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-iocs-from-malware-samplesgit 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-iocs-from-malware-samples)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples/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-iocs-from-malware-samples"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples.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 | $0.00082 | $0.03823 |
| Opus 5 | $0.00041 | $0.01912 |
| Sonnet 5 | $0.00016 | $0.00765 |
| Haiku 4.5 | $0.00008 | $0.00382 |
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.
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}", 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-pythonlibraries 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
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.
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.
- 8d ago First seen · 399 lines · 82 tokens per session scan A 41eec5442e40
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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