5 AI Security Vulnerabilities Most Developers Miss

You patched your app, but did you patch your AI? Every week, another company makes headlines for an AI-related security incident — leaked training data, jailbroken chatbots, or models manipulated into producing harmful output. The uncomfortable truth: most of these incidents exploited vulnerabilities that are well-documented but rarely checked for. Why AI vulnerabilities slip through the cracks Developers are trained to think about application security. But AI components introduce a fundamentally different threat model. There's no static code to analyze — the behavior emerges from weights and training data. Traditional scanners don't catch these issues, and most security teams lack AI-specific expertise. Here are five vulnerabilities that consistently fly under the radar. 1. System prompt exposure If your application uses an LLM with a system prompt, assume someone will try to extract it. Techniques range from simple ("What are your instructions?") to sophisticated (asking the model to