Leveraging Large Language Models for Command Injection Vulnerability Analysis in Python: An Empirical Study on Popular Open-Source Projects (2505.15088v1)

Abstract: Command injection vulnerabilities are a significant security threat in dynamic languages like Python, particularly in widely used open-source projects where security issues can have extensive impact. With the proven effectiveness of LLMs(LLMs) in code-related tasks, such as testing, researchers have explored their potential for vulnerabilities analysis. This study evaluates the potential of LLMs, such as GPT-4, as an alternative approach for automated testing for vulnerability detection. In particular, LLMs have demonstrated advanced contextual understanding and adaptability, making them promising candidates for identifying nuanced security vulnerabilities within code. To evaluate this potential, we applied LLM-based analysis to six high-profile GitHub projects-Django, Flask, TensorFlow, Scikit-learn, PyTorch, and Langchain-each with over 50,000 stars and extensive adoption across software development and academic research. Our analysis assesses both the strengths and limitations of LLMs in detecting command injection vulnerabilities, evaluating factors such as detection accuracy, efficiency, and practical integration into development workflows. In addition, we provide a comparative analysis of different LLM tools to identify those most suitable for security applications. Our findings offer guidance for developers and security researchers on leveraging LLMs as innovative and automated approaches to enhance software security.

Leveraging LLMs for Command Injection Vulnerability Analysis in Python

The paper presents an empirical paper exploring the application of LLMs for detecting command injection vulnerabilities in Python code within high-profile open-source projects. The focus is on the potential role of LLMs as an automated alternative to traditional security tools like Bandit, analyzing their efficacy, limitations, and cost-effectiveness in vulnerability detection.

Overview

Command injection vulnerabilities pose significant security risks, allowing unauthorized command execution, leading to potential data breaches and system compromise. This paper evaluates LLMs, such as GPT-4, GPT-4o, Claude 3.5 Sonnet, and DeepSeek-R1, for their ability to detect these vulnerabilities in Python projects like Django, Flask, and PyTorch, which are crucial in AI and software development.

The methodology involves several stages: extracting Python files from six popular projects, identifying functions with potential vulnerabilities using known methods prone to command injection, and employing LLMs for vulnerability detection and security test generation. The LLMs' role extends beyond detection, providing security tests that assess the presence of vulnerabilities in the code, enhancing the overall integrity of the analysis.

Key Findings and Numerical Results

The analysis reveals that among 190 candidate functions tested, various models demonstrate different capabilities. GPT-4 showed a promising balance, achieving an accuracy of 75.5%, with precision and F1 scores of 68.4% and 74.5%, respectively. In comparison, other models like DeepSeek-R1, while effective in test generation, showed variability in detection metrics, indicating the distinctive strengths of each LLM.

The paper also identifies specific limitations in LLM-based approaches, such as difficulties in detecting vulnerabilities when parameterized with list-type inputs. These insights are crucial for understanding the nuanced detection capabilities and guiding future improvements.

Comparative Analysis

Comparative analysis with Bandit, a traditional static analysis tool, underscores the advantages of LLMs. While Bandit achieved high recall rates, it suffered from significant false positives, affecting overall accuracy and precision. GPT-4, with better contextual understanding and adaptability, offered a more balanced detection with fewer false positives.

Implications and Future Directions

The findings suggest that LLMs, particularly GPT-4, provide a viable alternative or complement to static analysis tools for command injection vulnerability detection. The results demonstrate the potential for LLMs to analyze fragmented and non-compilable code, offering new dimensions in automated security analysis through test generation.

Theoretical implications highlight the need for refining LLM-based methods to address overlooked vulnerabilities, paving the way for hybrid models that integrate LLMs with traditional approaches. Practically, the research encourages developers and security researchers to incorporate LLMs into their security workflows to enhance detection accuracy and efficiency.

Future research can expand the dataset scope and explore fine-tuning LLMs for refined vulnerability analysis. Additionally, integrating more diverse LLM architectures could further enhance the robustness and applicability of this approach across various domains, enhancing security best practices in the software development landscape.