Agentic AI red teaming, mapped to the OWASP Agentic Top 10
Agents do not just answer, they act. They call tools, hold memory, and chain steps toward a goal, and that changes the threat model. We red team AI agents against the OWASP Agentic Top 10, covering the failure modes a chatbot test never reaches.
Why agents need different testing than chatbots
A chatbot produces text. An agent takes actions: it calls APIs and tools, reads and writes memory, and decides its own next step. Each of those capabilities is new attack surface.
A prompt that is harmless as an answer can be dangerous as an instruction to act. Testing an agent means testing what it does, not just what it says.
The OWASP Agentic Top 10
We map agent testing to the OWASP Agentic Top 10, the community effort to catalogue the threats specific to agentic AI. We cover the current threat categories as the specification stands at the time of your engagement.
Attacks we run
The threat classes we exercise against an agent include:
- Tool and function-call abuse: getting the agent to call the wrong tool, or the right tool with dangerous arguments
- Memory poisoning: planting content the agent later trusts as its own context
- Goal and instruction manipulation: hijacking the objective the agent is pursuing
- Multi-step and indirect prompt injection across a chain of actions
- Excessive agency: the agent doing more than it was authorised to do
- Unsafe tool chains and permission or identity confusion
Trajectory inspection
We inspect the agent's step-by-step trajectory, not just its final output, so we can show exactly where a run went wrong and why. That makes a finding reproducible and fixable rather than anecdotal.
How it maps to a scorecard
Every finding gets a CVSS-aligned severity score and ships in a signed, dated report with a patch playbook and rerun criteria, the same format we use for all red-team work.
Related frameworks
Agentic testing sits alongside MITRE ATLAS and the NIST AI RMF, so the agent-specific findings connect to the broader adversarial-ML and governance picture your organisation already tracks.
Who signs the report.
Lattice/AI is an independent AI evaluation firm, founded and led by Rahul Srivastava. For seventeen years he has made it his work to find how systems fail before the people who rely on them do, and he built Lattice/AI to bring that discipline to AI.
His research on AI safety is published and peer-reviewed, he contributes to Microsoft PyRIT, the open framework Microsoft built for adversarial AI testing, and his findings have carried enough weight for a major model vendor to ship a fix in response. What you are hiring is judgment proven in the open, with a name attached to it, not a logo.
Every engagement ends in a signed, dated report that shows what failed next to what passed. Here is the format we ship: a sample scorecard.
Questions buyers ask.
What is the OWASP Agentic Top 10?
A community effort to catalogue the security threats specific to agentic AI, the systems that call tools, hold memory, and act autonomously, rather than only generating text. We map agent red teaming to it.
How is agentic red teaming different from LLM red teaming?
LLM red teaming targets what a model says. Agentic red teaming targets what an agent does: tool calls, memory, autonomy, and multi-step chains. The failure modes, and the blast radius, are different.
What is excessive agency and how do you test for it?
Excessive agency is an agent taking actions beyond what it was authorised to do. We test it by probing whether the agent can be led to use tools, permissions, or data outside its intended scope.
Do you test multi-agent systems?
Yes. We test single agents and systems where multiple agents coordinate, including the handoffs and shared memory between them.
Can you test tool and MCP integrations?
Yes. Tool chains, function-calling, and integration layers are a core part of the agent attack surface we exercise.
Tell us what’s under contract.
Three sentences is usually enough. Tell us what you are trying to ship, when it has to land, and the thing that scares you about it.