Audit what your agent can actually do — not just what it can be tricked into saying.
Tool authorization, MCP server scope, tenant boundaries, and sensitive-action protection — reviewed end to end, with proof for every reachable impact.
Where AI agents actually leak
Most agent incidents aren't model failures — they're authorization failures behind a tool call. Telhawk audits the layer where the agent meets your data and systems.
Which tools the agent can call, on whose behalf, with what data — and whether the underlying APIs enforce the same boundaries as the human UI.
Authentication, scope, tenant isolation, and abuse paths in MCP servers and function-calling endpoints behind the agent.
Not whether the model can be tricked — it can — but what an attacker actually reaches when it is: data, actions, other tenants, downstream systems.
Money movement, exports, account changes, and any agent-triggered action that should require approval, throttling, or audit logging.
Agent security with proof attached.
What you get from a Telhawk agent audit
Common questions about AI agent security audits
Prompt injection is part of it, but it isn't the goal. The goal is to find what an attacker can actually do through the agent — read other tenants' data, trigger sensitive actions, move money, exfiltrate secrets — and prove it. Most agent risk is authorization risk wearing an LLM costume.
Yes. MCP servers, function-calling endpoints, and tool APIs are treated as first-class attack surface — same model we use for any sensitive API, with agent-specific abuse paths layered on top.
Access to a representative environment, the tool/MCP catalog, and the authorization model. Telhawk handles scoping, scenarios, and validation. Staging is preferred; production is workable with safety boundaries.
Telhawk audits the tools, scopes, and approval flows that decide what your agent can actually do — and proves every finding.