awesome lists

Technical Briefing – awesome‑lists Repository: https://github.com/moses-y/awesome-lists.git

The Problem

SOC/DFIR teams need a single source of truth for constantly changing threat‑intel artefacts (malicious IP/ASN ranges, YARA rules, compromised domains, etc.). Maintaining these lists manually is error‑prone and creates gaps in detection coverage.

What This Does

The repo ships >34 k data files (CSV, JSON, YARA) under Lists/. Small helper scripts in Lists/DYNDNS/.../scripts and a handful of Python utilities transform the raw feeds into consumable CSV/JSON outputs. For example, Lists/DYNDNS/dyn‑dns‑list‑mthcht_corrected/scripts/main.js orchestrates the download, parsing, and CSV export of dynamic‑DNS blocklists. Python entry points such as Lists/ASNs/resolve_spamhaus_ranges.py and Lists/Domains/C2IntelFeed/fetch_feeds.py fetch remote feeds, normalise fields, and write the results to the repository‑wide data folders.

How It Is Wired

Key shared modules

• Lists/DYNDNS/dyn‑dns‑list‑mthcht_corrected/scripts/scraperUtils.js – imported by 14 other scraper modules; it contains URL construction and CSV helper functions. Its high fan‑in makes it a stability hotspot.
• log – invoked from 8 locations, providing timestamped console output.

External interactions

• Filesystem writes: 19 functions create or modify CSV/JSON files (e.g., write_csv, atomic_write_text).
• Network calls: 24 functions use fetch/http_download to pull remote lists (e.g., fetch_url, http_get_text).
• External commands: 4 functions invoke shell tools (e.g., git in Lists/TOR/build_tor_history.py).
• One cryptographic helper (download_file in Lists/CERTS/download_certcentral_suspicious_signers_list.py) generates a SHA‑256 hash for integrity checks.

The module graph contains no cycles and 15 import edges across 79 code files, indicating a shallow dependency tree. The most‑connected hub (scraperUtils.js) is pure‑utility code; changes here propagate to every DYNDNS scraper.

How To Use It

# Clone the repository
git clone https://github.com/moses-y/awesome-lists.git
cd awesome-lists

# Install Node‑based DYNDNS tooling
cd Lists/DYNDNS/dyn-dns-list-mthcht_corrected/scripts
npm ci                     # uses package-lock.json
node main.js               # runs the full list build

# Run a Python helper (example: Spamhaus ASN range builder)
cd ../../../../ASNs
python3 resolve_spamhaus_ranges.py

No additional configuration files are referenced in the code; the scripts read remote URLs directly from hard‑coded constants. If you need to adjust a source URL or output path, edit the corresponding constant in the target script (e.g., scrapers/duiadns.js).

Real‑World Use

A SOC can schedule the two entry points above via cron or GitHub Actions. The generated CSVs are then dropped into a SIEM ingestion folder, enabling automated enrichment of alerts with the latest malicious IP/ASN data.

# Example GitHub Action snippet (already present in the repo)
- name: Build DYNDNS lists
  run: |
    cd Lists/DYNDNS/dyn-dns-list-mthcht_corrected/scripts
    npm ci && node main.js

Code Health & Issues

• High – Add a test suite – 79 source files have no associated tests.
• High – Remove committed database dump – Lists/AV signatures/ClamAV/daily.mdb leaks data.
• High – Push to default branch – Workflow .github/workflows/Mullvad VPN IPs and domains update.yml pushes directly without PR review.
• Medium – GITHUB_TOKEN permissions – No explicit permissions: block in the same workflow.
• Medium – Dependabot missing – No automated dependency updates for npm or GitHub Actions.
• Medium – Dependency‑vulnerability gate – No scan step in CI.
• Medium – Pre‑commit secret scan – Secret‑shaped YARA files are tracked without a guard.
• Medium – Large binaries – Files >5 MiB (e.g., Lists/ASNs/get_ip_range_debug.log 99.9 MiB) should be moved to Git LFS or external storage.
• Medium – Checkout persist‑credentials – Workflow keeps the token in .git/config.
• Low – Job timeout – No timeout-minutes set on the Mullvad workflow.

Additional observations from the static audit: duplicated 6‑line code blocks across 18 files, file handles opened without with statements, broad except: clauses, deep nesting (max indentation depth 6), and oversized modules (e.g., splunk_vuln_check.py at 842 lines). These increase maintenance risk.

The Bottom Line

awesome-lists provides a massive, ready‑to‑use collection of threat‑intel artefacts with straightforward scripts to refresh them. The data is valuable, but the codebase suffers from low test coverage, several hygiene lapses (large binaries, committed secrets), and a few maintainability hotspots (the scraperUtils.js hub and duplicated logic). Teams that need raw list data can adopt it quickly, but should first add tests, clean up large assets, and tighten CI security before relying on it in production pipelines.