LLM red teaming guide
Security controls are only effective if they work under adversarial pressure. This guide provides structured red teaming exercises for validating your AI security defenses — injection detection, PII masking, policy enforcement, and data exfiltration prevention.
Red team exercises
Injection Detection
Test prompt firewall against known injection patterns
- → Basic instruction override
- → Encoded payloads (Base64, ROT13)
- → Context switching attacks
- → Multi-turn escalation
PII Bypass
Test PII detection with obfuscated personal data
- → Split entity across lines
- → Phonetic spelling of names
- → Partial redaction attempts
- → Custom entity evasion
Policy Bypass
Attempt to circumvent governance policies
- → Provider switching to unapproved tools
- → Role misrepresentation
- → After-hours usage simulation
- → Sensitivity threshold testing
Data Exfiltration
Test document leak detection
- → Paraphrased corporate content
- → Summarized financial data
- → Fragmented document submission
- → Cross-session data assembly
Red team methodology
- Scope — Define which AI surfaces and controls to test. Use threat modeling to identify high-priority test areas.
- Reconnaissance — Understand the target: which AI tools, detection engines, policy rules, and enforcement actions are deployed.
- Attack execution — Run the structured exercises above, documenting each attempt, result, and any successful bypass.
- Analysis — Review detection logs in the audit trail to assess what was caught and what was missed.
- Remediation — Update detection rules, adjust thresholds, and retrain models to close identified gaps.
Test your AI defenses
Deploy PromptWall and validate with structured red teaming.
Frequently asked questions
What is LLM red teaming?+
LLM red teaming is the practice of adversarial testing against AI systems to discover vulnerabilities, bypass defenses, and evaluate security controls. It includes prompt injection testing, jailbreak attempts, data exfiltration simulation, and policy bypass evaluation — specifically targeting AI-specific attack surfaces.
How often should I red team AI systems?+
Quarterly at minimum, plus ad-hoc testing after: deploying new AI tools, changing security policies, updating detection models, or when new attack techniques are published. Continuous automated testing complements periodic manual red team exercises.
Can I red team ChatGPT and Copilot directly?+
You can test your security controls around ChatGPT and Copilot — not the models themselves. Red teaming should focus on: can my prompt firewall detect injection? Does PII masking catch all entity types? Can users bypass governance policies? This validates your defenses, not OpenAI's model safety.