You shipped an AI system.
You also shipped a new attack surface.
SpartanX red teams your LLMs and agents for prompt injection, guardrail bypass, agent manipulation, and data exfiltration, as a first-class surface.
The new surface problem
An LLM or an agent in production is not just a feature. It is an attacker-reachable surface that can be talked into leaking data, breaking its guardrails, or taking actions on your systems it was never meant to take.
Traditional testing was not built for this. Prompts are inputs, tools are reachable, and context is manipulable. None of that shows up in a scanner, and the risk grows every time the agent gets more power.
How AI red teaming works
Treat AI as a first-class surface
Your LLMs and agents are tested like any other attack surface, not bolted on as an afterthought.
Attack the model and the agent
The adversary runs prompt injection, guardrail and jailbreak bypass, agent manipulation, and attempts to turn the system's own tools against it.
Chase exfiltration and action
It tests whether the system can be driven to leak sensitive data or take unauthorized actions, then chains those into other surfaces where relevant.
Prove and prioritize
Each successful attack is shown with a reproducible proof of concept and scored by business impact.
What you get
AI risk made concrete
Guardrails under real pressure
Exfiltration paths, exposed
It ends treating AI safety as a one-off prompt checklist instead of an attack surface that adapts and must be tested like one.
Red team your AI
See what an adversary can make your LLMs and agents do.