AI Purple Teaming Intermediate

Purple teaming bridges the gap between offensive and defensive AI security by having red and blue teams work together in real time. Rather than waiting for a final report, purple teaming creates a continuous feedback loop where attacks are launched, detection is tested, and defenses are improved collaboratively. This approach accelerates security maturity for AI systems.

Purple Team Workflow

  1. Joint Planning

    Red and blue teams together define attack scenarios and expected detection capabilities. This ensures tests are meaningful and measurable.

  2. Controlled Attack Execution

    The red team executes an attack while the blue team monitors in real time. Both teams communicate throughout the exercise.

  3. Detection Assessment

    Was the attack detected? How quickly? What was the quality of the alert? Which monitoring gaps were exposed?

  4. Immediate Improvement

    The blue team creates or refines detection rules based on what they learned. The red team helps validate the new detections work.

  5. Iterate

    Repeat with variations of the attack or move to the next scenario. Each iteration improves both offensive techniques and defensive capabilities.

Purple Team Exercise: LLM Safety Testing

Here is an example purple team exercise for an LLM-powered application:

Phase Red Team Action Blue Team Response Outcome
Round 1 Direct prompt injection attempt Input filter catches known patterns Detected - baseline confirmed
Round 2 Encoded injection (base64) No detection - gap identified Blue team adds encoding detection
Round 3 Multi-turn context escalation Output monitor flags policy violation Detected at output, not input - improve input monitoring
Round 4 Indirect injection via document No detection - new attack vector Blue team adds document scanning pipeline

Detection Coverage Matrix

Track purple team progress with a detection coverage matrix:

Detection Coverage Matrix 
ATLAS Technique          | Tested | Detected | Alert Quality | Status
--------------------------|--------|----------|---------------|--------
Adversarial Input (FGSM)  | Yes    | Yes      | High          | Good
Adversarial Input (PGD)   | Yes    | Partial  | Medium        | Improving
Model Extraction          | Yes    | Yes      | Medium        | Good
Prompt Injection (Direct) | Yes    | Yes      | High          | Good
Prompt Injection (Indirect)| Yes   | No       | N/A           | Gap
Data Poisoning            | No     | N/A      | N/A           | Untested
Membership Inference      | Yes    | No       | N/A           | Gap
Jailbreaking              | Yes    | Partial  | Low           | Improving

Building a Purple Team Culture

  • Shared objectives — Both teams measure success by overall security improvement, not by whether attacks succeed or fail
  • No blame — Detection gaps are learning opportunities, not failures
  • Regular cadence — Schedule purple team exercises at a regular cadence (weekly or bi-weekly)
  • Knowledge sharing — Red team teaches attack techniques; blue team teaches detection architecture
  • Shared metrics — Track detection coverage, mean time to detect, and improvement velocity together
Pro Tip: Start small with tabletop exercises before moving to live testing. Walk through attack scenarios on a whiteboard first, discussing what monitoring would detect each step. This builds understanding before adding the complexity of live operations.

Ready to Explore Tools?

The next lesson covers automated red teaming tools and frameworks that scale your AI security testing efforts.

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