Safety constraints are rules that limit what a model may say or do when an output could create unacceptable risk. In cyber security, they are used to stop an AI system from giving exploit instructions, helping with credential theft, or carrying out actions that cross from defensive analysis into abuse. They can also restrict tool use, reduce permissions, and require human approval before sensitive steps are taken.
In practice, safety constraints appear as policy filters, refusal behavior, approval gates, and access controls around model outputs. Defenders use them to keep AI useful for tasks like testing and analysis without enabling operational attack capability. Attackers may try to bypass them with prompt injection or jailbreak techniques, so strong systems layer constraints with monitoring, logging, and least-privilege design.