AI Agent Penetration Testing & Red Teaming

An adversarial security assessment of autonomous and multi-agent AI systems that tests whether an attacker can manipulate an agent into taking unauthorized actions through its tools, memory, permissions, or connected systems.

In practice, yes. Both describe adversarial testing of an AI agent and everything it can do. Some teams say "penetration testing" and some say "red teaming"; we deliver the same rigorous, human-validated engagement either way.

LLM and chatbot testing focus on what the model outputs. Agent testing focuses on what the agent does, its ability to call tools and take real actions, which is where the serious business risk lives.

Tool and function abuse, excessive agency, memory and context poisoning, indirect prompt injection that triggers actions, task hijacking, human-in-the-loop bypass, and abuse of trust between agents in multi-agent systems.

Yes. Every finding is mapped to the OWASP Top 10 for Agentic Applications (2026), alongside the OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF.

Yes. We test inter-agent trust, message spoofing, orchestration abuse, and how a single manipulated agent affects the wider system.

Details of the agent architecture, its tools and permissions, and access to a test environment or a controlled scope of the production system, defined in the rules of engagement.

It depends on the number of agents, tool integrations, and whether the system is single or multi-agent. Request a scoped quote and we return pricing within 5 hours.