When AI safety constrains defenders more than attackers

Enterprise AI systems deployed for security work are heavily restricted by safety guardrails (automated filters designed to prevent harmful outputs), while attackers freely use jailbroken models (AI systems with safety measures bypassed), open-source alternatives, and purpose-built malicious tools. This creates an asymmetry where defenders face routine refusals when requesting legitimate defensive content like phishing simulations or proof-of-concept code, while attackers can easily circumvent safety measures through prompt injection (tricking AI by hiding instructions in its input) and other well-documented techniques, giving them a significant operational advantage.