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Ctf Malware

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ljagiello
ctf-malware

Provides malware analysis and network traffic techniques for CTF challenges. Use when analyzing obfuscated scripts, malicious packages, custom crypto protocols, C2 traffic, PE/.NET binaries, RC4/AES encrypted communications, YARA rules, shellcode analysis, memory forensics for malware (Volatility malfind, process injection detection), anti-analysis techniques (VM/sandbox detection, timing evasion, API hashing, process injection, environment checks), or extracting malware configurations and indicators of compromise.

Overview

Publisherljagiello
Repositoryctf-skills
Skill namectf-malware
Stars
3.3K
Forks
380
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ljagiello on GitHub. Read the source before you install it.

Installation

Install the Ctf Malware AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ljagiello/ctf-skills.git /tmp/ctf-skills
mkdir -p .claude/skills
cp -r /tmp/ctf-skills/ctf-malware .claude/skills/ctf-malware
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ctf Malware in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ctf Malware on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ctf Malware is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CTF Malware & Network Analysis

Quick reference for malware analysis CTF challenges. Each technique has a one-liner here; see supporting files for full details with code.

Prerequisites

Python packages (all platforms):

bash
pip install yara-python pefile capstone oletools unicorn pycryptodome \
  volatility3 dissect.cobaltstrike

Linux (apt):

bash
apt install strace ltrace tshark binwalk binutils

macOS (Homebrew):

bash
brew install wireshark binwalk binutils ghidra

Manual install:

  • dnSpy — GitHub, .NET decompiler (Windows)

Additional Resources

  • scripts-and-obfuscation.md - JavaScript deobfuscation, PowerShell analysis, eval/base64 decoding, junk code detection, hex payloads, Debian package analysis, dynamic analysis techniques (strace/ltrace, network monitoring, memory string extraction, automated sandbox execution), YARA rules for malware detection, shellcode analysis (Unicorn Engine, Capstone), memory forensics for malware (Volatility 3 malfind, process injection detection), anti-analysis techniques (VM detection, timing evasion, API hashing, process injection), trojanized plugin analysis with custom alphabet C2 decoding
  • c2-and-protocols.md - C2 traffic patterns, custom crypto protocols, RC4 WebSocket, DNS-based C2, network indicators, PCAP analysis, AES-CBC, encryption ID, Telegram bot recovery, Poison Ivy RAT Camellia decryption
  • pe-and-dotnet.md - PE analysis (peframe, pe-sieve, pestudio), .NET analysis (dnSpy, AsmResolver), LimeRAT extraction, sandbox evasion, malware config extraction, PyInstaller+PyArmor

When to Pivot

  • If the sample is really just a normal crackme, packed challenge binary, or custom VM with no malware behavior, switch to /ctf-reverse.
  • If the main job is network reconstruction, disk carving, or host artifact recovery, switch to /ctf-forensics.
  • If the challenge turns into public attribution or infrastructure tracing, switch to /ctf-osint.

Quick Start Commands

bash
# Static analysis
file suspicious_file
strings -n 8 suspicious_file | head -50
xxd suspicious_file | head -20

# PE analysis
python3 -c "import pefile; pe=pefile.PE('mal.exe'); print(pe.dump_info())" | head
peframe mal.exe

# Dynamic analysis (sandboxed!)
strace -f -s 200 ./suspicious 2>&1 | head -100
ltrace ./suspicious 2>&1 | head -50

# Network indicators
strings suspicious_file | grep -E '[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}'
strings suspicious_file | grep -iE 'http|ftp|ws://'

# YARA scan
yara -r rules.yar suspicious_file

Obfuscated Scripts

  • Replace eval/bash with echo to print underlying code; extract base64/hex blobs and analyze with file. See scripts-and-obfuscation.md.

JavaScript & PowerShell Deobfuscation

  • JS: Replace eval with console.log, decode unescape(), atob(), String.fromCharCode().
  • PowerShell: Decode -enc base64, replace IEX with output. See scripts-and-obfuscation.md.

Junk Code Detection

  • NOP sleds, push/pop pairs, dead writes, unconditional jumps to next instruction. Filter to extract real call targets. See scripts-and-obfuscation.md.

PCAP & Network Analysis

bash
tshark -r file.pcap -Y "tcp.stream eq X" -T fields -e tcp.payload

Look for C2 on unusual ports. Extract IPs/domains with strings | grep. See c2-and-protocols.md.

