Skill 详情

extracting-config-from-agent-tesla-rat

Relevant malware-analysis specialty, but sample-family specific.

匹配类型可能匹配已针对 网络安全 审核
来源mukul975/anthropic-cybersecurity-skills外部来源
报告安装量46仅表示受欢迎程度

使用前先检查

自动化审核只检查相关性,不代表安全审查或推荐。使用前请阅读来源中的说明。

已保存的来源预览

SKILL.md

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: extracting-config-from-agent-tesla-rat
description: Extracts embedded configuration from Agent Tesla RAT samples, including
  SMTP/FTP/Telegram exfiltration credentials, keylogger settings, and C2 endpoints,
  via .NET decompilation and memory analysis. Use when analyzing a suspected or
  confirmed Agent Tesla sample and you need to recover its exfiltration channel
  and C2 configuration for threat intelligence or incident response.
domain: cybersecurity
subdomain: malware-analysis
tags:
- agent-tesla
- rat
- config-extraction
- dotnet
- malware-analysis
- keylogger
- credential-theft
version: '1.0'
author: mahipal
license: Apache-2.0
atlas_techniques:
- AML.T0024
- AML.T0056
- AML.T0086
nist_ai_rmf:
- GOVERN-1.1
- MEASURE-2.7
- MANAGE-3.1
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
mitre_attack:
- T1027
- T1055
- T1140
- T1497
- T1003
---
# Extracting Config from Agent Tesla RAT

## Overview

Agent Tesla is a .NET-based Remote Access Trojan (RAT) and keylogger that ranked among the top 10 malware variants in 2024, impacting 6.3% of corporate networks globally. It exfiltrates stolen credentials via SMTP email, FTP upload, Telegram bot API, or Discord webhooks. The malware configuration is embedded in the .NET assembly, typically obfuscated using string encryption, resource encryption, or custom loaders that decrypt and execute Agent Tesla in memory via .NET Reflection (fileless). Configuration extraction involves decompiling the .NET assembly with dnSpy or ILSpy, identifying the decryption routine for configuration strings, and extracting SMTP server addresses, credentials, FTP endpoints, Telegram bot tokens, and targeted applications.


## When to Use

- When performing authorized security testing that involves extracting config from agent tesla rat
- When analyzing malware samples or attack artifacts in a controlled environment
- When conducting red team exercises or penetration testing engagements
- When building detection capabilities based on offensive technique understanding

## Prerequisites

- dnSpy or ILSpy for .NET decompilation
- Python 3.9+ with `dnlib` or `pythonnet` for automated extraction
- de4dot for .NET deobfuscation
- Understanding of .NET IL code and Reflection
- Sandbox for dynamic analysis (ANY.RUN, CAPE)

## Workflow

### Step 1: Deobfuscate and Extract Configuration

```python
#!/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.
在 GitHub 阅读完整来源 (打开外部页面)
相关上下文

相关工作