AI agents invoke rerun_workflow to trigger actions in GitHub Actions 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.
Rerunning a workflow triggers external CI/CD execution (jobs, scripts, deployments) on GitHub Actions. This constitutes executing an external operation. The description is empty, so confidence is slightly reduced, but the tool name and server context strongly imply re-executing a prior workflow run. Misuse could cause repeated unintended deployments or resource consumption, warranting high severity.
From the tool's definition Tool name 'rerun_workflow' on a server that manages GitHub Actions workflows; sibling tools include 'trigger_workflow' and 'cancel_workflow_run', confirming execution-oriented operations.
Documented attack patterns abuse exactly the kind of access rerun_workflow gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and GitHub Actions MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for rerun_workflow:
{
"version": "1",
"default": "deny",
"tools": {
"rerun_workflow": {
"limits": [
{
"counter": "rerun_workflow_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}rerun_workflow 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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rerun_workflow. It is categorised as a Execute tool in the GitHub Actions MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the GitHub Actions MCP Server MCP server in PolicyLayer and add a rule for rerun_workflow: 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 GitHub Actions MCP Server. Nothing to install.
rerun_workflow 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 rerun_workflow 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 rerun_workflow. 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.
rerun_workflow is provided by the GitHub Actions MCP Server MCP server (ko1ynnky/github-actions-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from GitHub Actions MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
9 GitHub Actions MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.