RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code

Abstract

that current LLMs have a limited ability to resist malicious code generation with an average refusal rate of 40.36% in text-to-code scenario and 11.52% in code-to-code scenario. The average refusal rate of all LLMs in RMCBench is only 28.71%; ChatGPT-4 has a refusal rate of only 35.73%. We also analyze the factors that affect LLM's ability to resist malicious code generation and provide implications for developers to enhance model robustness. CCS CONCEPTS• Security and privacy → Software security engineering.