extracting-config-from-agent-tesla-rat

从 Agent Tesla RAT 提取配置

概述

Agent Tesla 是一款基于 .NET 的远程访问木马（RAT）和键盘记录器，在 2024 年跻身十大恶意软件变种之列，影响了全球 6.3% 的企业网络。它通过 SMTP 电子邮件、FTP 上传、Telegram bot API 或 Discord webhook 窃取凭据。恶意软件配置嵌入在 .NET 程序集中，通常使用字符串加密、资源加密或自定义加载器进行混淆，这些加载器通过 .NET Reflection（无文件）在内存中解密并执行 Agent Tesla。配置提取涉及使用 dnSpy 或 ILSpy 反编译 .NET 程序集，识别配置字符串的解密例程，并提取 SMTP 服务器地址、凭据、FTP 端点、Telegram bot token 和目标应用程序。

何时使用

• 在执行涉及从 agent tesla rat 提取配置的授权安全测试时
• 在受控环境中分析恶意软件样本或攻击产物时
• 在开展红队演练或渗透测试项目时
• 在基于攻击技术理解构建检测能力时

先决条件

• 用于 .NET 反编译的 dnSpy 或 ILSpy
• Python 3.9+ 搭配 dnlib 或 pythonnet 用于自动化提取
• 用于 .NET 反混淆的 de4dot
• 了解 .NET IL 代码和 Reflection
• 用于动态分析的沙箱（ANY.RUN、CAPE）

工作流

步骤 1：反混淆并提取配置

#!/usr/bin/env python3
"""Extract Agent Tesla RAT configuration from .NET assemblies."""
import re
import sys
import json
import base64
import hashlib
from pathlib import Path


def extract_strings_from_dotnet(filepath):
    """Extract readable strings from .NET binary for config analysis."""
    with open(filepath, 'rb') as f:
        data = f.read()

    # Extract US (User Strings) heap from .NET metadata
    strings = []

    # Look for common Agent Tesla config patterns
    patterns = {
        "smtp_server": re.compile(rb'smtp[\.\-][\w\.\-]+\.\w{2,}', re.I),
        "email": re.compile(rb'[\w\.\-]+@[\w\.\-]+\.\w{2,}'),
        "ftp_url": re.compile(rb'ftp://[\w\.\-:/]+', re.I),
        "telegram_token": re.compile(rb'\d{8,10}:[A-Za-z0-9_-]{35}'),
        "telegram_chat": re.compile(rb'(?:chat_id=|chatid[=:])[\-]?\d{5,15}', re.I),
        "discord_webhook": re.compile(rb'https://discord\.com/api/webhooks/\d+/[\w-]+'),
        "password": re.compile(rb'(?:pass(?:word)?|pwd)[=:]\s*[\w!@#$%^&*]{4,}', re.I),
        "port": re.compile(rb'(?:port|smtp_port)[=:]\s*\d{2,5}', re.I),
    }

    results = {}
    for name, pattern in patterns.items():
        matches = pattern.findall(data)
        if matches:
            results[name] = [m.decode('utf-8', errors='replace') for m in matches]

    # Extract Base64-encoded strings (common obfuscation)
    b64_pattern = re.compile(rb'[A-Za-z0-9+/]{20,}={0,2}')
    b64_decoded = []
    for match in b64_pattern.finditer(data):
        try:
            decoded = base64.b64decode(match.group())
            text = decoded.decode('utf-8', errors='strict')
            if text.isprintable() and len(text) > 5:
                b64_decoded.append(text)
        except Exception:
            pass

    if b64_decoded:
        results["base64_decoded_strings"] = b64_decoded[:30]

    return results


def decrypt_agenttesla_strings(data, key_hex):
    """Decrypt Agent Tesla encrypted configuration strings."""
    key = bytes.fromhex(key_hex)
    # Agent Tesla V1: Simple XOR with key
    decrypted_strings = []

    # Find encrypted blobs (high-entropy byte sequences)
    blob_pattern = re.compile(rb'[\x80-\xff]{16,256}')
    for match in blob_pattern.finditer(data):
        blob = match.group()
        # Try XOR decryption
        decrypted = bytes(b ^ key[i % len(key)] for i, b in enumerate(blob))
        try:
            text = decrypted.decode('utf-8', errors='strict')
            if text.isprintable() and len(text.strip()) > 3:
                decrypted_strings.append(text.strip())
        except UnicodeDecodeError:
            pass

    # V2: SHA256-based key derivation then AES
    sha256_key = hashlib.sha256(key).digest()

    return decrypted_strings


def analyze_exfiltration_config(config):
    """Analyze extracted configuration for exfiltration methods."""
    methods = []

    if config.get("smtp_server"):
        methods.append({
            "type": "SMTP",
            "servers": config["smtp_server"],
            "emails": config.get("email", []),
        })

    if config.get("ftp_url"):
        methods.append({
            "type": "FTP",
            "urls": config["ftp_url"],
        })

    if config.get("telegram_token"):
        methods.append({
            "type": "Telegram",
            "tokens": config["telegram_token"],
            "chat_ids": config.get("telegram_chat", []),
        })

    if config.get("discord_webhook"):
        methods.append({
            "type": "Discord",
            "webhooks": config["discord_webhook"],
        })

    return methods


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print(f"Usage: {sys.argv[0]} <agent_tesla_sample>")
        sys.exit(1)

    config = extract_strings_from_dotnet(sys.argv[1])
    methods = analyze_exfiltration_config(config)

    report = {"raw_config": config, "exfiltration_methods": methods}
    print(json.dumps(report, indent=2))

验证标准

• 已识别窃取方法（SMTP/FTP/Telegram/Discord）
• 已从配置中提取服务器地址和凭据
• 已恢复目标应用程序列表
• 已记录键盘记录器和屏幕截图捕获设置
• 已识别持久化机制
• 已提取适用于网络阻断的 IOC

参考资料

• Splunk - Agent Tesla Detection and Analysis
• Qualys - Catching the RAT Agent Tesla
• ANY.RUN Agent Tesla Analysis
• Trustwave - Agent Tesla Novel Loader
• Malpedia - Agent Tesla