AI agents invoke run_ghauri to trigger actions in Pentester-MCP. 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.
Ghauri is a SQL injection testing and exploitation tool that runs queries against target databases to identify and exploit vulnerabilities. While the description is empty, the tool name combined with server context and sibling tools clearly indicate this executes arbitrary SQL injection payloads against external systems.
From the tool's definition Tool name 'run_ghauri' with context that Ghauri is an open-source SQL injection exploitation tool. The server description explicitly states it enables 'execution' of 'penetration testing tools' including 'web exploitation.' The sibling tools…
Documented attack patterns abuse exactly the kind of access run_ghauri gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Pentester-MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for run_ghauri:
{
"version": "1",
"default": "deny",
"tools": {
"run_ghauri": {
"limits": [
{
"counter": "run_ghauri_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}run_ghauri 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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run_ghauri. It is categorised as a Execute tool in the Pentester-MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Pentester- MCP server in PolicyLayer and add a rule for run_ghauri: 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 Pentester-MCP. Nothing to install.
run_ghauri 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 run_ghauri 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 run_ghauri. 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.
run_ghauri is provided by the Pentester- MCP server (halilkirazkaya/pentester-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Pentester-MCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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337 Pentester-MCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.