AI agents invoke sam_local_invoke to trigger actions in AWS Labs CloudTrail MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool executes code by invoking serverless functions. Even if local, it can run arbitrary code whose effects depend on function arguments and implementation. This is Execute-category because it triggers external operations (function invocation) whose consequences are argument-dependent.
From the tool's definition Tool name 'sam_local_invoke' indicates invocation of AWS SAM (Serverless Application Model) functions locally. The 'invoke' action triggers code execution.
Documented attack patterns abuse exactly the kind of access sam_local_invoke gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and AWS Labs CloudTrail MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for sam_local_invoke:
{
"version": "1",
"default": "deny",
"tools": {
"sam_local_invoke": {
"limits": [
{
"counter": "sam_local_invoke_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} sam_local_invoke stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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sam_local_invoke. It is categorised as a Execute tool in the AWS Labs CloudTrail MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the AWS Labs CloudTrail MCP Server MCP server in PolicyLayer and add a rule for sam_local_invoke: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches AWS Labs CloudTrail MCP Server. Nothing to install.
sam_local_invoke is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the sam_local_invoke rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for sam_local_invoke. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
sam_local_invoke is provided by the AWS Labs CloudTrail MCP Server MCP server (awslabs.cloudtrail-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from AWS Labs CloudTrail MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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805 AWS Labs CloudTrail MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.