AI Red Teaming for Agents: Attacks, Campaigns, and Runtime Defense

Adversarial testing of an AI system — model, prompts, retrieved context and tools together — to find inputs that make it act against your intent. The classes are jailbreaks, direct and indirect prompt injection, data exfiltration, tool abuse, and resource-exhaustion “sponge” attacks. It differs from penetration testing because the attack surface is natural language, results are probabilistic, and a model upgrade can reopen a class you closed.

No. An LLM firewall is runtime enforcement: inline guardrails inspecting, rewriting or blocking on every request. Red teaming is offline discovery: campaigns that generate attacks and report what worked. Campaigns tell you what to enforce; enforcement makes the finding stop costing you.

Not in the campaign sense. It provides the runtime enforcement half: guardrails on four hooks, ten native built-ins, roughly twenty partner integrations, policy binding by model, MCP server, tool, user, team or virtual account, and per-guardrail metrics. It does not document attack generation, an adversarial corpus, scheduled scans or red-team reporting. Several partners are security vendors with campaign-side capability of their own.

Yes. TrueFoundry runs in your VPC, on-prem, air-gapped, or hybrid, so prompts and responses never leave your domain even as you route across many providers.

Yes. It includes an MCP Gateway, an Agent Gateway, and an MCP & Agents Registry with tool-level access control. Agents on LangGraph, CrewAI, AutoGen, or a custom framework can all be governed centrally.

Ja. Das Gateway ist OpenTelemetry-kompatibel und lässt sich in Grafana, Datadog, Prometheus oder Ihren bevorzugten Stack integrieren. Es verfolgt jede Anfrage vom Prompt bis zur Ausführung von Tools und Modellen, sodass Sie eine einheitliche Protokollierung erhalten, ohne Ihre bestehenden Systeme entfernen zu müssen.