Large Language Models (LLMs) are integral to various business operations, enhancing customer service, research, and internal communications. However, their increasing role in decision-making raises significant security concerns, as vulnerabilities may lead to data breaches and operational disruptions. This is where LLM pentesting becomes essential. Unlike traditional security testing, which focuses on predictable outputs, LLM pentesting evaluates how AI models respond to malicious inputs, handle sensitive information, and interact with various systems.
Key risks include prompt injection, data leakage, and training data poisoning, which can compromise model integrity. A structured yet flexible approach to LLM pentesting involves scoping, behavior mapping, and simulating attacks, while evaluating security controls and impact analysis. Maintaining effective security involves not only pentesting but also implementing practices like input validation and output sanitization. As the threat landscape evolves, ongoing evaluation is crucial for safeguarding data and ensuring user trust, integrating LLM security into the DevSecOps lifecycle.
