AI red teaming for AI agents
Shark studies your agent the way an attacker would, then runs hundreds of multi-turn attacks against it: context poisoning, tool misuse and adversarial scenarios. You find the unsafe actions before release, not after.
What Shark tests
AI models and agents, including voice agents and agents connected to tools and MCP servers. If it takes input and does something with it, Shark can put pressure on it.
How an assessment works
Shark runs context poisoning, tool misuse simulation and adversarial scenario testing against your agent's real workflows.
Connect: use the browser extension, import your agent's API docs, or map its flow by hand.
Recon: Shark builds a profile first: what the agent is for, what it can reach, and how it responds.
Plan: each hypothesis about how the agent could fail branches into attack vectors, then specific probes.
Attack: probes run as multi-turn conversations, the way real attacks build context before they strike.
An attack library that keeps up with the attacks
A pipeline watches security frameworks and real-world incidents and turns them into new attack vectors, so the tests move when the threats do. Run the built-in suites of 200+ prompts each, or build your own flows when you want to push somewhere specific.
What you get
A findings report, written for the engineers who have to fix what it found. Every assessment ends with an executive report: confirmed and suspected vulnerabilities, the strengths that held, severity for each confirmed finding, and a mapping to the OWASP Top 10 for LLMs and for agentic AI.
From findings to enforced rules
If you also use Prism, the issues Shark finds become enforced rules. It works in the other direction too: attacks Prism catches in production feed back into Shark's library, so your next test covers them.
FAQ
What is AI red teaming?
Attacking your own AI agent before release to find the ways it can be pushed into unsafe actions, so you can fix them before real users or attackers find them.
How is LLM red teaming different from a penetration test?
A penetration test checks one fixed setup. An AI agent behaves differently across conversations, so red teaming tests how it responds across many contexts and attacks.
How do I connect my agent to Shark?
Three ways: the browser extension, importing your agent's API docs, or mapping its flow by hand. Shark then builds a recon profile before it starts testing.
Can I run Shark myself?
Yes. Shark is self-serve at shark.fencio.dev. If you want us to scope and run a full assessment with you, book one instead.
What happens to the findings?
You get them as a report. If you also use Prism, the issues Shark finds become enforced rules.
What are the biggest AI agent security risks?
The main risks are prompt injection (from user input, retrieved documents, tool responses or other agents), tool misuse, context poisoning, tools with more access than they need, and trusted MCP servers that push a malicious update. Each one can turn into a real action, like a transfer, a deleted log or leaked data, before anyone reviews it.