016_具身人工智能的安全开发生命周期：从需求分析到部署运维的全流程安全保障

# 具身AI威胁建模框架示例
class EmbodiedAIThreatModel:
    def __init__(self, system_name, system_components):
        self.system_name = system_name
        self.components = system_components
        self.threats = []
        self.assets = []
        self.attack_surfaces = []
        self.risks = []
    
    def identify_assets(self, asset_list):
        """识别系统资产"""
        for asset in asset_list:
            self.assets.append({
                "name": asset["name"],
                "type": asset["type"],  # 硬件、软件、数据、功能
                "value": asset["value"],  # 高、中、低
                "owner": asset.get("owner", "系统")
            })
    
    def define_attack_surfaces(self, surfaces):
        """定义攻击面"""
        for surface in surfaces:
            self.attack_surfaces.append({
                "component": surface["component"],
                "surface_type": surface["type"],  # 物理接口、网络接口、传感器等
                "access_method": surface["access_method"],
                "potential_impact": surface.get("potential_impact", "未知")
            })
    
    def identify_threats(self, threat_list):
        """识别潜在威胁"""
        for threat in threat_list:
            self.threats.append({
                "name": threat["name"],
                "category": threat["category"],  # 物理攻击、传感器欺骗等
                "target": threat["target"],
                "potential_impact": threat["impact"],
                "attack_vector": threat.get("vector", "未定义")
            })
    
    def assess_risks(self):
        """进行风险评估"""
        for threat in self.threats:
            # 查找受影响的资产
            affected_assets = [asset for asset in self.assets 
                             if asset["name"] == threat["target"]]
            
            for asset in affected_assets:
                # 计算风险等级（简化模型）
                likelihood = self._calculate_likelihood(threat)
                impact = self._calculate_impact(asset["value"], threat["potential_impact"])
                risk_score = likelihood * impact
                
                risk_level = "低"
                if risk_score >= 7: risk_level = "高"
                elif risk_score >= 4: risk_level = "中"
                
                self.risks.append({
                    "threat": threat["name"],
                    "affected_asset": asset["name"],
                    "likelihood": likelihood,
                    "impact": impact,
                    "risk_score": risk_score,
                    "risk_level": risk_level,
                    "recommended_mitigation": self._suggest_mitigation(threat, asset)
                })
    
    def _calculate_likelihood(self, threat):
        """计算威胁发生的可能性（1-10）"""
        # 简化的可能性计算逻辑
        category_weights = {
            "物理攻击": 6,
            "传感器欺骗": 7,
            "决策攻击": 5,
            "通信攻击": 8,
            "隐私侵犯": 7
        }
        base_likelihood = category_weights.get(threat["category"], 5)
        return min(base_likelihood, 10)
    
    def _calculate_impact(self, asset_value, threat_impact):
        """计算潜在影响（1-10）"""
        # 简化的影响计算逻辑
        value_weights = {"高": 3, "中": 2, "低": 1}
        impact_weights = {
            "数据泄露": 8,
            "系统破坏": 9,
            "功能失效": 7,
            "隐私侵犯": 8,
            "物理伤害": 10
        }
        
        value_factor = value_weights.get(asset_value, 2)
        impact_factor = impact_weights.get(threat_impact, 5)
        return min(value_factor * impact_factor / 2, 10)
    
    def _suggest_mitigation(self, threat, asset):
        """建议缓解措施"""
        # 基于威胁和资产类型的简化缓解措施建议
        if threat["category"] == "物理攻击":
            return "实施物理访问控制、防篡改包装和入侵检测"
        elif threat["category"] == "传感器欺骗":
            return "采用多传感器融合、异常检测和传感器冗余"
        elif threat["category"] == "决策攻击":
            return "实施决策验证、异常行为检测和权限控制"
        else:
            return "根据具体威胁制定专门的缓解策略"
    
    def generate_threat_model_report(self):
        """生成威胁模型报告"""
        return {
            "system_name": self.system_name,
            "assets": self.assets,
            "attack_surfaces": self.attack_surfaces,
            "threats": self.threats,
            "risks": sorted(self.risks, key=lambda x: x["risk_score"], reverse=True),
            "high_priority_risks": [r for r in self.risks if r["risk_level"] == "高"]
        }

# 使用示例
def example_threat_modeling():
    # 创建威胁模型实例
    threat_model = EmbodiedAIThreatModel(
        "智能服务机器人系统",
        ["感知系统", "决策系统", "执行系统", "通信系统", "用户交互界面"]
    )
    
    # 识别资产
    assets = [
        {"name": "摄像头传感器", "type": "硬件", "value": "中"},
        {"name": "激光雷达", "type": "硬件", "value": "高"},
        {"name": "控制算法", "type": "软件", "value": "高"},
        {"name": "用户数据", "type": "数据", "value": "高"},
        {"name": "运动控制系统", "type": "功能", "value": "高"}
    ]
    threat_model.identify_assets(assets)
    
    # 定义攻击面
    surfaces = [
        {"component": "摄像头传感器", "type": "感知接口", 
         "access_method": "物理访问/信号干扰", "potential_impact": "感知欺骗"},
        {"component": "通信系统", "type": "网络接口", 
         "access_method": "无线信号拦截", "potential_impact": "数据泄露"},
        {"component": "用户交互界面", "type": "人机接口", 
         "access_method": "社会工程学", "potential_impact": "未授权访问"}
    ]
    threat_model.define_attack_surfaces(surfaces)
    
