feat(phase6-9): Complete modular architecture and Agent Teams
Phase 6.4 - Modular Architecture: - Add lewooogo-brain adapters for LLM providers - Add lewooogo-data dual memory (Redis + PostgreSQL) - Implement consensus engine for multi-agent decisions - Add incident memory service for historical context Phase 9 - Agent Teams (Claude Agent SDK): - Add base agent class with Claude Sonnet 4 integration - Implement action planner, blast radius, and security agents - Add agent API endpoints and proposal workflow - Integrate ADR-009 OpenClaw Agent Teams architecture DevOps & CI/CD: - Add GitHub Actions CI/CD workflows (ci.yaml, cd.yaml) - Add pre-commit hooks and secrets baseline - Add docker-compose for local development - Update Kubernetes network policies Frontend Improvements: - Add auto-healing error boundary component - Update i18n messages for agent features - Enhance dual-state incident card with execution feedback Documentation: - Add 7 ADRs covering MCP, design system, architecture decisions - Update ARCHITECTURE_MEMORY.md with modular design - Add GLOBAL_RULES.md and SOUL.md for project identity Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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apps/api/src/agents/security.py
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apps/api/src/agents/security.py
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"""
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Security Agent - 安全風險評估專家
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=================================
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職責:
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- 分析提案的安全風險
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- 檢查權限邊界
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- 評估潛在漏洞
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- 回傳風險評分 (0-10)
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符合 ADR-009 SecurityAgent 規範
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"""
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import asyncio
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import time
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from dataclasses import dataclass, field
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from typing import Any
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import structlog
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from src.agents.base import AgentResult, AgentStatus, BaseAgent
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logger = structlog.get_logger(__name__)
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# =============================================================================
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# Security Result
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# =============================================================================
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@dataclass
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class SecurityResult(AgentResult):
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"""
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SecurityAgent 分析結果
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額外欄位:
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- risk_score: 風險評分 (0-10, 10 最高風險)
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- risk_factors: 風險因素列表
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- permission_issues: 權限問題
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- recommendations: 安全建議
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"""
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risk_score: float = 0.0
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risk_factors: list[str] = field(default_factory=list)
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permission_issues: list[str] = field(default_factory=list)
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recommendations: list[str] = field(default_factory=list)
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def to_dict(self) -> dict[str, Any]:
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"""轉換為 dict"""
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base = super().to_dict()
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base.update({
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"risk_score": self.risk_score,
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"risk_factors": self.risk_factors,
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"permission_issues": self.permission_issues,
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"recommendations": self.recommendations,
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})
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return base
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# =============================================================================
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# Security Agent
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# =============================================================================
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# 安全規則引擎 (本地快速檢查)
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SECURITY_RULES: dict[str, dict[str, Any]] = {
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"delete_operation": {
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"patterns": ["delete", "rm", "remove", "destroy", "drop"],
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"risk_score": 8.0,
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"factor": "破壞性操作: 涉及刪除資源",
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"recommendation": "確保有備份，並考慮使用 --dry-run 先行測試",
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},
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"force_operation": {
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"patterns": ["--force", "-f", "--no-wait", "--grace-period=0"],
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"risk_score": 7.0,
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"factor": "強制操作: 跳過安全確認",
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"recommendation": "移除 --force 參數，使用標準流程",
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},
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"privileged_namespace": {
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"patterns": ["kube-system", "kube-public", "default"],
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"risk_score": 9.0,
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"factor": "敏感命名空間: 操作影響 K8s 核心組件",
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"recommendation": "確認是否真的需要操作系統命名空間",
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},
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"secret_operation": {
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"patterns": ["secret", "configmap", "credential", "password", "token"],
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"risk_score": 8.5,
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"factor": "敏感資料: 操作涉及機密資訊",
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"recommendation": "確保日誌不會記錄機密內容",
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},
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"network_policy": {
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"patterns": ["networkpolicy", "ingress", "egress", "firewall"],
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"risk_score": 7.5,
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"factor": "網路變更: 可能影響服務連通性",
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"recommendation": "變更前確認流量影響範圍",
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},
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"rbac_operation": {
