Top 10 OWASP for LLMs: How to Test?

The OWASP top 10 for Large Language Models (LLMs) highlights significant security threats that developers, data scientists, QA, and security experts must address when designing and building applications using LLM technologies. Compiled by an international team of experts, this list includes vulnerabilities like prompt injection, insecure output handling, training data poisoning, and excessive agency, among others. Each threat poses unique risks, such as unauthorized access, data leaks, and biased outputs. To mitigate these risks, strategies like strict input validation, robust access controls, data sanitization, and continuous monitoring are recommended. Examples include attacks that exploit model weaknesses or misuse plugins, demonstrating the potential for significant ethical, privacy, and security challenges. Utilizing tools like testRigor for automated testing can help identify vulnerabilities and ensure that LLM applications maintain high standards of security and reliability. As LLMs become more integrated into various systems, ongoing vigilance and adherence to best practices are essential for safeguarding both the integrity of outputs and the privacy of sensitive data.