    # 识别威胁
    threats = [
        {"name": "摄像头欺骗攻击", "category": "传感器欺骗", 
         "target": "摄像头传感器", "impact": "功能失效"},
        {"name": "激光雷达干扰", "category": "传感器欺骗", 
         "target": "激光雷达", "impact": "功能失效"},
        {"name": "控制算法篡改", "category": "决策攻击", 
         "target": "控制算法", "impact": "系统破坏"},
        {"name": "用户数据窃取", "category": "隐私侵犯", 
         "target": "用户数据", "impact": "数据泄露"},
        {"name": "物理劫持攻击", "category": "物理攻击", 
         "target": "运动控制系统", "impact": "物理伤害"}
    ]
    threat_model.identify_threats(threats)
    
    # 评估风险
    threat_model.assess_risks()
    
    # 生成报告
    report = threat_model.generate_threat_model_report()
    print(f"识别的高优先级风险数量: {len(report['high_priority_risks'])}")
    return report

# 具身AI自动化安全测试框架示例
class AutomatedSecurityTesting:
    def __init__(self, target_system):
        self.target_system = target_system
        self.test_suites = {
            "sensor_security": [],
            "communication_security": [],
            "authentication_security": [],
            "authorization_security": [],
            "physical_security": []
        }
        self.test_results = []
    
    def add_test_case(self, test_category, test_case):
        """添加测试用例"""
        if test_category in self.test_suites:
            self.test_suites[test_category].append({
                "name": test_case["name"],
                "description": test_case["description"],
                "test_function": test_case["function"],
                "severity": test_case.get("severity", "中"),
                "prerequisites": test_case.get("prerequisites", [])
            })
    
    def run_tests(self):
        """运行所有测试用例"""
        self.test_results = []
        
        for category, tests in self.test_suites.items():
            print(f"运行测试类别: {category}")
            for test in tests:
                print(f"  执行测试: {test['name']}")
                
                try:
                    # 检查前置条件
                    prerequisites_met = self._check_prerequisites(test["prerequisites"])
                    if not prerequisites_met:
                        result = {
                            "category": category,
                            "test_name": test["name"],
                            "status": "跳过",
                            "reason": "前置条件未满足",
                            "timestamp": datetime.now().isoformat()
                        }
                    else:
                        # 执行测试函数
                        test_result = test["test_function"](self.target_system)
                        result = {
                            "category": category,
                            "test_name": test["name"],
                            "status": "通过" if test_result["passed"] else "失败",
                            "details": test_result.get("details", ""),
                            "remediation": test_result.get("recommendations", ""),
                            "timestamp": datetime.now().isoformat()
                        }
                except Exception as e:
                    result = {
                        "category": category,
                        "test_name": test["name"],
                        "status": "错误",
                        "error": str(e),
                        "timestamp": datetime.now().isoformat()
                    }
                
                self.test_results.append(result)
                print(f"  测试结果: {result['status']}")
    
    def _check_prerequisites(self, prerequisites):
        """检查测试前置条件"""
        # 简化的前置条件检查
        return True  # 实际实现中需要检查具体条件
    
    def generate_report(self, format="json"):
        """生成测试报告"""
        summary = {
            "total_tests": len(self.test_results),
            "passed_tests": sum(1 for r in self.test_results if r["status"] == "通过"),
            "failed_tests": sum(1 for r in self.test_results if r["status"] == "失败"),
            "skipped_tests": sum(1 for r in self.test_results if r["status"] == "跳过"),
            "error_tests": sum(1 for r in self.test_results if r["status"] == "错误"),
            "pass_rate": (sum(1 for r in self.test_results if r["status"] == "通过") / 
                         max(len(self.test_results), 1)) * 100
        }
        
        report = {
            "target_system": self.target_system,
            "test_summary": summary,
            "test_results": self.test_results,
            "high_severity_failures": [r for r in self.test_results 
                                      if r["status"] == "失败" and 
                                      any(t["severity"] == "高" for t in 
                                          self.test_suites.get(r["category"], []))]
        }
        
        if format == "json":
            return json.dumps(report, indent=2)
        elif format == "html":
            # 可以实现HTML格式报告生成
            return self._generate_html_report(report)
        else:
            return str(report)
    
    def _generate_html_report(self, report):
        """生成HTML格式的测试报告"""
        # 简化的HTML报告生成
        return "<html><body><h1>安全测试报告</h1></body></html>"

# 示例测试用例函数
def test_sensor_spoofing_detection(target_system):
    """测试传感器欺骗检测能力"""
    # 实际实现中，这里会执行真实的传感器欺骗测试
    # 以下是示例返回结果
    return {
        "passed": True,
        "details": "系统成功检测到95%的传感器欺骗尝试",
        "recommendations": "建议增强对特定波长激光欺骗的检测能力"
    }

def test_secure_boot_verification(target_system):
    """测试安全启动验证机制"""
    # 实际实现中，这里会测试安全启动流程
    return {
        "passed": False,
        "details": "在特定条件下，安全启动验证可以被绕过",
        "recommendations": "修复bootloader中的验证漏洞，增加额外的完整性检查"
    }

# 使用示例（需要导入datetime和json模块）
from datetime import datetime
import json

def example_security_testing():
    # 创建测试实例
    tester = AutomatedSecurityTesting("智能服务机器人系统")
    
    # 添加测试用例
    tester.add_test_case("sensor_security", {
        "name": "传感器欺骗检测测试",
        "description": "测试系统检测传感器欺骗攻击的能力",
        "function": test_sensor_spoofing_detection,
        "severity": "高",
        "prerequisites": ["系统处于测试模式", "传感器接口可访问"]
    })
    
    tester.add_test_case("authentication_security", {
        "name": "安全启动验证测试",
        "description": "验证系统的安全启动机制",
        "function": test_secure_boot_verification,
        "severity": "高",
        "prerequisites": ["系统可重启", "可访问启动日志"]
    })
    
    # 运行测试
    tester.run_tests()
    
    # 生成报告
    report = tester.generate_report()
    print("测试报告已生成")
    return report