Custom Crypto Protocols

  • Stream ciphers share keystream state for both directions; concatenate ALL payloads chronologically.
  • ChaCha20 keystream extraction: send nullbytes (0 XOR anything = anything). See c2-and-protocols.md.

C2 Traffic Patterns

  • Beaconing, DGA, DNS tunneling, HTTP(S) with custom headers, encoded payloads. See c2-and-protocols.md.

RC4-Encrypted WebSocket C2

  • Remap port with tcprewrite, add RSA key for TLS decryption, find RC4 key in binary. See c2-and-protocols.md.

Identifying Encryption Algorithms

  • AES: 0x637c777b S-box; ChaCha20: expand 32-byte k; TEA/XTEA: 0x9E3779B9; RC4: sequential S-box init. See c2-and-protocols.md.

AES-CBC in Malware

  • Key = MD5/SHA256 of hardcoded string; IV = first 16 bytes of ciphertext. See c2-and-protocols.md.

PE Analysis

bash
peframe malware.exe      # Quick triage
pe-sieve                 # Runtime analysis
pestudio                 # Static analysis (Windows)

See pe-and-dotnet.md.

.NET Malware Analysis

  • Use dnSpy/ILSpy for decompilation; AsmResolver for programmatic analysis. LimeRAT C2: AES-256-ECB with MD5-derived key. See pe-and-dotnet.md.

Malware Configuration Extraction

  • Check .data section, PE/.NET resources, registry keys, encrypted config files. See pe-and-dotnet.md.

Sandbox Evasion Checks

  • VM detection, debugger detection, timing checks, environment checks, analysis tool detection. See pe-and-dotnet.md.

Anti-Analysis Techniques

VM detection (CPUID, MAC prefix, registry, disk size), timing evasion (sleep/RDTSC sandbox detection), API hashing (ROR13/DJB2/CRC32 + hashdb lookup), process injection (hollowing, APC, CreateRemoteThread), environment checks. See scripts-and-obfuscation.md.

Trojanized Plugin Analysis

Diff malicious plugin against official release to find injected code in try/except blocks. Custom alphabet rotation (C[(C.index(ch) - offset) % len(C)]) decodes C2 domain, XOR decodes endpoint path. See scripts-and-obfuscation.md.

PyInstaller + PyArmor Unpacking

  • pyinstxtractor.py to extract, PyArmor-Unpacker for protected code. See pe-and-dotnet.md.

Telegram Bot Evidence Recovery

  • Use bot token from malware source to call getUpdates and getFile APIs. See c2-and-protocols.md.

Debian Package Analysis

bash
ar -x package.deb && tar -xf control.tar.xz  # Check postinst scripts

See scripts-and-obfuscation.md.

YARA Rules for Malware Detection

Write YARA rules to match byte patterns, strings, and regex against files or memory dumps. Detect XOR loops ({31 ?? 80 ?? ?? 4? 75}), base64 blobs, encoded PowerShell. Use yarac to compile for faster scanning. See scripts-and-obfuscation.md.

Shellcode Analysis

Disassemble with objdump -b binary -m i386:x86-64, emulate with Unicorn Engine (hook syscalls safely), or use Capstone for programmatic disassembly. Look for XOR decoder stubs. See scripts-and-obfuscation.md.

Memory Forensics for Malware

vol windows.malfind detects injected code (PAGE_EXECUTE_READWRITE without mapped file). windows.pstree reveals suspicious parent-child relationships. YARA scan memory with windows.vadyarascan.VadYaraScan. See scripts-and-obfuscation.md.

Network Indicators Quick Reference

bash
strings malware | grep -E '[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}'
tshark -r capture.pcap -Y "dns.qry.name" -T fields -e dns.qry.name | sort -u

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ctf Malware AI skill do?

Provides malware analysis and network traffic techniques for CTF challenges. Use when analyzing obfuscated scripts, malicious packages, custom crypto protocols, C2 traffic, PE/.NET binaries, RC4/AES encrypted communications, YARA rules, shellcode analysis, memory forensics for malware (Volatility malfind, process injection detection), anti-analysis techniques (VM/sandbox detection, timing evasion, API hashing, process injection, environment checks), or extracting malware configurations and indicators of compromise.

Why use Ctf Malware on TypingMind?

Because you install it once and use it with any model. Ctf Malware is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ctf Malware in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ljagiello/ctf-skills/tree/main/ctf-malware. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ctf Malware?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ctf Malware?

As many as you like. As long as a model supports skills, you can use Ctf Malware with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ctf Malware AI skill free?

Yes. It is published on GitHub by ljagiello under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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