010_具身人工智能的安全评估与测试框架：从静态分析到动态渗透测试

# 2025年具身AI模型对抗样本测试示例
import torch
import torch.nn as nn
import numpy as np
from art.attacks.evasion import FastGradientMethod, ProjectedGradientDescent
from art.classifiers import PyTorchClassifier

class ModelSecurityTester:
    def __init__(self, model, loss_fn, input_shape, nb_classes):
        self.model = model
        self.loss_fn = loss_fn
        self.input_shape = input_shape
        self.nb_classes = nb_classes
        
        # 初始化对抗攻击工具
        self.classifier = self._init_classifier()
    
    def _init_classifier(self):
        # 初始化PyTorch分类器
        classifier = PyTorchClassifier(
            model=self.model,
            loss=self.loss_fn,
            input_shape=self.input_shape,
            nb_classes=self.nb_classes,
            preprocessing=(np.array([0.5, 0.5, 0.5]), np.array([0.5, 0.5, 0.5]))
        )
        return classifier
    
    def test_adversarial_robustness(self, test_data, epsilon_values=[0.01, 0.05, 0.1]):
        # 测试模型对抗攻击的鲁棒性
        results = {}
        
        # 计算干净数据的准确率
        clean_images, clean_labels = test_data
        clean_predictions = np.argmax(self.classifier.predict(clean_images), axis=1)
        clean_accuracy = np.sum(clean_predictions == np.argmax(clean_labels, axis=1)) / len(clean_labels)
        results['clean_accuracy'] = clean_accuracy
        
        # 测试不同强度的FGSM攻击
        results['fgsm'] = {}
        for epsilon in epsilon_values:
            attack = FastGradientMethod(estimator=self.classifier, eps=epsilon)
            x_adv = attack.generate(x=clean_images)
            adv_predictions = np.argmax(self.classifier.predict(x_adv), axis=1)
            adv_accuracy = np.sum(adv_predictions == np.argmax(clean_labels, axis=1)) / len(clean_labels)
            results['fgsm'][f'epsilon_{epsilon}'] = adv_accuracy
        
        # 测试不同强度的PGD攻击
        results['pgd'] = {}
        for epsilon in epsilon_values:
            attack = ProjectedGradientDescent(estimator=self.classifier, eps=epsilon, eps_step=0.01, max_iter=40)
            x_adv = attack.generate(x=clean_images)
            adv_predictions = np.argmax(self.classifier.predict(x_adv), axis=1)
            adv_accuracy = np.sum(adv_predictions == np.argmax(clean_labels, axis=1)) / len(clean_labels)
            results['pgd'][f'epsilon_{epsilon}'] = adv_accuracy
        
        return results
    
    def test_backdoor_robustness(self, clean_data, backdoored_data):
        # 测试模型对后门攻击的鲁棒性
        # 这里省略具体实现，实际应包含后门检测和后门强度评估
        pass

# 2025年具身AI传感器安全测试框架
import cv2
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
from sklearn.decomposition import PCA

class SensorSecurityTester:
    def __init__(self):
        # 初始化测试环境和工具
        self.test_results = {}
        self.attack_signatures = {}
    
    def camera_security_test(self, camera_stream, attack_types=None):
        """测试摄像头对物理对抗样本的敏感性"""
        if attack_types is None:
            attack_types = ['adversarial_patches', 'optical_illusions', 'motion_blur']
        
        results = {}
        
        for attack in attack_types:
            if attack == 'adversarial_patches':
                results[attack] = self._test_adversarial_patches(camera_stream)
            elif attack == 'optical_illusions':
                results[attack] = self._test_optical_illusions(camera_stream)
            elif attack == 'motion_blur':
                results[attack] = self._test_motion_blur(camera_stream)
        
        self.test_results['camera'] = results
        return results
    
    def _test_adversarial_patches(self, camera_stream):
        """测试对抗样本贴片攻击"""
        # 在实际测试中，这里会使用预定义的对抗贴片
        # 这里简化为模拟结果
        return {
            'success_rate': 0.75,
            'detection_threshold': 0.3,
            'vulnerabilities': ['high_contrast', 'certain_shapes'],
            'recommendations': ['implement patch detection', 'use robust recognition models']
        }
    
    def lidar_spoofing_test(self, lidar_system, attack_parameters=None):
        """测试LiDAR对欺骗攻击的抵抗力"""
        if attack_parameters is None:
            attack_parameters = {
                'distance_offsets': [0.1, 0.5, 1.0],
                'angle_deviations': [1, 3, 5],
                'spoofed_objects': ['static', 'moving', 'disappearing']
            }
        
        results = {
            'vulnerability_scores': {},
            'detection_capabilities': {},
            'recommendations': []
        }
        
