agentleFS
naravid19naravid19/claude-ctf-skills/skills/ctf-malware/SKILL.md

ctf-malware

Skill5 starsChanged 8 months agoMITView on GitHub

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.

  • Installs packages

4 other projects use a version of this, 2 of them edited.

Adopted by

Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projectsWeek of Oct 25: 0 new projectsWeek of Nov 1: 0 new projects
Sep 2026Oct 2026Nov 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

Most recently taken up by

The file

We split this skill into the rules it gives an agent

8 rules. About 0 are common, 2 are distinctive, and 6 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 6

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If the sample is really just a normal crackme, packed challenge binary, or custom VM with no malware behavior, switch to /ctf-reverse.

    line 50
  2. Very specific:

    If the main job is network reconstruction, disk carving, or host artifact recovery, switch to /ctf-forensics.

    line 51
  3. Very specific:

    If the challenge turns into public attribution or infrastructure tracing, switch to /ctf-osint.

    line 52
  4. Very specific:

    Look for C2 on unusual ports.

    line 97
  5. Distinctive:

    Use dnSpy/ILSpy for decompilation.

    line 132
  6. Distinctive:

    Check .data section, PE/.NET resources, registry keys, encrypted config files.

    line 136
  7. Very specific:

    Use bot token from malware source to call getUpdates and getFile APIs.

    line 156
  8. Very specific:

    Write YARA rules to match byte patterns, strings, and regex against files or memory dumps.

    line 168

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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):

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

Linux (apt):

apt install strace ltrace tshark binwalk binutils

macOS (Homebrew):

brew install wireshark binwalk binutils ghidra

Manual install:

  • dnSpy — GitHub, .NET decompiler (Windows)

MCP servers (optional): IDA Pro MCP / Ghidra MCP (unpacking, config extraction), WinDbg MCP (svnscha/mcp-windbg) for dynamic analysis, JADX MCP for Android — see tools-mcp.md. Run malware only in an isolated, disposable VM.

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
  • tools-mcp.md - MCP server integration for malware: IDA/Ghidra MCP for unpacking and config/decryptor extraction, WinDbg MCP for dynamic debugging (RWX region hunting, in-memory C2 strings, live VirtualAlloc/CreateRemoteThread breakpoints, .writemem payload dumping to skip the packer), JADX MCP for Android malware — pseudocode-first, isolated-VM safety note

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

# 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

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

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

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

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

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Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.