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The OWASP LLM Top 10 names ten specific risks in LLM-powered apps: prompt injection, data leakage, excessive agency, and seven more, each with its own attack pattern. Testing against these risks means chaining a planted instruction through to real data exposure or confirming that rate limits cap costs, rather than just documenting that they should. …

LLM pentesting needs to cover more than the model's text output. A proper test scopes the model, its plugins, and its data sources. Then, it works through known risk categories, including prompt injection, data leakage, and tool abuse. Shipping an AI feature moves faster than most security processes were built to handle. A chatbot goes …