        # 模拟不同距离偏移的测试结果
        results['vulnerability_scores']['distance_offset'] = {}
        for offset in attack_parameters['distance_offsets']:
            # 在实际测试中，这里会测量系统对距离欺骗的敏感性
            results['vulnerability_scores']['distance_offset'][f'{offset}m'] = 1.0 - min(1.0, offset * 0.8)
        
        # 模拟不同角度偏差的测试结果
        results['vulnerability_scores']['angle_deviation'] = {}
        for angle in attack_parameters['angle_deviations']:
            results['vulnerability_scores']['angle_deviation'][f'{angle}deg'] = 1.0 - min(1.0, angle * 0.15)
        
        # 生成安全建议
        avg_vulnerability = np.mean([v for cat in results['vulnerability_scores'].values() 
                                   for v in cat.values()])
        if avg_vulnerability > 0.6:
            results['recommendations'].append('Implement multi-frequency LiDAR')
            results['recommendations'].append('Add geometric consistency checks')
        
        self.test_results['lidar'] = results
        return results
    
    def imu_jamming_test(self, imu_system, frequency_ranges=None):
        """测试IMU对电磁干扰的敏感性"""
        if frequency_ranges is None:
            frequency_ranges = [(0, 100), (100, 1000), (1000, 10000)]
        
        results = {
            'frequency_response': {},
            'sensitivity_analysis': {},
            'mitigation_effectiveness': {}
        }
        
        # 模拟不同频率范围的干扰测试
        for freq_range in frequency_ranges:
            # 在实际测试中，这里会测量系统在不同频率干扰下的性能
            mid_freq = (freq_range[0] + freq_range[1]) / 2
            results['frequency_response'][f'{freq_range[0]}-{freq_range[1]}Hz'] = {
                'error_magnitude': min(1.0, mid_freq / 10000),
                'recovery_time': max(0.1, mid_freq / 20000)
            }
        
        # 分析最敏感的频率范围
        most_vulnerable = max(
            results['frequency_response'].items(),
            key=lambda x: x[1]['error_magnitude']
        )
        results['sensitivity_analysis']['most_vulnerable_frequency'] = most_vulnerable[0]
        
        self.test_results['imu'] = results
        return results
    
    def sensor_fusion_robustness_test(self, fusion_system, attack_scenarios=None):
        """测试多传感器融合系统的鲁棒性"""
        if attack_scenarios is None:
            attack_scenarios = [
                'single_sensor_compromise',
                'multiple_sensor_mismatch',
                'temporal_synchronization_attack'
            ]
        
        results = {
            'scenario_performance': {},
            'detection_accuracy': {},
            'recovery_capability': {}
        }
        
        # 模拟不同攻击场景的测试结果
        for scenario in attack_scenarios:
            # 在实际测试中，这里会评估融合系统在各种攻击场景下的表现
            if scenario == 'single_sensor_compromise':
                results['scenario_performance'][scenario] = {
                    'detection_rate': 0.85,
                    'false_positive_rate': 0.05,
                    'performance_degradation': 0.2
                }
            elif scenario == 'multiple_sensor_mismatch':
                results['scenario_performance'][scenario] = {
                    'detection_rate': 0.7,
                    'false_positive_rate': 0.1,
                    'performance_degradation': 0.4
                }
            elif scenario == 'temporal_synchronization_attack':
                results['scenario_performance'][scenario] = {
                    'detection_rate': 0.6,
                    'false_positive_rate': 0.15,
                    'performance_degradation': 0.5
                }
        
        # 生成综合评分
        avg_detection = np.mean([s['detection_rate'] for s in results['scenario_performance'].values()])
        avg_degradation = np.mean([s['performance_degradation'] for s in results['scenario_performance'].values()])
        
        results['overall_robustness_score'] = 1.0 - avg_degradation
        
        self.test_results['sensor_fusion'] = results
        return results
    
    def generate_comprehensive_report(self):
        """生成综合安全测试报告"""
        report = {
            'test_summary': {},
            'vulnerability_assessment': {},
            'recommendations': []
        }
        
        # 汇总各类传感器的测试结果
        for sensor_type, results in self.test_results.items():
            if sensor_type == 'camera':
                avg_vulnerability = np.mean([1 - r.get('success_rate', 1) for r in results.values()])
                report['test_summary'][sensor_type] = {
                    'overall_score': avg_vulnerability,
                    'status': 'vulnerable' if avg_vulnerability < 0.5 else 'moderate' if avg_vulnerability < 0.8 else 'secure'
                }
            elif sensor_type in ['lidar', 'imu']:
                avg_vulnerability = np.mean([v for cat in results.get('vulnerability_scores', {}).values() 
                                           for v in cat.values()])
                report['test_summary'][sensor_type] = {
                    'overall_score': avg_vulnerability,
                    'status': 'vulnerable' if avg_vulnerability < 0.5 else 'moderate' if avg_vulnerability < 0.8 else 'secure'
                }
            elif sensor_type == 'sensor_fusion':
                report['test_summary'][sensor_type] = {
                    'overall_score': results.get('overall_robustness_score', 0),
                    'status': 'vulnerable' if results.get('overall_robustness_score', 0) < 0.5 else 
                             'moderate' if results.get('overall_robustness_score', 0) < 0.8 else 'secure'
                }
        
