deepseek-r1 一键自动化渗透

   # autopentest.py
class WorkflowEngine:
    def __init__(self, config):
        self.modules = {
            'pre_engagement': PreEngagement(config),
            'info_gathering': InfoGather(config),
            'threat_modeling': ThreatModeler(config),
            # ...其他模块
        }
        self.workflow = [
            ('pre_engagement', 'validate_scope'),
            ('info_gathering', 'full_scan'),
            ('threat_modeling', 'analyze_attack_surface'),
            # ...其他步骤
        ]

    def execute(self):
        context = {}
        for module_name, method_name in self.workflow:
            module = self.modules[module_name]
            method = getattr(module, method_name)
            context = method(context)
            if context.get('abort'):
                break
        return context

   class InfoGather:
    def __init__(self, config):
        self.tools = {
            'subdomain': SubdomainEnumerator(config),
            'port': PortScanner(config),
            'crawler': AdvancedCrawler(config)
        }
        self.ai_assist = AIScanner(config)

    def full_scan(self, context):
        target = context['target']
        
        # 多线程执行扫描任务
        with ThreadPoolExecutor() as executor:
            futures = {
                executor.submit(self.tools['subdomain'].enumerate, target),
                executor.submit(self.tools['port'].scan, target),
                executor.submit(self.tools['crawler'].crawl, target)
            }
            
            results = {}
            for future in as_completed(futures):
                data = future.result()
                results.update(data)
        
        # AI辅助分析异常特征
        ai_findings = self.ai_assist.analyze(results)
        results.update(ai_findings)
        
        context['scan_results'] = results
        return context

   class AIAnalyzer:
    def __init__(self):
        self.model = load_model('vuln_predict.h5')
        self.threat_intel = ThreatIntelAPI()

    def analyze_vulns(self, scan_data):
        # 特征预处理
        features = self._extract_features(scan_data)
        
        # 预测漏洞可能性
        predictions = self.model.predict(features)
        
        # 关联威胁情报
        enriched_data = []
        for vuln in predictions:
            intel_data = self.threat_intel.query(vuln['cve_id'])
            vuln.update({
                'exploitability': intel_data.get('exploit_score'),
                'patch_status': intel_data.get('patch_info'),
                'recommendation': self._generate_mitigation(vuln)
            })
            enriched_data.append(vuln)
            
        return sorted(enriched_data, key=lambda x: x['risk_score'], reverse=True)

   class AttackChainGenerator:
    def generate_attack_paths(self, context):
        attack_graph = nx.DiGraph()
        
        # 构建攻击路径图
        for vuln in context['vulnerabilities']:
            attack_graph.add_node(vuln['id'], 
                type=vuln['type'], 
                access_level=vuln['access_level'])
            
        # 自动连接相关攻击节点
        for vuln1, vuln2 in combinations(attack_graph.nodes, 2):
            if self._is_connectable(vuln1, vuln2):
                attack_graph.add_edge(vuln1, vuln2)
        
        # 寻找最优攻击路径
        return nx.dag_longest_path(attack_graph)

   class ExploitExecutor:
    def smart_execute(self, vuln_info):
        # 动态选择Payload
        payload = self._select_payload(
            vuln_info['target_env'],
            vuln_info['protection_mechanisms']
        )
        
        # 上下文感知的漏洞利用
        if vuln_info['service'] == 'web':
            return self._web_exploit(vuln_info, payload)
        elif vuln_info['protocol'] == 'smb':
            return self._smb_exploit(vuln_info, payload)
        else:
            return self._generic_exploit(vuln_info, payload)

   class SandboxExecutor:
    def safe_execute(self, payload):
        # 使用Docker创建隔离环境
        with DockerSandbox() as sandbox:
            result = sandbox.run(
                image="isolated_env:latest",
                command=payload,
                timeout=30
            )
            
            # 行为监控和分析
            behavior_report = self.monitor.behavior_analysis(
                result.system_calls,
                result.network_activity
            )
            
            return {
                'output': result.stdout,
                'threat_level': behavior_report['risk_score']
            }

   class SmartReporter:
    def generate_dynamic_report(self, data):
        # 自动生成执行摘要
        summary = self.ai_summarizer.generate_executive_summary(data)
        
        # 漏洞数据可视化
        charts = {
            'risk_distribution': self._create_pie_chart(data),
            'timeline': self._create_attack_timeline(data)
        }
        
        # 生成多格式报告
        report = {
            'html': self._render_html_template(summary, charts),
            'pdf': self._convert_to_pdf(html_report),
            'markdown': self._generate_technical_md(data)
        }
        
        return report