AI Red Teaming Services
Structured adversarial red teaming for AI and LLM systems — testing prompt injection, jailbreaks, model extraction, data poisoning, and AI agent compromise scenarios.
AI Red Teaming — Adversarial Testing for AI Systems
Structured adversarial testing of LLM applications, AI agents, and machine learning systems — uncovering vulnerabilities that automated scanners and conventional penetration tests miss entirely.
- OWASP LLM Top 10 full coverage with automated adversarial test suites
- Manual expert exploitation: novel attack chains, multi-turn manipulation, agent hijacking
- Indirect injection testing via realistic data sources (documents, emails, database records)
- Jailbreak resistance benchmarking across known and emerging bypass techniques
- Model extraction and data exfiltration attempt scenarios
- Risk-ranked findings with exploitability evidence and remediation roadmap
Why AI Fails Under Adversarial Pressure
Emergent LLM behaviours under adversarial conditions are not discoverable through code review or standard QA — red teaming is the only reliable method for surfacing them.
Manual + Automated Approach
Automated suites provide systematic OWASP LLM Top 10 coverage; human red teamers develop novel attack chains that no automated scanner can anticipate.
Regulatory Alignment
NIST AI RMF and EU AI Act both explicitly recommend adversarial evaluation as part of responsible AI deployment — red team findings serve as evidence for compliance frameworks.