        # 生成最终安全建议
        for sensor_type, summary in report['test_summary'].items():
            if summary['status'] == 'vulnerable':
                report['recommendations'].append(f'Implement enhanced security measures for {sensor_type}')
            elif summary['status'] == 'moderate':
                report['recommendations'].append(f'Improve {sensor_type} security with targeted measures')
        
        return report

### 4.1 物理渗透测试案例研究

#### 医疗机器人物理安全评估案例

某医院部署的手术辅助机器人在物理安全评估中发现了严重问题：

修复措施实施后的安全提升：

| 测试项 | 修复前 | 修复后 | 安全提升 |
|-------|-------|-------|--------|
| 物理访问控制 | 30% | 85% | +55% |
| 防篡改能力 | 20% | 90% | +70% |
| 侧信道防护 | 15% | 75% | +60% |
| EM屏蔽效果 | 25% | 80% | +55% |
| 整体安全评分 | 22.5% | 82.5% | +60% |

### 4.2 物理安全防护最佳实践

### 4.3 互动问答：物理安全挑战

**问题1：在工业环境中部署的具身AI系统，如何平衡物理安全与维护便利性？**

**问题2：对于移动机器人平台，如何设计一个既能抵抗物理攻击又不增加太多重量的防护方案？**

**问题3：侧信道攻击（如功耗分析）在实际环境中有多普遍？企业应该投入多少资源来防范这类攻击？**

### 3. 通信安全测试

验证具身AI通信系统的安全性需要全面检测各类通信接口和协议：

```python
# 2025年具身AI通信安全测试框架
import socket
import ssl
import hashlib
import time
import threading
import logging
import numpy as np
from cryptography.fernet import Fernet
from scapy.all import sniff, sendp, IP, TCP, UDP, ARP

class CommunicationSecurityTester:
    def __init__(self):
        self.test_results = {}
        self.logger = self._setup_logger()
    
    def _setup_logger(self):
        """设置日志记录器"""
        logger = logging.getLogger('comms_security_tester')
        logger.setLevel(logging.INFO)
        handler = logging.StreamHandler()
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        handler.setFormatter(formatter)
        logger.addHandler(handler)
        return logger
    
    def encrypted_communication_test(self, target_host, target_port, timeout=5):
        """测试通信链路的加密强度和完整性保护"""
        results = {
            'encryption_protocol': None,
            'cipher_suite': None,
            'key_strength': None,
            'vulnerabilities': [],
            'security_score': 0
        }
        
        try:
            # 创建SSL上下文
            context = ssl.create_default_context()
            context.check_hostname = False
            context.verify_mode = ssl.CERT_NONE  # 在测试环境中放宽证书验证
            
            with socket.create_connection((target_host, target_port), timeout=timeout) as sock:
                with context.wrap_socket(sock, server_hostname=target_host) as ssock:
                    # 获取SSL连接信息
                    results['encryption_protocol'] = ssock.version()
                    results['cipher_suite'] = ssock.cipher()
                    
                    # 分析加密强度
                    if results['cipher_suite']:
                        cipher_name = results['cipher_suite'][0]
                        key_size = results['cipher_suite'][2]
                        results['key_strength'] = key_size
                        
                        # 评估安全性
                        if key_size < 256:
                            results['vulnerabilities'].append('weak_key_size')
                            results['security_score'] = 0.4
                        elif 'RC4' in cipher_name or '3DES' in cipher_name:
                            results['vulnerabilities'].append('outdated_cipher')
                            results['security_score'] = 0.6
                        elif 'TLSv1.0' in results['encryption_protocol'] or 'TLSv1.1' in results['encryption_protocol']:
                            results['vulnerabilities'].append('outdated_protocol')
                            results['security_score'] = 0.7
                        else:
                            results['security_score'] = 0.9
        except Exception as e:
            self.logger.error(f"加密通信测试失败: {str(e)}")
            results['error'] = str(e)
            results['security_score'] = 0.1
        
        self.test_results['encrypted_communication'] = results
        return results
    
    def protocol_security_test(self, target_host, protocols=None):
        """评估通信协议的安全性和合规性"""
        if protocols is None:
            protocols = ['http', 'mqtt', 'ros', 'grpc']
        
        results = {
            'protocol_analysis': {},
            'compliance_issues': [],
            'recommendations': []
        }
        
        for protocol in protocols:
            protocol_results = self._test_specific_protocol(target_host, protocol)
            results['protocol_analysis'][protocol] = protocol_results
            
            # 收集合规性问题
            if 'vulnerabilities' in protocol_results and protocol_results['vulnerabilities']:
                for vuln in protocol_results['vulnerabilities']:
                    results['compliance_issues'].append(f"{protocol}: {vuln}")
        
        # 生成安全建议
        for protocol, proto_results in results['protocol_analysis'].items():
            if proto_results.get('security_score', 0) < 0.6:
                results['recommendations'].append(f"升级 {protocol} 协议或实施额外的安全层")
            elif proto_results.get('security_score', 0) < 0.8:
                results['recommendations'].append(f"优化 {protocol} 配置以增强安全性")
        
        self.test_results['protocol_security'] = results
        return results
    
    def _test_specific_protocol(self, target_host, protocol):
        """测试特定协议的安全性"""
        results = {
            'security_score': 0,
            'vulnerabilities': [],
            'config_issues': []
        }
        
        # 模拟不同协议的测试结果
        if protocol == 'http':
            results['security_score'] = 0.3  # HTTP默认不安全
            results['vulnerabilities'].append('clear_text_transmission')
            results['config_issues'].append('missing_https_redirect')
        elif protocol == 'mqtt':
            # 假设测试发现部分问题
            results['security_score'] = 0.7
            results['config_issues'].append('weak_password_policy')
            results['config_issues'].append('missing_tls')
        elif protocol == 'ros':
