AI Red Teaming
Red teaming an AI system means deliberately attacking it with adversarial inputs before real attackers do.
Prerequisites
Overview
Because jailbreak and injection techniques evolve constantly, a system that was secure at launch can develop gaps over time. Red teaming makes probing for those gaps a deliberate, ongoing practice rather than something discovered from a real incident.
Where It Fits
Adversarial Test Attempts
System Under Test
Findings
Fixes Applied
Retested with the next round of red teaming.
Key Points
- Ongoing, not one-time
- Red teaming needs to repeat as the system, its data, and attack techniques all change over time.
- Covers the whole pipeline
- Effective red teaming probes prompt injection, retrieval, tool use, and output — not just direct jailbreak attempts against the base model.
- Structured findings
- Results feed back into concrete fixes and regression tests, not just a report that gets filed away.
Interview Question
Why does red teaming need to be an ongoing practice rather than a one-time pre-launch exercise?
Jailbreak and injection techniques evolve continuously, and the system itself changes — new tools, new data sources, model updates — each of which can reopen a gap that was previously closed. Treating red teaming as a one-time gate before launch misses everything that changes afterward.
Explain It in 30 Seconds
AI red teaming deliberately probes a system with adversarial inputs across its whole pipeline — not just the base model — as an ongoing practice, since new tools, data sources, and evolving attack techniques can reopen previously-closed gaps.