malware-analysis

A malware-analysis guide for examining suspicious programs and scripts, including how they behave and how they try to avoid detection.

In plain words
What is it for?
It helps with malware triage, writing YARA, Snort, or Sigma detection rules, unpacking obfuscated files, investigating incidents, and studying attacker techniques.
Why use it?
It gives developers and security teams a structured way to understand potentially harmful files without relying only on guesswork.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/jessefmoore/offensive-claude-code/malware-analysis
Any agent
npx skills add jessefmoore/offensive-claude-code --skill malware-analysis
Clone the repo
git clone --depth 1 https://github.com/jessefmoore/offensive-claude-code

Made for: Claude Code, Codex.

Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,648 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00028 $0.01648
Opus 5 $0.00014 $0.00824
Sonnet 5 $0.00006 $0.00330
Haiku 4.5 $0.00003 $0.00165

Measured 2d ago against content hash 4ae3753e9b0b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

malware-analysis 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 2d 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.

skills/malware-analysis/SKILL.md · 217 lines

How it starts

The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Malware Analysis

When to Activate

  • Analyzing suspicious binaries or scripts
  • Writing detection signatures (YARA, Snort, Sigma)
  • Understanding malware capabilities and C2 protocols
  • Unpacking protected/obfuscated samples
  • Incident response — determining scope of compromise
  • Threat intelligence — attributing samples to threat actors

Static Analysis

Initial Triage

# File identification
file sample.exe
sha256sum sample.exe
ssdeep sample.exe  # fuzzy hash for similarity

# PE analysis
pestudio sample.exe  # GUI: imports, strings, indicators
python3 -c "import pefile; pe=pefile.PE('sample.exe'); print(pe.dump_info())"

# Strings
floss sample.exe  # FLARE Obfuscated String Solver (decodes obfuscated strings)
strings -n 8 sample.exe | grep -iE '(http|ftp|cmd|powershell|reg|schtask|wmic)'

# Capability detection
capa sample.exe  # maps to MITRE ATT&CK techniques
# Output: persistence/registry, defense-evasion/process-injection, etc.

# Import analysis
python3 -c "
import pefile
pe = pefile.PE('sample.exe')
for entry in pe.DIRECTORY_ENTRY_IMPORT:
    print(entry.dll.decode())
    for imp in entry.imports:
        print(f'  {imp.name.decode() if imp.name else hex(imp.ordinal)}')
"

Suspicious Indicators

# High-confidence malicious:
- VirtualAlloc + WriteProcessMemory + CreateRemoteThread (process injection)
- NtUnmapViewOfSection + NtMapViewOfSection (process hollowing)
- SetWindowsHookEx (keylogger/hooking)
- CryptEncrypt with hardcoded key (ransomware)
- InternetOpen + InternetConnect + HttpSendRequest (C2 communication)
- RegSetValueEx on Run keys (persistence)
- CreateToolhelp32Snapshot + Process32First (process enumeration)

# Packing indicators:
- High entropy sections (>7.0)
- Few imports (only LoadLibrary/GetProcAddress)
- Section names: UPX, .packed, .vmp, .themida
- Entry point in non-standard section

Dynamic Analysis

Sandbox Setup

# Isolated VM with:
# - Snapshot before execution
# - Network capture (inetsim for fake services)
# - Process monitoring (procmon, API Monitor)
# - File system monitoring (sysmon)
# - Registry monitoring

# Inetsim (fake internet services)
inetsim --config /etc/inetsim/inetsim.conf

# FakeDNS
python3 fakedns.py -c 192.168.1.100  # redirect all DNS to analysis host

Read the full file on GitHub · 217 lines

Changes

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.

  1. 2d ago First seen · 217 lines · 28 tokens per session scan A 4ae3753e9b0b

Subscribe to this mod's changes

malware-analysis is a skill published in the GitHub repository jessefmoore/offensive-claude-code (2 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 1,648 once invoked, about $0.0001 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 2026-08-31.

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