            # 机器人操作系统的典型安全问题
            results['security_score'] = 0.5
            results['vulnerabilities'].append('insecure_defaults')
            results['config_issues'].append('unrestricted_network_access')
        elif protocol == 'grpc':
            # gRPC通常更安全，但可能存在配置问题
            results['security_score'] = 0.8
            results['config_issues'].append('missing_auth')
        
        return results
    
    def man_in_the_middle_simulation(self, network_interface, target_ips, duration=60):
        """测试系统对中间人攻击的抵抗力"""
        results = {
            'attack_success_rate': 0,
            'data_exfiltration': False,
            'encryption_bypass': False,
            'mitigation_effectiveness': 0
        }
        
        try:
            # 启动ARP欺骗攻击模拟
            arp_spoof_thread = threading.Thread(
                target=self._simulate_arp_spoofing,
                args=(network_interface, target_ips, duration)
            )
            arp_spoof_thread.daemon = True
            arp_spoof_thread.start()
            
            # 模拟数据包捕获和分析
            captured_data = self._capture_network_traffic(network_interface, duration)
            
            # 分析攻击效果
            if 'unencrypted_data' in captured_data:
                results['attack_success_rate'] = min(1.0, len(captured_data['unencrypted_data']) / 100)
                results['data_exfiltration'] = True
            
            if 'encrypted_challenges' in captured_data:
                results['encryption_bypass'] = any(not challenge['encrypted'] for challenge in captured_data['encrypted_challenges'])
            
            # 评估缓解措施的有效性
            results['mitigation_effectiveness'] = 1.0 - results['attack_success_rate']
            if results['encryption_bypass']:
                results['mitigation_effectiveness'] *= 0.5
        except Exception as e:
            self.logger.error(f"中间人攻击模拟失败: {str(e)}")
            results['error'] = str(e)
        
        self.test_results['mitm_test'] = results
        return results
    
    def _simulate_arp_spoofing(self, interface, target_ips, duration):
        """模拟ARP欺骗攻击"""
        # 在实际测试中，这里会发送伪造的ARP数据包
        # 这里简化为模拟过程
        end_time = time.time() + duration
        while time.time() < end_time:
            time.sleep(1)  # 模拟攻击间隔
    
    def _capture_network_traffic(self, interface, duration):
        """捕获网络流量进行分析"""
        # 在实际测试中，这里会使用scapy等工具捕获真实流量
        # 这里简化为模拟结果
        return {
            'unencrypted_data': ['sample_data_1', 'sample_data_2'],  # 模拟捕获的未加密数据
            'encrypted_challenges': [
                {'encrypted': True, 'protocol': 'TLSv1.3'},
                {'encrypted': True, 'protocol': 'TLSv1.2'},
                {'encrypted': False, 'protocol': 'HTTP'}  # 模拟一个未加密的连接
            ]
        }
    
    def secure_channel_performance_test(self, target_host, target_port, test_data_size=1024*1024):
        """测试安全通信的性能开销"""
        results = {
            'unencrypted_latency': 0,
            'encrypted_latency': 0,
            'throughput_degradation': 0,
            'cpu_overhead': 0,
            'memory_overhead': 0
        }
        
        try:
            # 测试未加密通信的延迟
            start_time = time.time()
            with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
                sock.connect((target_host, target_port))
                test_data = b'X' * test_data_size
                sock.sendall(test_data)
                response = sock.recv(test_data_size)
            results['unencrypted_latency'] = time.time() - start_time
            
            # 测试加密通信的延迟
            start_time = time.time()
            context = ssl.create_default_context()
            context.check_hostname = False
            context.verify_mode = ssl.CERT_NONE
            with socket.create_connection((target_host, target_port)) as sock:
                with context.wrap_socket(sock, server_hostname=target_host) as ssock:
                    test_data = b'X' * test_data_size
                    ssock.sendall(test_data)
                    response = ssock.recv(test_data_size)
            results['encrypted_latency'] = time.time() - start_time
            
            # 计算性能下降比例
            results['throughput_degradation'] = (results['encrypted_latency'] - results['unencrypted_latency']) / results['unencrypted_latency']
            
            # 模拟CPU和内存开销
            results['cpu_overhead'] = min(0.3, results['throughput_degradation'] * 0.8)
            results['memory_overhead'] = min(0.2, results['throughput_degradation'] * 0.5)
        except Exception as e:
            self.logger.error(f"安全通道性能测试失败: {str(e)}")
            results['error'] = str(e)
        
        self.test_results['performance_test'] = results
        return results
    
    def generate_communication_security_report(self):
        """生成通信安全测试综合报告"""
        report = {
            'test_summary': {},
            'risk_assessment': {},
            'recommendations': []
        }
        
        # 汇总各类测试结果
        for test_type, results in self.test_results.items():
            if test_type == 'encrypted_communication':
                report['test_summary']['encryption_security'] = {
                    'score': results.get('security_score', 0),
                    'status': 'vulnerable' if results.get('security_score', 0) < 0.5 else 
                             'moderate' if results.get('security_score', 0) < 0.8 else 'secure'
                }
            elif test_type == 'protocol_security':
                avg_score = np.mean([p.get('security_score', 0) for p in results.get('protocol_analysis', {}).values()])
                report['test_summary']['protocol_security'] = {
                    'score': avg_score,
                    'status': 'vulnerable' if avg_score < 0.5 else 
                             'moderate' if avg_score < 0.8 else 'secure'
                }
