extracting-iocs-from-malware-samples

extracting-iocs-from-malware-samples is a skill for Claude Code, Codex from adriannoes/awesome-agentic-ai. It costs 82 tokens per session (3,823 once invoked), scanned A, original, MIT.

A malware-analysis workflow for extracting indicators of compromise (IOCs), such as file hashes, IP addresses, domains, URLs, registry keys, file paths, mutexes, and behavior patterns. These indicators are clues that can help identify or block related attacks.

In plain words
What is it for?
Use it to create blocklists, YARA or network-detection rules, SIEM content, and structured intelligence for systems such as MISP or STIX/TAXII.
Why use it?
Analysis results are not immediately useful to defense teams unless they are turned into concrete detection and threat-intelligence data. Extracting and validating IOCs reduces the chance of sharing misleading indicators.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create blocklists, YARA or network-detection rules, SIEM content, and structured intelligence for systems such as MISP or STIX/TAXII.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples
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.

Any agent
npx skills add adriannoes/awesome-agentic-ai --skill extracting-iocs-from-malware-samples
Clone the repo
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-ai

Made for: Claude Code, Codex.

Wrote 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.

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README.md
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Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,823 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00082 $0.03823
Opus 5 $0.00041 $0.01912
Sonnet 5 $0.00016 $0.00765
Haiku 4.5 $0.00008 $0.00382

Measured 8d ago against content hash 41eec5442e40, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

extracting-iocs-from-malware-samples scanned grade A with 1 finding 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 8d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

resp = requests.get(f"https://www.virustotal.com/api/v3/ip_addresses/{ip}",
cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/extracting-iocs-from-malware-samples/SKILL.md · 399 lines

How it starts

The opening of the file, as written. The whole thing — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Extracting IOCs from Malware Samples

When to Use

  • A malware analysis (static or dynamic) is complete and actionable indicators need to be extracted for defense teams
  • Building blocklists for firewalls, proxies, and DNS sinkholes from analyzed samples
  • Creating YARA rules, Snort/Suricata signatures, or SIEM detection content from malware artifacts
  • Contributing to threat intelligence sharing platforms (MISP, OTX, ThreatConnect)
  • Tracking malware campaigns by correlating IOCs across multiple samples

Do not use for IOCs from unverified sources without validation; false positives in blocklists can disrupt legitimate business operations.

Prerequisites

  • Python 3.8+ with iocextract, pefile, yara-python libraries installed
  • Completed malware analysis report (static analysis, dynamic analysis, or reverse engineering)
  • Access to PCAP files, memory dumps, or sandbox reports from the analysis
  • MISP instance or STIX/TAXII server for structured IOC sharing
  • VirusTotal API key for IOC enrichment and validation
  • CyberChef for decoding obfuscated indicators

Workflow

Step 1: Extract File-Based IOCs

Compute hashes and identify file metadata indicators:

# Generate all standard hashes
md5sum malware_sample.exe
sha1sum malware_sample.exe
sha256sum malware_sample.exe

# Generate ssdeep fuzzy hash for similarity matching
ssdeep malware_sample.exe

# Generate imphash (import hash) for PE files
python3 -c "
import pefile
pe = pefile.PE('malware_sample.exe')
print(f'Imphash: {pe.get_imphash()}')
"

# Generate TLSH (Trend Micro Locality Sensitive Hash)
python3 -c "
import tlsh
with open('malware_sample.exe', 'rb') as f:
    h = tlsh.hash(f.read())
print(f'TLSH: {h}')
"

# Compile file metadata IOCs
python3 << 'PYEOF'
import pefile
import os
import hashlib
import datetime

pe = pefile.PE("malware_sample.exe")

print("FILE IOCs:")
with open("malware_sample.exe", "rb") as f:
    data = f.read()
    print(f"  MD5:        {hashlib.md5(data).hexdigest()}")
    print(f"  SHA-1:      {hashlib.sha1(data).hexdigest()}")
    print(f"  SHA-256:    {hashlib.sha256(data).hexdigest()}")
    print(f"  File Size:  {len(data)} bytes")

ts = pe.FILE_HEADER.TimeDateStamp
print(f"  Compile:    {datetime.datetime.utcfromtimestamp(ts)} UTC")
print(f"  Imphash:    {pe.get_imphash()}")
PYEOF

Read the full file on GitHub · 399 lines

Files

What ships with it

3 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.

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. 8d ago First seen · 399 lines · 82 tokens per session scan A 41eec5442e40

Subscribe to this mod's changes

extracting-iocs-from-malware-samples is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 13d ago), licensed MIT. It adds 82 tokens to every session and 3,823 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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