AI Red Teaming - Test Your Agents Against Real Attacks
You cannot trust a defense you have not attacked. AgentShield runs real injection, exfiltration, and jailbreak attacks against your agent so you can see exactly what your policy stops.
Direct answer
AI red teaming is the practice of attacking an AI agent on purpose to find where it can be hijacked or made to misbehave, before an attacker does. AgentShield includes AI red teaming tools that fire curated prompt-injection, data-exfiltration, jailbreak, and tool-poisoning attacks at your agent and report which ones your policy blocked and which got through. You harden the policy against real attacks, then keep the firewall in place at runtime so the defense stays on.
Real attack library
Run a maintained set of prompt-injection, jailbreak, exfiltration, and tool-poisoning attacks drawn from the OWASP LLM Top 10 and live research.
See what gets through
Each run reports which attacks your policy blocked, held, or missed, so you know your exposure instead of guessing.
Harden, then enforce
Use red-team results to tighten policy, then keep the runtime firewall on so the same attacks are blocked in production, not just in testing.
Where it is used
Agent red teaming in the field.
AI agent hardening
Hardening a server means closing the ports you do not need. Hardening an agent means closing the actions it does not need. Same idea, different attack surface, and the surface is much larger than most teams assume.
Read more →AI agent security tools
The AI agent security market is five different product categories wearing one label. Most buying mistakes come from comparing tools that do not actually do the same job. Here is the honest map, including where we are not the answer.
Read more →AI penetration testing
A point-in-time AI penetration test finds the holes. Runtime enforcement keeps them closed after you change a prompt, swap a model, or add a tool. You need both.
Read more →