            elif test_type == 'mitm_test':
                report['test_summary']['mitm_resistance'] = {
                    'score': results.get('mitigation_effectiveness', 0),
                    'status': 'vulnerable' if results.get('mitigation_effectiveness', 0) < 0.5 else 
                             'moderate' if results.get('mitigation_effectiveness', 0) < 0.8 else 'secure'
                }
            elif test_type == 'performance_test':
                report['test_summary']['performance_impact'] = {
                    'throughput_degradation': results.get('throughput_degradation', 0),
                    'cpu_overhead': results.get('cpu_overhead', 0),
                    'memory_overhead': results.get('memory_overhead', 0)
                }
        
        # 综合风险评估
        avg_security_score = np.mean([s.get('score', 0) for s in report['test_summary'].values() if isinstance(s, dict) and 'score' in s])
        report['risk_assessment']['overall_security_level'] = {
            'score': avg_security_score,
            'risk_level': 'high' if avg_security_score < 0.5 else 
                         'moderate' if avg_security_score < 0.8 else 'low'
        }
        
        # 生成最终安全建议
        if report['risk_assessment']['overall_security_level']['risk_level'] == 'high':
            report['recommendations'].append('立即实施全面的通信加密')
            report['recommendations'].append('审计并更新所有通信协议')
            report['recommendations'].append('实施网络分段和访问控制')
        elif report['risk_assessment']['overall_security_level']['risk_level'] == 'moderate':
            report['recommendations'].append('加强现有加密措施')
            report['recommendations'].append('修复已发现的协议漏洞')
            report['recommendations'].append('实施入侵检测系统')
        
        return report

# 2025年具身AI物理渗透测试框架
import subprocess
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import logging
import threading
import serial
import smbus2

class PhysicalPenetrationTester:
    def __init__(self):
        self.test_results = {}
        self.logger = self._setup_logger()
    
    def _setup_logger(self):
        """设置日志记录器"""
        logger = logging.getLogger('physical_penetration_tester')
        logger.setLevel(logging.INFO)
        handler = logging.StreamHandler()
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        handler.setFormatter(formatter)
        logger.addHandler(handler)
        return logger
    
    def physical_access_test(self, target_device, access_points=None):
        """测试未授权物理访问的防护措施"""
        if access_points is None:
            access_points = ['main_panel', 'usb_ports', 'network_ports', 'reset_buttons', 'ventilation']
        
        results = {
            'access_points_test': {},
            'security_gaps': [],
            'resistance_score': 0
        }
        
        for point in access_points:
            point_result = self._test_access_point(target_device, point)
            results['access_points_test'][point] = point_result
            
            if point_result.get('vulnerable', False):
                results['security_gaps'].append(f"{point}: {point_result.get('vulnerability_description', '')}")
        
        # 计算物理防护评分
        secure_points = sum(1 for r in results['access_points_test'].values() if not r.get('vulnerable', True))
        results['resistance_score'] = secure_points / len(access_points) if access_points else 0
        
        self.test_results['physical_access'] = results
        return results
    
    def _test_access_point(self, device, access_point):
        """测试特定的物理访问点"""
        # 模拟测试结果
        results = {
            'vulnerable': False,
            'vulnerability_description': '',
            'access_difficulty': 0,  # 0-10，10为最难访问
            'mitigation_effectiveness': 0  # 0-1，1为完全有效
        }
        
        if access_point == 'main_panel':
            results['vulnerable'] = False
            results['access_difficulty'] = 8
            results['mitigation_effectiveness'] = 0.9
        elif access_point == 'usb_ports':
            results['vulnerable'] = True
            results['vulnerability_description'] = '未锁定的USB端口可能被用于恶意设备插入'
            results['access_difficulty'] = 2
            results['mitigation_effectiveness'] = 0.3
        elif access_point == 'network_ports':
            results['vulnerable'] = True
            results['vulnerability_description'] = '未受保护的网络端口可能被用于网络攻击'
            results['access_difficulty'] = 3
            results['mitigation_effectiveness'] = 0.4
        elif access_point == 'reset_buttons':
            results['vulnerable'] = False
            results['access_difficulty'] = 9
            results['mitigation_effectiveness'] = 0.85
        elif access_point == 'ventilation':
            results['vulnerable'] = True
            results['vulnerability_description'] = '通风口可能被用于物理访问或注入恶意物质'
            results['access_difficulty'] = 5
            results['mitigation_effectiveness'] = 0.5
        
        return results
    
    def device_tampering_test(self, target_device, tamper_methods=None):
        """尝试篡改设备并评估检测能力"""
        if tamper_methods is None:
            tamper_methods = ['case_opening', 'component_removal', 'solder_tampering', 'debug_port_access']
        
        results = {
            'tamper_tests': {},
            'detection_capabilities': {},
            'tamper_resistance_score': 0
        }
        
        for method in tamper_methods:
            test_result = self._test_tamper_method(target_device, method)
            results['tamper_tests'][method] = test_result
            
            # 记录篡改检测能力
            if 'detection' in test_result:
                results['detection_capabilities'][method] = test_result['detection']
        
        # 计算防篡改评分
        detected_tampering = sum(1 for d in results['detection_capabilities'].values() if d)
        results['tamper_resistance_score'] = detected_tampering / len(tamper_methods) if tamper_methods else 0
        
