by: Satu Korhonen and Silvan Gebhardt
AI systems fail in unpredictable ways, from suggesting insecure code to leaking sensitive data. Learn why traditional security testing isn’t enough and why adversarial testing is essential to understanding and mitigating the real risks of generative AI.
AI Systems Fail Differently
Consider this: a large language model greenlights a malicious URL because it looks like a familiar domain. A coding assistant suggests a firewall rule that exposes the wrong port, not from a bug, but because it misunderstood your intent. Another model recommends uploading sensitive logs to Pastebin, while a fourth suggests hardcoding access credentials directly into a Git repository.
These aren’t edge cases; they are real events we’ve seen in the field. What makes these AI systems so dangerous is their ability to be confidently and convincingly wrong. This isn’t a simple usability flaw—it’s a security risk with consequences that scale with the AI’s role. A flawed coding assistant creates vulnerabilities. A flawed AI companion can have devastating, real-world impacts, including a documented case that contributed to a teenager’s suicide.
Yet many teams skip the step designed to catch these failures: adversarial testing.
What Adversarial Testing Is (and What It Isn’t)
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