AI systems are becoming part of everyday life in business, healthcare, finance, and many other areas. As these systems handle more important tasks, the security risks they face grow larger. AI red teaming tools help organizations test their AI systems by simulating attacks and finding weaknesses before real threats can exploit them. These tools work by challenging AI models in different ways to see how they respond under pressure.
This happened in the early 1990s, when I was a young engineer starting an internship at one of the companies that helped create the smart card industry. I believed my card was secure. I believed the system worked. But watching strangers casually extract something that was supposed to be secret and protected was a shock. It was also the moment I realized how insecure security actually is, and the devastating impact security breaches could have on individuals, global enterprises, and governments.
The problem in brief: LLM training produces a black box that can only be tested through prompts and output token analysis. If trained to switch from good to evil by a particular prompt, there is no way to tell without knowing that prompt. Other similar problems happen when an LLM learns to recognize a test regime and optimizes for that, rather than the real task it's intended for - Volkswagening - or if it just decides to be deceptive.
