AI jailbreaking is adversarial prompting used to bypass a model’s safety rules, policy filters, or refusal behavior. The goal is not to improve the answer, but to push the system past boundaries the developer intended. In security terms, it is a trust-boundary attack: the attacker tries to make untrusted text override trusted instructions.
This matters because modern AI systems often read documents, web pages, search results, or connector data, and may also trigger tools or actions. A malicious prompt hidden in that content can cause the model to reveal restricted information, ignore guardrails, or perform unsafe actions. Defenses focus on least privilege, separating instructions from retrieved content, sandboxing risky actions, requiring confirmation for important steps, and testing with adversarial prompts. AI jailbreaking is therefore less a clever trick than a control problem in connected systems.