继续执行直到当前函数返回。在返回点暂停。
AI agents invoke gdb_finish to trigger actions in GDB 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 resumes execution of a debugged program until the current function returns, then pauses. It triggers execution of code in the target process, making it an Execute-category tool. Misuse could cause the program to advance past important breakpoints or execute unintended code paths, but it pauses at the function return so impact is bounded.
From the tool's definition 继续执行直到当前函数返回。在返回点暂停。 (Continue execution until the current function returns. Pause at the return point.)
Attacks that exploit this kind of access
继续执行直到当前函数返回。在返回点暂停。. It is categorised as a Execute tool in the GDB MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the GDB MCP Server MCP server in PolicyLayer and add a rule for gdb_finish: 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 GDB MCP Server. Nothing to install.
gdb_finish 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 gdb_finish 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 gdb_finish. 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.
gdb_finish is provided by the GDB MCP Server MCP server (zjm1060/gdb-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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