mthcht/awesome-lists: A Curated Arsenal of Detection Lists for SOC and CTI Workflows

What This Repository Actually Offers

The project is a curated collection of detection-oriented lists, not a tool or a framework. It targets SOC analysts, CERT teams, and CTI professionals who need indicators for threat hunting and detection. The README organizes content into two main areas: a threat hunting section with links to external guides and searches, and a 'My Detection Lists' section with dozens of CSV files. These lists cover a wide range of artifacts: suspicious named pipes, TLDs, ASNs, Windows services, tasks, ports, firewall rules, user agents, certificates, USB IDs, mutex names, MAC addresses, hostnames, browser extensions, and more. It also includes ransomware extensions and ransom note names, which are directly useful for DFIR. The repository is primarily YARA in language, but the actual content is mostly CSV data, with YARA rules referenced in a separate linked project.

How the Lists Are Structured and Updated

The lists live under the 'Lists' directory, organized into subdirectories like 'TLDs', 'ASNs', 'CERTS', 'Hijacklibs', 'TOR', 'Drivers', and 'Phishing'. Each list is typically a CSV file with a descriptive name, such as 'suspicious_named_pipe_list.csv' or 'ransomware_extensions_list.csv'. Some lists are marked as 'updated automatically', including suspicious TLDs, ASNs, Hijacklibs, TOR nodes, and malicious SSL certificates. This suggests an automated pipeline, though the README does not describe how it works. The 'big-files' release, dated 2025-03-01, indicates that large datasets are packaged for convenient download, which is practical for users who do not want to clone the entire history. The presence of a Maxmind GeoIP database under 'ASNs/correlation_maxmind_geo_db' suggests that some lists are enriched with geolocation data, but the README does not explain the correlation method.

Getting Started: Commands and Configuration

There is no installation step; you simply clone the repository. Use 'git clone https://github.com/mthcht/awesome-lists.git' or download the 'big-files' release from the GitHub releases page. Once you have the files, you can inspect the CSV lists directly. For example, to see the suspicious named pipes list, open 'Lists/suspicious_named_pipe_list.csv'. To use these in detection, you would import them into your SIEM or EDR. For YARA rules, the README links to a separate repository, 'ThreatHunting-Keywords-yara-rules', which you would clone and compile with your YARA scanner. No configuration files or API keys are mentioned. The main integration effort is mapping each CSV column to your detection platform's fields, which the README does not specify. This means you need to understand your own data schema.

Where This Shines and Where It Falls Short

The strength is breadth and organization. Having a single place for suspicious port lists, Windows service names, and ransomware extensions saves time. The automatic updates for certain lists reduce manual maintenance. However, there are clear limitations. First, the lists are static snapshots; they are only as current as the last update. The 'updated automatically' claim is unverified, and the release date is the only concrete timestamp. Second, the repository is a collection of indicators, not a detection engine. You must build the logic to correlate these lists with your logs. For example, a suspicious TLD list is useless unless you have a field for the TLD in your DNS logs. Third, the README is a long list of links with minimal context. It does not explain the confidence level of each indicator or the false positive potential. A list of suspicious hostnames might include legitimate domains that happen to match a pattern. You need to validate each list against your environment.

How It Compares to Alternative Approaches

The closest alternative is to build your own indicators from threat intelligence feeds, such as MISP or OpenCTI. Those platforms aggregate indicators from multiple sources, provide correlation, and offer APIs for automation. In contrast, this repository is a manual, curated set of lists that you download and integrate. The difference is in workflow: with MISP, you connect your SIEM to a live feed; with awesome-lists, you ingest static files and manage updates yourself. Another alternative is to use vendor-specific threat intel feeds, like Recorded Future or Anomali, which offer richer context and scoring but come with a cost. This repository is free and open, but it lacks the enrichment and scoring that commercial feeds provide. For a small SOC with limited resources, the CSV lists are a practical starting point, but for large-scale operations, a dedicated platform is more sustainable.

Maintenance and License Considerations

The repository is licensed under MIT, which allows reuse and modification with attribution. However, the individual lists may contain data from third-party sources, such as Maxmind GeoIP, which has its own license terms. You need to review those terms before using the lists commercially. The project appears actively maintained, with the last push on 2025-03-01 and a release on the same date. The 'big-files' release suggests a strategy to handle large data files, which is a practical approach for distribution. Upgrade cost is low: you can re-clone or download the latest release to get updates. However, if you have customized the lists for your environment, you will need to merge changes manually. There is no automated update mechanism within the repository itself, so you must schedule regular pulls or downloads. For a security team, this is a manageable but ongoing task.

Who Should Use This and Who Should Not

This repository is ideal for a SOC analyst who needs a quick reference for a specific hunt, such as checking a suspicious service name or a TLD. It is also useful for CTI analysts who want to enrich their reports with concrete indicators. However, it is not for teams that expect a turnkey detection solution. If you lack the engineering capacity to integrate CSV lists into your SIEM, this will be a burden. Also, if you require high-confidence, low-false-positive indicators, you will need to do extra validation. The lists are raw and uncurated in terms of quality; there is no scoring or context. For a mature security operation, this could be a supplementary source, but not the primary one. Before adopting, verify the update frequency and test a few lists against your historical data to see the match rate.

Editorial conclusion

Adopt mthcht/awesome-lists if you are a SOC analyst, DFIR investigator, or CTI analyst who needs ready-made, categorized indicators for hunting and detection engineering. It is not a full detection platform; you must integrate the lists into your SIEM, EDR, or YARA scanner. Before relying on any list, verify the freshness of the 'updated automatically' entries and check the license terms for each sub-list, as the MIT license covers the repository but individual data sources may have their own restrictions. Start by cloning the repo and reviewing the lists under the 'Lists' directory to see which match your use case.

Frequently asked questions

Official sources

1. Official README
2. Project repository
3. Release notes