AI Red Teaming
Adversarial testing of AI systems the way a real attacker would approach them: autonomous campaigns against LLM applications, AI agents and multi-agent workflows, with evidence for security teams, auditors and regulators.
Red teaming and penetration testing
| AI penetration testing | AI red teaming | |
|---|---|---|
| Objective | Identify vulnerabilities in a defined system | Show how a realistic adversary could reach a goal |
| Scope | One application or integration | Systems, agents and workflows end to end |
| Approach | Systematic test catalogue | Multi-step, multi-turn and multi-agent campaigns |
| Frequency | Before release or periodically | Before release and continuously as regression |
Many organizations combine both; see AI penetration testing.
What the campaigns cover
Autonomous campaigns
Nine specialized AI-Egis agents plan, adapt and evaluate each campaign.
Agents and multi-agent systems
Tool use, delegation between agents and trust boundaries in orchestration.
Guardrail resilience
Whether defenses keep working against adaptive and multi-turn attempts.
Reproducible evidence
Session recording with byte-identical replay and SARIF reporting.
Regulatory mapping
OWASP, MITRE ATLAS, EU AI Act and NIST AI RMF.
Original research
Published work by I-314 Security Research, including the Tool Output Mimicry technique (DOI 10.5281/zenodo.19794072).
Frequently asked questions
What is AI red teaming?
Adversarial testing of an AI system from the perspective of a motivated attacker, with the goal of finding how it can fail, be misused or be manipulated before it reaches production or while it operates.
How is AI red teaming different from AI penetration testing?
A penetration test is a scoped assessment of a defined system at a point in time. Red teaming runs broader, multi-step campaigns that simulate realistic adversaries, including multi-turn and multi-agent scenarios, and can be repeated continuously.
Is AI red teaming required by regulation?
The EU AI Act requires adversarial testing for general-purpose AI models with systemic risk, and frameworks such as the NIST AI RMF recommend it. Supervisors increasingly expect documented, reproducible evidence of testing.
How often should it be done?
Before each release and after significant changes to models, prompts, tools or data sources. Deterministic replay allows previous campaigns to be repeated as regression tests.
Plan an AI red team exercise
I-314 can define objectives, rules of engagement and scope for your AI systems.