        self.test_results['device_tampering'] = results
        return results
    
    def power_analysis(self, target_device, analysis_methods=None):
        """进行功耗分析，评估侧信道安全"""
        if analysis_methods is None:
            analysis_methods = ['simple_power_analysis', 'differential_power_analysis', 'correlation_power_analysis']
        
        results = {
            'power_tests': {},
            'information_leakage_detected': [],
            'security_score': 0
        }
        
        for method in analysis_methods:
            test_result = self._analyze_power_consumption(target_device, method)
            results['power_tests'][method] = test_result
            
            if test_result.get('information_leaked', False):
                results['information_leakage_detected'].append({
                    'method': method,
                    'information_type': test_result.get('information_type', 'unknown'),
                    'severity': test_result.get('severity', 'low')
                })
        
        # 计算安全评分
        secure_analyses = sum(1 for r in results['power_tests'].values() if not r.get('information_leaked', True))
        results['security_score'] = secure_analyses / len(analysis_methods) if analysis_methods else 0
        
        self.test_results['power_analysis'] = results
        return results
    
    def electromagnetic_radiation_analysis(self, target_device, frequency_ranges=None):
        """分析电磁辐射，评估信息泄露风险"""
        if frequency_ranges is None:
            frequency_ranges = [(0, 100), (100, 1000), (1000, 10000), (10000, 100000)]
        
        results = {
            'radiation_tests': {},
            'emission_patterns': [],
            'information_risk_score': 0
        }
        
        for freq_range in frequency_ranges:
            test_result = self._analyze_em_radiation(target_device, freq_range)
            freq_label = f"{freq_range[0]}-{freq_range[1]}Hz"
            results['radiation_tests'][freq_label] = test_result
            
            if test_result.get('suspicious_patterns', False):
                results['emission_patterns'].append({
                    'frequency_range': freq_label,
                    'pattern_type': test_result.get('pattern_type', 'unknown'),
                    'risk_level': test_result.get('risk_level', 'low')
                })
        
        # 计算信息风险评分
        safe_ranges = sum(1 for r in results['radiation_tests'].values() if not r.get('suspicious_patterns', True))
        results['information_risk_score'] = safe_ranges / len(frequency_ranges) if frequency_ranges else 0
        
        self.test_results['em_radiation'] = results
        return results
    
    def generate_physical_security_report(self):
        """生成物理安全测试综合报告"""
        report = {
            'test_summary': {},
            'overall_assessment': {},
            'critical_vulnerabilities': [],
            'recommendations': []
        }
        
        # 汇总各类测试结果
        for test_type, results in self.test_results.items():
            if test_type == 'physical_access':
                report['test_summary']['physical_access_control'] = {
                    'score': results.get('resistance_score', 0),
                    'status': 'vulnerable' if results.get('resistance_score', 0) < 0.5 else 
                             'moderate' if results.get('resistance_score', 0) < 0.8 else 'secure',
                    'security_gaps': results.get('security_gaps', [])
                }
            elif test_type == 'device_tampering':
                report['test_summary']['tamper_resistance'] = {
                    'score': results.get('tamper_resistance_score', 0),
                    'status': 'vulnerable' if results.get('tamper_resistance_score', 0) < 0.5 else 
                             'moderate' if results.get('tamper_resistance_score', 0) < 0.8 else 'secure'
                }
            elif test_type == 'power_analysis':
                report['test_summary']['power_side_channel'] = {
                    'score': results.get('security_score', 0),
                    'status': 'vulnerable' if results.get('security_score', 0) < 0.5 else 
                             'moderate' if results.get('security_score', 0) < 0.8 else 'secure',
                    'leakage_detected': results.get('information_leakage_detected', [])
                }
            elif test_type == 'em_radiation':
                report['test_summary']['em_radiation'] = {
                    'score': results.get('information_risk_score', 0),
                    'status': 'vulnerable' if results.get('information_risk_score', 0) < 0.5 else 
                             'moderate' if results.get('information_risk_score', 0) < 0.8 else 'secure',
                    'suspicious_patterns': results.get('emission_patterns', [])
                }
        
        # 计算总体安全评分
        scores = [s.get('score', 0) for s in report['test_summary'].values()]
        report['overall_assessment']['score'] = np.mean(scores) if scores else 0
        report['overall_assessment']['status'] = 'critical' if report['overall_assessment']['score'] < 0.3 else \
                                               'high_risk' if report['overall_assessment']['score'] < 0.5 else \
                                               'moderate_risk' if report['overall_assessment']['score'] < 0.7 else \
                                               'low_risk' if report['overall_assessment']['score'] < 0.9 else 'secure'
        
        # 生成安全建议
        if report['overall_assessment']['status'] in ['critical', 'high_risk']:
            report['recommendations'].extend([
                '实施全面的物理访问控制系统',
                '加强硬件防篡改机制，如防篡改封条和传感器',
                '实施侧信道攻击防护措施，如电源滤波和电磁屏蔽',
                '定期进行物理安全审计'
            ])
        
        return report

## 安全评估流程与标准

### 评估前准备

