Jie Lin (University of Central Florida), David Mohaisen (University of Central Florida)

Large Language Models (LLMs) have demonstrated strong potential in tasks such as code understanding and generation. This study evaluates several advanced LLMs—such as LLaMA-2, CodeLLaMA, LLaMA-3, Mistral, Mixtral, Gemma, CodeGemma, Phi-2, Phi-3, and GPT-4—for vulnerability detection, primarily in Java, with additional tests in C/C++ to assess generalization. We transition from basic positive sample detection to a more challenging task involving both positive and negative samples and evaluate the LLMs’ ability to identify specific vulnerability types. Performance is analyzed using runtime and detection accuracy in zero-shot and few-shot settings with custom and generic metrics. Key insights include the strong performance of models like Gemma and LLaMA-2 in identifying vulnerabilities, though this success varies, with some configurations performing no better than random guessing. Performance also fluctuates significantly across programming languages and learning modes (zero- vs. few-shot). We further investigate the impact of model parameters, quantization methods, context window (CW) sizes, and architectural choices on vulnerability detection. While CW consistently enhances performance, benefits from other parameters, such as quantization, are more limited. Overall, our findings underscore the potential of LLMs in automated vulnerability detection, the complex interplay of model parameters, and the current limitations in varied scenarios and configurations.

View More Papers

MOBIDOJO: A Virtual Security Combat Platform for 5G Cellular...

Hyunwoo Lee (Ohio State University), Haohuang Wen (Ohio State University), Phillip Porras (SRI), Vinod Yegneswaran (SRI), Ashish Gehani (SRI), Prakhar Sharma (SRI), Zhiqiang Lin (Ohio State University)

Read More

CASPR: Context-Aware Security Policy Recommendation

Lifang Xiao (Institute of Information Engineering, Chinese Academy of Sciences), Hanyu Wang (Institute of Information Engineering, Chinese Academy of Sciences), Aimin Yu (Institute of Information Engineering, Chinese Academy of Sciences), Lixin Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Dan Meng (Institute of Information Engineering, Chinese Academy of Sciences)

Read More

What’s Done Is Not What’s Claimed: Detecting and Interpreting...

Chang Yue (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Zhixiu Guo (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Jun Dai, Xiaoyan Sun (Department of Computer Science, Worcester Polytechnic Institute), Yi Yang (Institute of Information Engineering, Chinese Academy…

Read More