Hunting vulnerabilities in LLM-integrated frameworks and applications - for fun and profit

Large Language Models (LLMs) have exhibited immense potential in various computational tasks, including code generation, data analysis, and program repair. The development of LLM-integrated applications has surged in recent years, mirroring the rise of traditional web applications. While frameworks have emerged to streamline LLM-integrated application development, it's essential to acknowledge that certain APIs within these frameworks, particularly those enabling code execution, may be susceptible to security threats like remote code execution and SQL injection. Left unaddressed, these vulnerabilities can result in severe consequences, including the compromise of sensitive assets like OpenAI API keys.

This project aims to construct a comprehensive system for detecting vulnerabilities in LLM-integrated frameworks. Students will engage in the development of cutting-edge vulnerability detection tools by leveraging diverse program analysis techniques. These tools will be designed to identify vulnerabilities within LLM-integrated frameworks, providing a proactive approach to security. Detected vulnerabilities can be eligible for bug bounties on platforms such as (https://huntr.dev/). This incentive-driven approach encourages students to hone their skills while addressing real-world security challenges.