```python
# 2025年具身AI安全评估前准备框架
class PreAssessmentManager:
    def __init__(self):
        self.assessment_plan = {}
    
    def requirement_analysis(self, system_under_test, stakeholders):
        """确定评估目标、范围和标准"""
        # 分析系统架构和安全需求
        system_components = self._identify_system_components(system_under_test)
        
        # 识别关键资产和潜在攻击面
        critical_assets = self._identify_critical_assets(system_under_test)
        attack_surfaces = self._identify_attack_surfaces(system_under_test)
        
        # 确定评估目标和范围
        assessment_goals = self._determine_assessment_goals(stakeholders)
        assessment_scope = self._define_assessment_scope(system_components)
        
        return {
            'system_components': system_components,
            'critical_assets': critical_assets,
            'attack_surfaces': attack_surfaces,
            'assessment_goals': assessment_goals,
            'assessment_scope': assessment_scope
        }
    
    def environment_preparation(self, assessment_scope):
        """设置测试环境，准备工具和资源"""
        # 准备测试环境配置
        test_environment = self._setup_test_environment(assessment_scope)
        
        # 准备评估工具集
        assessment_tools = self._prepare_assessment_tools(assessment_scope)
        
        # 准备测试数据集
        test_datasets = self._prepare_test_datasets(assessment_scope)
        
        return {
            'test_environment': test_environment,
            'assessment_tools': assessment_tools,
            'test_datasets': test_datasets
        }
    
    def team_formation(self, assessment_goals):
        """组织跨领域安全专家团队"""
        # 确定所需专业领域
        required_expertise = self._identify_required_expertise(assessment_goals)
        
        # 组建多学科团队
        assessment_team = self._form_multidisciplinary_team(required_expertise)
        
        # 分配团队角色和责任
        team_roles = self._assign_team_roles(assessment_team)
        
        return {
            'required_expertise': required_expertise,
            'assessment_team': assessment_team,
            'team_roles': team_roles
        }
    
    def risk_assessment(self, system_under_test):
        """初步识别潜在风险点"""
        # 执行威胁建模
        threat_model = self._perform_threat_modeling(system_under_test)
        
        # 识别潜在风险点
        potential_risks = self._identify_potential_risks(threat_model)
        
        # 风险优先级排序
        prioritized_risks = self._prioritize_risks(potential_risks)
        
        return {
            'threat_model': threat_model,
            'potential_risks': potential_risks,
            'prioritized_risks': prioritized_risks
        }

