Every model you deploy, every agent you run, every pipeline you expose is a surface. And the threats targeting them don't look like anything your security team has seen before. Jailbreaks that bypass your system prompt in a single turn. Indirect injections hiding inside the PDFs your agents read. Tool abuse that tricks your AI into calling APIs it was never meant to touch. Data exfiltration through iterative probing.

Traditional security tools weren't built for this. Firewalls don't read prompts. SIEMs don't understand reasoning chains. Penetration testers can't move at the speed of a language model.

Before ANTICELLS RED sends a single adversarial input, it builds intelligence. A collector agent continuously ingests attack research from arXiv papers, GitHub repositories, and jailbreak databases, classifying every technique by injection type, target surface, encoding method, and attack style.

The result is a living strategy library that grows with the threat landscape — never frozen in time, never limited to what your team already knows. This is the difference between testing with yesterday's playbook and testing with everything that exists today.

At the center of ANTICELLS RED is an orchestrator: a central brain that takes your target, consults memory from every previous engagement, and decides which specialized agent to deploy, which strategy to lead with, and which attack paths are most likely to succeed.

It scores techniques by past success rates and coverage gaps. It routes intelligently. It doesn't repeat what didn't work. It doubles down on what did.

Think of it as the combination of a seasoned red team lead and a system that never forgets a single result from a single previous run — across every target it has ever tested.

ANTICELLS RED deploys specialized agents across the full spectrum of AI attack surfaces. Indirect prompt injection — adversarial payloads hidden inside documents, web pages, and tool outputs that your agent processes without suspecting. RAG poisoning — a dedicated agent that crafts semantically plausible poisoned documents, injects them into your retrieval corpus, and actively verifies they rank higher than clean content before your agent ever queries them.

Tool abuse and schema extraction — probing your agent's tool interfaces for unauthorized API calls, parameter manipulation, and capability disclosure. Hallucination and bullshit induction — payloads designed to corrupt your agent's reasoning context and force it to produce confident, plausible, and entirely fabricated outputs.

Each attack family runs continuously, autonomously, and at machine speed.

After each engagement, ANTICELLS RED produces structured reports that go beyond a list of failed prompts. You get attack paths — the exact sequence of decisions and inputs that led to a successful exploit. You get success rates by technique, by agent, by vulnerability class.

You get coverage maps showing which attack surfaces were tested and which remain unexplored. You get the reasoning behind every orchestrator decision — so your team understands not just what broke, but why the system decided to try it in the first place.