Red-Teaming & Adversarial Evaluation
Actively attack the agent before deployment — injection, jailbreak, manipulation, data exfiltration — and remediate what is found.
This page is the complete instruction page for one pattern called "Red-Teaming & Adversarial Evaluation." It explains the problem this pattern solves, the idea behind the solution, when you should (and should not) use it, and what happens afterward — both the good effects and the costs.
This matters because building AI agents is not just about making them clever. It is also about making them safe and predictable. Following a well-tested pattern like this one helps avoid common mistakes, and shows you exactly which safety rules and regulations it connects to, listed under "Standards Mesh" on this page.
Context
Defenders who have not thought like attackers ship systems that attackers find first. Regulators now expect adversarial testing as part of risk management.
Problem
Passive testing misses the vulnerabilities that matter most.
Forces
- Red-team depth vs cost
- Fix latency vs disclosure pressure
Solution
Engage an internal or external red team before launch and periodically thereafter. Scope explicitly includes prompt injection, jailbreak, tool misuse, data exfiltration, policy bypass. Findings tracked in a remediation register with severity and timeline.
Applicability
- High-risk agents under EU AI Act
- Any customer-facing agent
- Any agent with write access
Anti-Patterns
- Treating red-team as optional
- Fixing findings without regression tests
Consequences
- +Uncovered vulnerabilities addressed
- +Confidence for regulators and executives
- −Specialist effort
- −Findings may delay launch