安全评估执行流程图：
开始
  ↓
初始化测试环境 → 配置测试工具 → 准备测试数据集
  ↓
┌─────────────────────────────────────┐
│      多维安全评估并行执行          │
├────────────┬─────────────┬─────────┤
│  静态评估  │  动态测试   │ 物理测试 │
│            │             │         │
│ •代码审计  │ •渗透测试   │ •访问控制│
│ •模型审查  │ •模糊测试   │ •设备篡改│
│ •配置检查  │ •漏洞扫描   │ •侧信道分析│
└────────────┴─────────────┴─────────┘
  ↓
数据收集与分析 → 结果验证 → 漏洞确认
  ↓
结束

# 安全评估验证与跟进框架
class SecurityValidationManager:
    def __init__(self):
        self.validation_results = {}
    
    def validate_remediation(self, vulnerabilities, remediation_actions):
        """确认修复措施的有效性"""
        validation_results = {}
        
        for vuln_id, vuln in vulnerabilities.items():
            # 针对每个漏洞验证修复措施
            action = remediation_actions.get(vuln_id)
            if action:
                is_remediated = self._test_remediation_effectiveness(vuln, action)
                validation_results[vuln_id] = {
                    'vulnerability': vuln,
                    'remediation_action': action,
                    'is_remediated': is_remediated,
                    'validation_date': datetime.now().isoformat()
                }
        
        self.validation_results['remediation'] = validation_results
        return validation_results
    
    def continuous_reassessment(self, system_under_test, initial_results):
        """建立定期安全评估机制"""
        # 确定重评估周期
        reassessment_schedule = self._establish_reassessment_schedule(initial_results)
        
        # 制定重评估计划
        reassessment_plan = self._create_reassessment_plan(system_under_test, reassessment_schedule)
        
        # 执行增量评估
        incremental_results = self._perform_incremental_assessment(system_under_test, initial_results)
        
        return {
            'reassessment_schedule': reassessment_schedule,
            'reassessment_plan': reassessment_plan,
            'incremental_results': incremental_results
        }
    
    def establish_monitoring(self, system_under_test, critical_points):
        """实施持续安全监控"""
        # 设置监控点
        monitoring_points = self._setup_monitoring_points(critical_points)
        
        # 配置告警机制
        alert_mechanism = self._configure_alert_system(monitoring_points)
        
        # 实施持续监控
        continuous_monitoring = self._implement_continuous_monitoring(system_under_test, monitoring_points)
        
        return {
            'monitoring_points': monitoring_points,
            'alert_mechanism': alert_mechanism,
            'continuous_monitoring': continuous_monitoring
        }

安全左移效益分析：
┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│ 设计阶段修复  │   │ 开发阶段修复  │   │ 部署后修复   │
│ 成本: $100   │   │ 成本: $1,000  │   │ 成本: $10,000 │
│ 时间: 1天    │   │ 时间: 1周     │   │ 时间: 1个月   │
└───────────────┘   └───────────────┘   └───────────────┘

DevSecOps集成点：
开发阶段 → 构建阶段 → 测试阶段 → 部署阶段 → 运维阶段
   ↓           ↓           ↓           ↓           ↓
代码安全分析 依赖检查    安全测试    配置检查    持续监控
静态应用安全测试  容器扫描   渗透测试    合规检查    异常检测
模型安全审查  基础设施扫描 模糊测试    漏洞管理    威胁狩猎

STRIDE威胁建模在具身AI中的应用：
S - Spoofing（仿冒）
└── 验证身份认证机制，防止传感器信号仿冒

T - Tampering（篡改）
└── 检测和防止传感器数据、控制指令篡改

R - Repudiation（否认）
└── 实施不可否认的操作日志和审计跟踪

I - Information Disclosure（信息泄露）
└── 评估数据传输和存储的保密性

D - Denial of Service（拒绝服务）
└── 测试系统在高负载下的可用性

E - Elevation of Privilege（权限提升）
└── 评估权限管理和隔离机制

具身AI安全评估团队组成：
├── 安全架构师：整体安全策略和框架设计
├── 软件安全专家：代码和应用安全评估
├── 硬件安全专家：物理和侧信道安全测试
├── AI安全专家：模型安全和鲁棒性评估
├── 渗透测试工程师：模拟攻击验证安全措施
├── 合规专家：确保符合相关法规要求
└── 领域专家：提供具身AI业务场景专业知识

AI辅助安全评估工作流：
原始数据 → AI预处理 → 威胁识别 → 漏洞分析 → 报告生成
   ↓           ↓           ↓           ↓           ↓
自动化收集 异常检测   智能分类   风险评估   自动报告
减少人力成本 提高准确率 加速分析过程 优化优先级 标准化输出

实时安全评估架构：
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│  传感器数据流  │ →  │  实时分析引擎  │ →  │  安全决策系统  │
└───────────────┘    └───────────────┘    └───────────────┘
                                                   ↓
                                            ┌───────────────┐
                                            │  自动响应机制  │
                                            └───────────────┘