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"patterns": ["role", "rolebinding", "clusterrole", "serviceaccount"],
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"risk_score": 9.0,
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"factor": "權限變更: 操作涉及 RBAC 設定",
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"recommendation": "最小權限原則，避免過度授權",
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},
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"scale_to_zero": {
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"patterns": ["replicas=0", "replicas 0", "scale --replicas=0"],
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"risk_score": 8.0,
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"factor": "服務中斷: 副本數設為 0",
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"recommendation": "確認是否為計畫性維護",
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},
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"rollback": {
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"patterns": ["rollout undo", "rollback"],
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"risk_score": 5.0,
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"factor": "回滾操作: 相對安全但需確認目標版本",
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"recommendation": "確認回滾目標版本是穩定的",
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},
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"restart": {
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"patterns": ["rollout restart", "restart"],
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"risk_score": 3.0,
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"factor": "重啟操作: 低風險但可能造成短暫中斷",
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"recommendation": "確認服務有足夠副本處理滾動重啟",
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},
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}
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class SecurityAgent(BaseAgent[SecurityResult]):
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"""
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安全風險評估專家 Agent
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分析流程:
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1. 本地規則引擎快速掃描 (毫秒級)
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2. LLM 深度分析 (可選，複雜場景)
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3. 綜合評分
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使用方式:
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```python
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agent = SecurityAgent()
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result = await agent.analyze({
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"action": "kubectl delete pod nginx-xxx",
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"namespace": "awoooi-prod",
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"affected_services": ["nginx", "frontend"],
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})
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print(result.risk_score) # 0-10
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```
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"""
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AGENT_NAME = "security-expert"
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AGENT_DESCRIPTION = "資安專家，評估安全風險與權限影響"
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AGENT_TOOLS = ["Read", "Grep"] # 只讀權限
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def __init__(self, timeout_sec: float = 30.0, use_llm: bool = False):
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"""
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初始化 SecurityAgent
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Args:
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timeout_sec: 執行超時時間
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use_llm: 是否啟用 LLM 深度分析 (Phase 9.4 擴展)
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"""
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super().__init__(timeout_sec)
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self.use_llm = use_llm
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async def analyze(self, context: dict[str, Any]) -> SecurityResult:
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"""
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執行安全風險分析
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Args:
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context: 分析上下文
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- action: 要執行的指令
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- namespace: 目標命名空間
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- affected_services: 受影響服務列表
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- incident_id: 事件 ID (可選)
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Returns:
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SecurityResult 包含風險評分和詳細分析
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"""
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start_time = time.time()
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self.logger.info(
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"security_analysis_start",
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action=context.get("action", "")[:100],
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namespace=context.get("namespace"),
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)
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try:
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# Phase 1: 本地規則引擎 (同步、快速)
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rule_result = self._rule_engine_analyze(context)
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# Phase 2: LLM 深度分析 (可選，未來擴展)
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if self.use_llm and rule_result["risk_score"] >= 7.0:
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# 高風險場景啟用 LLM 二次確認
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# TODO: Phase 9.4 實作 LLM 分析
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pass
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latency_ms = int((time.time() - start_time) * 1000)
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result = SecurityResult(
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agent_name=self.AGENT_NAME,
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status=AgentStatus.SUCCESS,
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confidence=rule_result["confidence"],
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analysis=rule_result["analysis"],
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latency_ms=latency_ms,
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risk_score=rule_result["risk_score"],
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risk_factors=rule_result["risk_factors"],
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permission_issues=rule_result["permission_issues"],
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recommendations=rule_result["recommendations"],
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raw_response=rule_result,
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)
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self.logger.info(
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"security_analysis_complete",
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risk_score=result.risk_score,
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latency_ms=latency_ms,
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)
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return result
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except Exception as e:
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latency_ms = int((time.time() - start_time) * 1000)
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self.logger.exception(
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"security_analysis_error",
