Continue execution in the debugger after hitting a breakpoint.
AI agents invoke continue_execution to trigger actions in Dap. 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 resumes program execution in a debugger, which is an active execution control action. It affects the running state of a debugged process and could cause code to run past breakpoints, potentially leading to unintended behavior depending on what code executes next.
From the tool's definition Continue execution in the debugger after hitting a breakpoint
Documented attack patterns abuse exactly the kind of access continue_execution gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Dap, and nothing reaches the server without passing your rules. This is the rule we recommend for continue_execution:
{
"version": "1",
"default": "deny",
"tools": {
"continue_execution": {
"limits": [
{
"counter": "continue_execution_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}continue_execution 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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Continue execution in the debugger after hitting a breakpoint. It is categorised as a Execute tool in the Dap MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Dap MCP server in PolicyLayer and add a rule for continue_execution: 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 Dap. Nothing to install.
continue_execution 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 continue_execution 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 continue_execution. 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.
continue_execution is provided by the Dap MCP server (kashuncheng/dap_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Dap, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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14 Dap tools catalogued and risk-classified — across an index of 43,000+ MCP servers.