The Problem
Security operations teams need current, machine-readable lists of suspicious infrastructure—ASNs, IP ranges, domains, TLDs, proxy lists, and VPN endpoints—to feed into SIEM detections, firewall rules, and threat hunting queries. Maintaining these lists manually is error-prone and quickly becomes stale. This repository aggregates dozens of such lists into one place, with automated updates.
What This Does
awesome-lists is a large collection of security indicator lists organized under Lists/. The bulk of the content is data files—over 16,000 JSON and CSV files covering ASN IP ranges, dynamic DNS providers, proxy lists, VPN IPs, and YARA rules. The Lists/ folder also contains the scripts that generate and update these lists, most notably the DYNDNS scraper suite under Lists/DYNDNS/dyn-dns-list-mthchtcorrected/scripts/ with its main.js entry point.
The operational core is the CI/CD pipeline. There are 58 GitHub Actions workflows in .github/workflows/ that refresh lists on schedules—for example, BadTLDs.yml, Proxy list main creation.yml, updatespamhausbadasrange.yml, and vpnnordvpn.yml. Many of these call Python scripts like Lists/ASNs/resolvespamhausranges.py and Lists/VPN/NordVPN/getnordvpnipslist.py.
How To Use It
Setup: Clone the repository. Python scripts have no declared dependency file (requirements.txt is not present), so you'll need to install dependencies manually based on what each script imports. The JavaScript scraper has a package.json in Lists/DYNDNS/dyn-dns-list-mthchtcorrected/scripts/.
git clone https://github.com/moses-y/awesome-lists cd awesome-lists For the JS scraper: cd Lists/DYNDNS/dyn-dns-list-mthchtcorrected/scripts npm install
Configuration: The scraper uses dotenv (declared in package.json), so environment variables go in a .env file in that scripts directory. The exact variable names are not documented in the README.
Running it: There is no single top-level command. Each workflow is self-contained. To reproduce a list update manually, invoke the relevant script—for example, python Lists/ASNs/resolvespamhausranges.py for the Spamhaus ASN ranges. The README does not document per-script usage, so you'll need to read each script to understand its arguments and output paths.
Real-World Use
A SOC analyst could use this repository as a feed source. For example, a scheduled job could pull Lists/ASNs/ASNIPRanges/ CSVs into a firewall or SIEM lookup table to block traffic from known-bad ASNs. The GitHub Actions workflows demonstrate the intended pattern: fetch from upstream sources, transform into CSV, and commit the result back to the repo. You could fork this and modify the workflows to push the generated lists to your own SIEM or threat intel platform instead.
Code Health & Issues
Static analysis of the codebase found 22 issues (1 high, 21 medium) across 7 categories. Key findings: High - Duplicated code blocks: 62 repeated 6-line blocks across 18 files, including Lists/DYNDNS/createdyndnslist.py and Lists/DYNDNS/dyn-dns-list-mthchtcorrected/scripts/scrapers/dnsexit.js. Shared logic should be extracted. Medium - Hub module: scraperUtils.js has 14 dependents; changes there have a wide blast radius. Medium - Oversized file: Lists/Others/splunk/splunkvulncheck.py is 842 lines. Medium - Deep nesting: 12 files exceed indentation depth 6, including Lists/Domains/DNSDB/TOP1MDNSDB.py. Medium - Broad exception handling: 3 files catch exceptions too widely. Medium - Resource safety: Lists/RangesIPAddressCompanyList/IPINFO/getiprangelist.py opens files without a context manager. Medium - High branching density: 3 files, notably Lists/ASNs/resolvespamhaus_ranges.py.
The repo has tests (431 files), CI, and a license. Dependency hygiene is a concern: puppeteer is 3 major versions behind, dotenv and node-fetch are 1 major behind. The analysis flagged secret-shaped paths in YARA rule filenames (e.g., HFLSASecrets.yara); these appear to be rule names, not committed credentials.
The Bottom Line
This is a useful data repository for SOC teams that want pre-assembled indicator lists without building their own collection pipeline. The code quality is mixed—the Python and JavaScript are functional but not polished, and the lack of a top-level dependency manifest makes setup manual. It's best treated as a data source to consume rather than a codebase to extend.