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error=str(e),
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)
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return SecurityResult(
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agent_name=self.AGENT_NAME,
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status=AgentStatus.FAILED,
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confidence=0.0,
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analysis=f"分析失敗: {str(e)}",
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latency_ms=latency_ms,
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error=str(e),
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risk_score=10.0, # 失敗時預設最高風險
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risk_factors=["分析過程發生錯誤"],
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recommendations=["請人工審核此操作"],
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)
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def _rule_engine_analyze(self, context: dict[str, Any]) -> dict[str, Any]:
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"""
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本地規則引擎分析
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快速檢查常見安全模式，毫秒級回應
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"""
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action = context.get("action", "").lower()
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namespace = context.get("namespace", "").lower()
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affected_services = context.get("affected_services", [])
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risk_factors: list[str] = []
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recommendations: list[str] = []
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permission_issues: list[str] = []
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max_risk_score: float = 0.0
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# 掃描所有安全規則
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for rule_name, rule in SECURITY_RULES.items():
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patterns = rule["patterns"]
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# 檢查 action
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if any(pattern in action for pattern in patterns):
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risk_factors.append(rule["factor"])
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recommendations.append(rule["recommendation"])
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max_risk_score = max(max_risk_score, rule["risk_score"])
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# 檢查 namespace
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if rule_name == "privileged_namespace":
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if any(pattern in namespace for pattern in patterns):
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risk_factors.append(rule["factor"])
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recommendations.append(rule["recommendation"])
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max_risk_score = max(max_risk_score, rule["risk_score"])
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# 檢查受影響服務數量
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if len(affected_services) > 5:
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risk_factors.append(f"大範圍影響: 涉及 {len(affected_services)} 個服務")
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max_risk_score = max(max_risk_score, 6.0)
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recommendations.append("考慮分批執行，降低爆炸半徑")
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# 檢查是否涉及生產環境
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if "prod" in namespace:
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if max_risk_score < 5.0:
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max_risk_score = 5.0 # 生產環境最低風險 5
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permission_issues.append("操作目標為生產環境")
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# 如果沒有匹配任何規則，給予基礎評分
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if not risk_factors:
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risk_factors.append("未偵測到明顯風險因素")
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max_risk_score = 2.0 # 基礎低風險
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# 計算信心分數 (規則匹配越多，信心越高)
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confidence = min(0.95, 0.7 + len(risk_factors) * 0.05)
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# 生成分析摘要
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if max_risk_score >= 8.0:
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analysis = f"高風險操作 (Score: {max_risk_score}/10): 建議人工審核"
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elif max_risk_score >= 5.0:
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analysis = f"中等風險 (Score: {max_risk_score}/10): 確認影響範圍後執行"
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else:
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analysis = f"低風險操作 (Score: {max_risk_score}/10): 可安全執行"
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return {
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"risk_score": max_risk_score,
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"risk_factors": risk_factors,
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"recommendations": list(set(recommendations)), # 去重
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"permission_issues": permission_issues,
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"confidence": confidence,
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"analysis": analysis,
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"rules_matched": len(risk_factors),
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}
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def _build_prompt(self, context: dict[str, Any]) -> str:
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"""建構 LLM Prompt (Phase 9.4 擴展)"""
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return f"""你是 AWOOOI 的資安專家。
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分析以下操作的安全風險：
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操作指令: {context.get("action", "N/A")}
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目標命名空間: {context.get("namespace", "N/A")}
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受影響服務: {", ".join(context.get("affected_services", []))}
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評估:
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1. 是否涉及敏感資料
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2. 是否可能被利用
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3. 權限邊界是否被突破
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輸出 JSON:
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```json
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{{
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"risk_score": 0-10,
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"risk_factors": ["...", "..."],
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"permission_issues": ["...", "..."],
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"recommendations": ["...", "..."],
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"analysis": "一句話摘要",
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"confidence": 0-1
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}}
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```"""
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def _parse_response(self, response: str) -> dict[str, Any]:
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"""解析 LLM 回應"""
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return self._extract_json(response)
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