AI Auditing: The OWASP LLM Top 10 Checklist

WHY IT EXISTS Large Language Models introduce new, non-obvious attack surfaces beyond traditional software. Threats like prompt injection or training data poisoning are not the same as SQL injection or XSS. AI auditing exists to systematically identify and mitigate these unique risks before they lead to data breaches, system failures, or reputational damage.

THE MENTAL MODEL An AI audit is like a pre-flight checklist for an airplane. A pilot doesn't just check the engine; they inspect the wings, fuel, controls, and communication systems. Similarly, an AI audit examines the entire LLM-powered system: the model, its training data, its inputs (prompts), its outputs, any connected plugins, and the humans who rely on it. It's a holistic security assessment, not just a model performance test.

HOW IT WORKS Auditors use a structured framework, most commonly the OWASP Top 10 for LLM Applications, as a guide. They systematically test for each category of vulnerability. For example, to check for Prompt Injection (LLM01), they craft malicious inputs to see if they can bypass system instructions. For Insecure Output Handling (LLM02), they check if the application properly sanitizes the LLM's responses before using them in downstream systems. The process involves a mix of automated scanning and manual, adversarial testing to uncover weaknesses.

WHEN TO USE IT Conduct an AI audit before deploying any public-facing LLM application, after any major model or application update, and periodically as part of a regular security review cycle. It's especially critical for systems where the LLM has agency to perform actions, handles sensitive data, or provides information that influences important decisions.

WHEN NOT TO USE IT A full-scale audit might be overkill for internal, non-critical experiments with no connection to production systems or sensitive data. However, even in these cases, a lightweight review based on the OWASP principles is a good practice to build security habits. The core principles are never irrelevant, but the formality of the audit can be scaled.

ONE CANONICAL EXAMPLE A company builds a customer service chatbot with a plugin that accesses order information. An audit using the OWASP Top 10 would test for several risks. First, Prompt Injection (LLM01): can a user craft a prompt to make the bot reveal another user's order details? Second, Insecure Plugin Design (LLM07): does the plugin have overly broad permissions, allowing it to modify orders instead of just reading them? Third, Sensitive Information Disclosure (LLM06): does the model inadvertently leak PII in its responses? The audit provides specific fixes for these flaws before launch.