Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add adriannoes/awesome-agentic-ai --skill extracting-config-from-agent-tesla-ratgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/extracting-config-from-agent-tesla-rat)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/extracting-config-from-agent-tesla-rat"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/extracting-config-from-agent-tesla-rat/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/extracting-config-from-agent-tesla-rat"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/extracting-config-from-agent-tesla-rat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01773 |
| Opus 5 | $0.00024 | $0.00886 |
| Sonnet 5 | $0.00010 | $0.00355 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
extracting-config-from-agent-tesla-rat scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
dnliborpythonnetfor 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
#!/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))
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 206 lines · 48 tokens per session scan A 94e69527da11
extracting-config-from-agent-tesla-rat is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 48 tokens to every session and 1,773 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
importing-a-codebase
Use when the repo holds real source code but no specs: the existing-codebase branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for empty workspaces (starting-a-new-project) or feature work in a specced project (brainstorming).
starting-a-new-project
Use when the workspace is empty — no code yet — and the user brings a raw idea: the brand-new branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for features in an existing project — use brainstorming instead.
todos
This chat has a shared, live TODO plan — your tasks for the conversation, which the user also edits. Read this skill and reach for the todo tools whenever a request takes more than a couple of steps. It covers the plan model (group = task, items = its steps; loose items are the user's lane), how to work it: propose…
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.
brainstorming
Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip this because a change looks small.
reviewing-changes
Use when a review package asks you to review a plan step's change set (todo.startReview): you are the REVIEWER, not the author. How to judge an agent-written diff, file findings with addreviewcomment, and settle with exactly one reviewverdict.