class TechnicalArtifactCollector:
    """蒐集關於 AI 部署之公開技術產物。"""
 
    def check_common_artifacts(self, domain):
        """檢查公開可存取之技術產物。"""
        artifacts = {}
 
        # robots.txt 可能揭露 AI 相關路徑
        artifacts["robots_txt"] = self.fetch(f"https://{domain}/robots.txt")
 
        # OpenAPI/Swagger 文件
        swagger_paths = [
            "/swagger.json", "/openapi.json",
            "/api/docs", "/docs/api",
            "/v1/openapi.json"
        ]
        for path in swagger_paths:
            result = self.fetch(f"https://{domain}{path}")
            if result:
                artifacts["api_spec"] = result
 
        # 狀態與健康檢查端點
        health_paths = ["/health", "/status", "/api/health"]
        for path in health_paths:
            result = self.fetch(f"https://{domain}{path}")
            if result:
                artifacts["health"] = result
 
        # JavaScript bundle 可能含 API 端點
        # （需手動分析頁面原始碼）
 
        return artifacts

class OSINTReport:
    """將 OSINT 發現結構化為可付諸行動之報告。"""
 
    def __init__(self, target_name):
        self.target = target_name
        self.findings = {
            "infrastructure": {},
            "models": {},
            "team": {},
            "security_posture": {},
            "attack_surface": {}
        }
 
    def synthesize(self):
        """產生排序後之攻擊建議。"""
        recommendations = []
 
        # 若已知模型家族，建議針對性攻擊
        if self.findings["models"].get("family"):
            recommendations.append({
                "priority": "high",
                "finding": f"Model identified as {self.findings['models']['family']}",
                "recommendation": "Use model-specific attack techniques"
            })
 
        # 若於公開儲存庫中發現系統提示或組態
        if self.findings.get("exposed_configs"):
            recommendations.append({
                "priority": "critical",
                "finding": "System configuration found in public repository",
                "recommendation": "Craft injection payloads using exact system prompt"
            })
 
        # 若徵才啟事中無 AI 安全角色
        if not self.findings["team"].get("has_security_team"):
            recommendations.append({
                "priority": "medium",
                "finding": "No evidence of dedicated AI security team",
                "recommendation": "Expect weaker defenses; start with basic techniques"
            })
 
        return sorted(recommendations,
                      key=lambda x: {"critical": 0, "high": 1,
                                     "medium": 2, "low": 3}[x["priority"]])