AI-Security-Guardian：2025年AI安全防护框架的技术突破与实践

# 安全感知层核心代码示例
class SecuritySensor:
    def __init__(self):
        self.input_monitor = InputMonitor()
        self.model_monitor = ModelMonitor()
        self.output_monitor = OutputMonitor()
        self.environment_monitor = EnvironmentMonitor()
    
    def monitor(self, ai_system, input_data=None):
        # 监控AI系统
        monitoring_data = {
            "timestamp": time.time(),
            "ai_system_id": ai_system.id,
            "input": self.input_monitor.monitor(input_data),
            "model": self.model_monitor.monitor(ai_system.model),
            "output": self.output_monitor.monitor(ai_system.last_output),
            "environment": self.environment_monitor.monitor(ai_system.environment)
        }
        return monitoring_data
    
    def start_continuous_monitoring(self, ai_system, interval=5):
        # 启动持续监控
        def monitor_loop():
            while True:
                monitoring_data = self.monitor(ai_system)
                self._send_monitoring_data(monitoring_data)
                time.sleep(interval)
        
        thread = threading.Thread(target=monitor_loop, daemon=True)
        thread.start()
        return thread
    
    def _send_monitoring_data(self, monitoring_data):
        # 发送监控数据到威胁检测层
        # 实际实现中会使用消息队列或API
        pass

# 威胁检测层核心代码示例
class ThreatDetector:
    def __init__(self):
        self.adversarial_detector = AdversarialAttackDetector()
        self.model_theft_detector = ModelTheftDetector()
        self.data_leakage_detector = DataLeakageDetector()
        self.anomaly_detector = AnomalyDetector()
    
    def detect(self, monitoring_data):
        # 检测威胁
        threats = []
        
        # 检测对抗攻击
        adversarial_threat = self.adversarial_detector.detect(monitoring_data)
        if adversarial_threat:
            threats.append(adversarial_threat)
        
        # 检测模型窃取
        model_theft_threat = self.model_theft_detector.detect(monitoring_data)
        if model_theft_threat:
            threats.append(model_theft_threat)
        
        # 检测数据泄露
        data_leakage_threat = self.data_leakage_detector.detect(monitoring_data)
        if data_leakage_threat:
            threats.append(data_leakage_threat)
        
        # 检测异常行为
        anomaly_threat = self.anomaly_detector.detect(monitoring_data)
        if anomaly_threat:
            threats.append(anomaly_threat)
        
        return threats
    
    def update_detection_models(self):
        # 更新检测模型
        self.adversarial_detector.update_model()
        self.model_theft_detector.update_model()
        self.data_leakage_detector.update_model()
        self.anomaly_detector.update_model()
    
    def set_detection_threshold(self, threat_type, threshold):
        # 设置检测阈值
        if threat_type == "adversarial":
            self.adversarial_detector.set_threshold(threshold)
        elif threat_type == "model_theft":
            self.model_theft_detector.set_threshold(threshold)
        elif threat_type == "data_leakage":
            self.data_leakage_detector.set_threshold(threshold)
        elif threat_type == "anomaly":
            self.anomaly_detector.set_threshold(threshold)
        else:
            raise ValueError(f"Unsupported threat type: {threat_type}")

# 防护响应层核心代码示例
class ProtectionResponder:
    def __init__(self):
        self.real_time_protection = RealTimeProtection()
        self.auto_responder = AutoResponder()
        self.isolation_mechanism = IsolationMechanism()
        self.remediation_advisor = RemediationAdvisor()
    
    def respond(self, threats, ai_system):
        # 响应威胁
        responses = []
        
        for threat in threats:
            # 根据威胁类型选择响应策略
            response_strategy = self._get_response_strategy(threat.type)
            
            # 执行响应
            response = response_strategy.execute(threat, ai_system)
            responses.append(response)
        
        return responses
    
    def _get_response_strategy(self, threat_type):
        # 获取响应策略
        strategies = {
            "adversarial": self.real_time_protection,
            "model_theft": self.isolation_mechanism,
            "data_leakage": self.auto_responder,
            "anomaly": self.remediation_advisor
        }
        return strategies.get(threat_type, self.auto_responder)
    
    def set_response_policy(self, threat_type, policy):
        # 设置响应策略
        strategy = self._get_response_strategy(threat_type)
        strategy.set_policy(policy)

# 安全审计层核心代码示例
class SecurityAuditor:
    def __init__(self):
        self.log_recorder = LogRecorder()
        self.report_generator = ReportGenerator()
        self.compliance_checker = ComplianceChecker()
        self.threat_intelligence = ThreatIntelligence()
    
    def record_event(self, event):
        # 记录安全事件
        self.log_recorder.record(event)
    
    def generate_report(self, ai_system_id, time_range):
        # 生成安全报告
        # 1. 获取事件日志
        events = self.log_recorder.get_events(ai_system_id, time_range)
        # 2. 生成报告
        report = self.report_generator.generate(events)
        # 3. 添加合规检查结果
        compliance_result = self.compliance_checker.check(ai_system_id, time_range)
        report["compliance"] = compliance_result
        # 4. 添加威胁情报
        threat_intel = self.threat_intelligence.get_relevant_intel(events)
        report["threat_intelligence"] = threat_intel
        
        return report
    
    def export_report(self, report, format="pdf"):
        # 导出安全报告
        if format == "pdf":
            return self.report_generator.export_pdf(report)
        elif format == "html":
            return self.report_generator.export_html(report)
        elif format == "json":
            return json.dumps(report, indent=2, ensure_ascii=False)
        else:
            raise ValueError(f"Unsupported report format: {format}")
    
    def get_compliance_status(self, ai_system_id):
        # 获取合规状态
        return self.compliance_checker.get_status(ai_system_id)

# 克隆仓库
git clone https://github.com/AI-Security-Guardian/AI-Security-Guardian.git
cd AI-Security-Guardian

# 创建虚拟环境
python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate  # Windows

# 安装依赖
pip install -r requirements.txt

# 配置环境变量
export AISG_CONFIG="./config.yaml"
export AISG_API_KEY="your-api-key"
export AISG_LOG_LEVEL="INFO"

# 启动AI-Security-Guardian服务
python -m aisg serve

# 导入AI-Security-Guardian
from aisg import AISecurityGuardian

# 初始化AI-Security-Guardian
aisg = AISecurityGuardian()

# 注册AI系统
ai_system = aisg.register_ai_system(
    name="我的AI系统",
    type="text_classification",
    framework="pytorch",
    model_path="models/my_model.pt"
)

# 启动安全监控
aisg.start_monitoring(ai_system.id)

# 检测对抗样本
sample_text = "这是一个测试句子"
adversarial_sample = "这是一个测试句⼦"  # 包含对抗扰动

# 检测结果
result1 = aisg.detect_threat(ai_system.id, {"input": sample_text})
result2 = aisg.detect_threat(ai_system.id, {"input": adversarial_sample})

print("正常样本检测结果：")
print(result1)
print("\n对抗样本检测结果：")
print(result2)

# 生成安全报告
report = aisg.generate_report(ai_system.id, {"start_time": "2025-12-01", "end_time": "2025-12-30"})
print("\n安全报告摘要：")
print(f"检测到的威胁数量：{report['threat_count']}")
print(f"主要威胁类型：{report['top_threats']}")
print(f"合规评分：{report['compliance']['score']}")

# 导出报告
pdf_report = aisg.export_report(report, format="pdf")
with open("security_report.pdf", "wb") as f:
    f.write(pdf_report)

print("\n安全报告已导出为security_report.pdf")