IVIS

AI Red Teaming · Adversarial Testing

Adversarial testing for systems that reason, retrieve, generate, or act.

When your product uses LLMs, agents, copilots, RAG, or tool-calling, the attack surface is the way it thinks — not just the code. ZIVIS red teams that behavior to find where it can be manipulated, leak data, or act without the right approval.

Conventional testing checks syntax. Red teaming AI checks meaning.

Standard security testing asks the right questions about authentication, inputs, and APIs. AI red teaming adds a new set of questions about how the system reasons, what it trusts, and what it can be talked into doing.

Traditional app security asks

  • Is authentication enforced?
  • Are inputs validated?
  • Are dependencies vulnerable?
  • Are APIs exposed?

AI red teaming also asks

  • Can the AI be manipulated by instructions?
  • Can it reveal data it should not?
  • Can it misuse tools or take unapproved actions?
  • Can it be steered into the wrong conclusion?
  • Can it trust the wrong context?
  • Can it create a workflow failure that looks normal?

What's included

  • Prompt and context manipulation testing.
  • RAG / data leakage and retrieval-poisoning testing.
  • Agent and tool-use abuse testing.
  • Permission and authority boundary testing.
  • Human approval-path and escalation review.
  • Memory and persistent-context poisoning review.
  • Logging and observability review.
  • AI-specific findings and remediation guidance.

Coverage that maps to the standards your buyers ask about.

  • OWASP LLM Top 10 — prompt injection, sensitive-data disclosure, supply chain, and more.
  • Agentic AI Top 10 — memory poisoning, tool misuse, privilege compromise, cascading failures.
  • Multi-step agent and workflow attack paths, not just single-prompt tricks.

Put your AI in front of an adversary.

Book AI red teaming as a standalone project, or pair it with a security audit or pen test. Not sure what your AI touches yet? Start with the Mini AI Risk Map.