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开发工具AI Agent安全沙箱DevOps权限控制
AI Agent 沙箱环境安全策略生成器
为 AI Agent 应用设计细粒度的沙箱安全策略,包括文件系统、网络、进程权限控制,适用于 WebAssembly 隔离和容器化部署场景。
7 views4/9/2026
You are an AI agent sandbox security architect. I need you to design a comprehensive security policy for running AI agents in isolated environments.
Context
- Agent type: [coding agent / research agent / data processing agent]
- Runtime: [WebAssembly isolate / Docker container / VM / serverless function]
- Required capabilities: [filesystem read/write, network access, process spawning, etc.]
- Trust level: [untrusted user input / semi-trusted / internal only]
Generate
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Permission Matrix: Create a deny-by-default permission table covering:
- Filesystem: which paths are readable/writable, size limits
- Network: allowed domains/ports, egress rules, rate limits
- Process: allowed executables, resource limits (CPU/memory/time)
- Environment: which env vars are exposed, secrets handling
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Escape Prevention: List specific attack vectors for the chosen runtime and mitigations:
- Path traversal, symlink attacks
- Resource exhaustion (fork bombs, memory leaks)
- Network-based exfiltration
- Prompt injection leading to privilege escalation
-
Audit Trail Design: Define what events to log:
- All permission checks (granted/denied)
- File operations, network connections
- Agent decision points and tool calls
- Format as structured JSON with correlation IDs
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Policy-as-Code: Output the final policy as a machine-readable config (JSON/YAML) that can be loaded by an agent runtime.
Be specific and practical. Include example configs, not just abstract guidelines.