What Is AI Red Teaming?
Adversarially testing AI systems to discover vulnerabilities before attackers do.
AI red teaming is the practice of adversarially testing AI systems to discover vulnerabilities, failure modes, and safety gaps before they're exploited in production. For AI agents with tool-use capabilities, red teaming is particularly critical — it tests not just what the model says, but what it can be manipulated into doing.
Red Teaming Techniques
AI red teaming techniques include: prompt injection (direct and indirect), jailbreaking (bypassing safety filters), role-playing attacks (manipulating model persona), context manipulation (poisoning retrieved data), tool abuse (exploiting function calling), multi-turn coaxing (gradually escalating requests), and adversarial machine learning (crafted inputs that cause misclassification). For agents with tool use, the most dangerous techniques target the action pathway — not the text output.
Red Teaming AI Agents vs Chatbots
Red teaming a chatbot tests text output safety. Red teaming an AI agent tests action safety. The attack surface is fundamentally different: can the agent be manipulated into executing destructive database queries? Can it be tricked into exfiltrating data via API calls? Can it be looped into infinite tool-use cycles? Can it bypass permission boundaries through indirect instructions? Agent red teaming requires testing the full stack — model, tools, and execution governance.
Exogram's Red Team Results
Exogram has been validated through extensive adversarial testing: 50 concurrent agents, 1,000 randomized MCP payloads, 14 attack vectors. Results: 952/1,000 correctly routed. 553 malicious payloads blocked. 399 benign permitted. 0 false negatives (no malicious action was permitted). 0 false positives (no benign action was incorrectly blocked). The deterministic policy engine doesn't degrade under adversarial conditions because it uses code logic, not probabilistic inference.
Frequently Asked Questions
How often should I red team my AI system?
Continuously. AI systems face evolving threats — new attack techniques emerge regularly. Automated red teaming should run as part of your CI/CD pipeline, with manual adversarial testing performed quarterly.
Can a well-trained model be immune to red team attacks?
No. No model is immune to all adversarial inputs. This is why execution governance is essential — even if an attack succeeds at the model level, the execution boundary blocks unauthorized actions.
What attack vectors did Exogram's red team cover?
14 vectors including: SQL injection via tool calls, privilege escalation, filesystem access, data exfiltration, billing exploitation, credential harvesting, phishing blast execution, and multi-agent coordination attacks.