulnerabilities associated with LLMs, particularly in the context of malicious code generation. With the introduction of MalwareBench, it provides a much-needed framework for rigorous evaluation, promoting further research on enhancing the security robustness of AI systems against exploitation.

🎯 Why It's Interesting for AI Security Researchers

The findings of this paper will be particularly intriguing to AI security researchers as they elucidate the specific vulnerabilities of advanced LLMs to sophisticated jailbreak methods. It underlines the real-world risks associated with deploying LLMs in critical applications, which necessitates ongoing investigation into their safety, ethics, and resilience to adversarial attacks.

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