What is AI Red Teaming?

AI red teaming is the practice of systematically testing AI systems for vulnerabilities, biases, and failure modes. Like traditional security red teaming, it involves thinking like an attacker to find weaknesses.

Why Red Team AI?

AI systems can fail in unexpected ways:

• Prompt injection bypasses safety controls
• Jailbreaks extract harmful content
• Data extraction reveals training data
• Bias produces discriminatory outputs

Red Teaming Techniques

Prompt Injection Testing

Attempt to override system instructions through crafted inputs. Test both direct injection and indirect injection through retrieved content.

Jailbreak Attempts

Try to bypass content policies through roleplay, encoding, or multi-turn conversations.

Data Extraction

Probe for training data leakage, system prompts, or sensitive information in context.

Bias Testing

Test for discriminatory outputs across demographic groups and sensitive topics.

Building a Red Team Program

• Define scope: What systems and attack vectors to test
• Assemble team: Mix of security experts and domain specialists
• Create test cases: Systematic coverage of attack categories
• Document findings: Severity, reproducibility, remediation
• Iterate: Retest after fixes, add new attack vectors

Continuous Red Teaming

Red teaming shouldn't be a one-time event. Production systems need ongoing testing:

• Automated adversarial testing in CI/CD
• Production monitoring for attack patterns
• Regular manual testing for new techniques
• Bug bounty programs for external researchers

DriftRail for Security Monitoring

DriftRail helps detect attacks in production:

• Prompt injection detection flags manipulation attempts
• Policy violation detection catches jailbreaks
• PII detection identifies data extraction
• Alerts notify teams of suspicious patterns

FAQ