The Case for Secure LLMs: Breaking Them to Build Trust

LLMs have a unique attack surface—the language itself—making them vulnerable to prompt injections, jailbreaks, and model poisoning that can bypass safeguards, leak data, or cause unintended actions. With countless pre-built models in circulation, manual inspection is unrealistic, making independent and objective testing essential. Drawing from application security practices, SAST can restrict risky capabilities like code execution or network calls, while DAST can simulate real-world threats such as prompt injections, data leaks, and harmful content. Automated tools play a critical role in uncovering vulnerabilities across an ever-evolving threat landscape. Mitigation calls for regular red teaming, sandboxed deployments, AI gateways for real-time prompt inspection, and continuous monitoring for emerging threats. In AI, I believe testing isn’t optional—to build trustworthy models, you must first learn how to break them. How is your team approaching LLM security, and what strategies have worked for you?