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.

Oui. La passerelle est compatible OpenTelemetry et s'intègre à Grafana, Datadog, Prometheus, ou à votre pile technologique préférée. Elle trace chaque requête, du prompt à l'exécution de l'outil et du modèle, vous offrant ainsi une journalisation unifiée sans avoir à remplacer ce que vous utilisez déjà.