Part 1 explained how we have to bound behavior instead of asserting exact outputs. This post maps where to place those boundaries. AI systems expose attack surfaces at three runtime checkpoints (i.e., input, processing and output) and the checks differ by system type (classical ML, LLM-based, or hybrid).
This is the first of five posts on testing AI systems securely. If you've shipped or evaluated AI in production, you've probably felt it: the test suite passes, coverage looks good, and something still nags. *What are we actually validating?* That gap is what this series addresses.
Introduction As large language models become increasingly embedded in production applications, from customer service chatbots to code assistants and document analysis tools, the security implications of these systems have moved from theoretical concern to practical necessity. Unlike traditional software security testing, LLM red teaming addresses unique challenges: prompt injection attacks, data leakage through carefully crafted … Continue reading An introduction to automated LLM red teaming
