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Audit an AI agent in five minutes
Run a deterministic privacy test locally, then replace the sample with a trace from your own agent. No account, hosted service or provider key is required.
1. Install and initialize
pip install agentleak
agentleak initThe initializer creates a reviewable agentleak.yaml, sample scenarios, traces and report directory. Pin a Git tag or commit for reproducible CI.
2. Run the built-in control
agentleak run --scenario healthcare_patient_summaryThe synthetic scenario exercises sensitive sources and disclosure channels without using production data. A JSON report explains every finding, channel and policy decision.
3. Analyze your own trace
{
"run_id": "run_001",
"agent_name": "support-bot",
"events": [
{"channel":"user_input","source":"user","target":"agent",
"content":"Book a follow-up for Maya Tremblay."},
{"channel":"tool_response","source":"crm","target":"agent",
"content":{"email":"[email protected]","sin":"123-456-789"}},
{"channel":"tool_call","source":"agent","target":"calendar",
"content":{"email":"[email protected]"}},
{"channel":"final_output","source":"agent","target":"user",
"content":"Follow-up scheduled."}
]
}agentleak run --trace trace.json --config agentleak.yaml --output reports/agentleak.jsonCapture events at boundaries: user input, tool calls and responses, memory, inter-agent messages, logs, generated files and final output.
4. Read the report
SourcesWhere the agent legitimately observed sensitive data.DisclosuresWhere that data crossed into a risky channel or target.Risk indexSeverity-weighted fraction of the audited vault that leaked.PolicyDeterministic assertions and the exact reason a gate passed or failed.