Low Risk

scan_directory

Scan a directory for LLM SDK usage, AI frameworks, exposed API tokens, and hardcoded secrets. Returns all findings grouped by type (token, secret, sdk, framework, endpoint, model) with file locations and severity levels.

Risk signalsAccepts file system path (directory)

Part of the AI Scanner server.

scan_directory is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call scan_directory to retrieve information from AI Scanner without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though scan_directory only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "scan_directory": {}
  }
}

See the full AI Scanner policy for all 3 tools.

Get this rule live on your own AI Scanner server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access scan_directory gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so scan_directory only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the scan_directory tool do? +

Scan a directory for LLM SDK usage, AI frameworks, exposed API tokens, and hardcoded secrets. Returns all findings grouped by type (token, secret, sdk, framework, endpoint, model) with file locations and severity levels.. It is categorised as a Read tool in the AI Scanner MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on scan_directory? +

Register the AI Scanner MCP server in PolicyLayer and add a rule for scan_directory: 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 AI Scanner. Nothing to install.

What risk level is scan_directory? +

scan_directory is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit scan_directory? +

Yes. Add a rate_limit block to the scan_directory 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.

How do I block scan_directory completely? +

Set action: deny in the PolicyLayer policy for scan_directory. 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.

What MCP server provides scan_directory? +

scan_directory is provided by the AI Scanner MCP server (ai-scanner-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every AI Scanner tool call.

Deterministic rules across all 3 AI Scanner tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

Free to start. No card required.

4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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