specterops/nemesis — explained in plain English
Analysis updated 2026-07-20 · repo last pushed 2026-07-11
Feed hundreds of files extracted from a compromised network into the pipeline instead of opening each one manually.
Surface credentials, Active Directory relationships, and interesting registry keys from raw data collected during an engagement.
Let human analysts and AI assistants collaboratively search through enriched results to find the next attack target.
Centralize scattered security engagement data so a team can connect dots they might otherwise miss.
| specterops/nemesis | future-agi/future-agi | zai-org/scail | |
|---|---|---|---|
| Stars | 986 | 979 | 955 |
| Language | Python | Python | Python |
| Last pushed | 2026-07-11 | — | — |
| Maintenance | Active | — | — |
| Setup difficulty | hard | moderate | hard |
| Complexity | 4/5 | 4/5 | 5/5 |
| Audience | ops devops | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires Docker and Dapr distributed application runtime, with containerized services needing configuration and potentially significant infrastructure resources.
Nemesis is a tool for offensive security teams, the people hired to break into networks the way real attackers would. During these engagements, testers collect a massive pile of files: stolen credentials, registry dumps, configuration files, and more. The project acts as a centralized hub that takes all that scattered data, processes it, and makes it searchable and analyzable so a team can quickly figure out what they've got and where to strike next. Think of it as a private, internal version of VirusTotal built specifically for red teamers and penetration testers. VirusTotal is the public service where anyone can upload a file and see what dozens of antivirus engines think about it. This platform does something similar, but it's designed for the opposite perspective: instead of defending against malware, it helps the attackers understand the files they've pulled from a target environment. It ingests the collected files, enriches them by pulling out useful patterns and structured information, and then lets both human analysts and AI assistants dig through the results collaboratively. The typical users are security consultants working on multi-day engagements. For example, if a tester compromises a workstation and extracts hundreds of files from the corporate network, they can feed everything into this system rather than manually opening each file. The pipeline then surfaces things like credentials, active directory relationships, or interesting registry keys, helping the team connect dots they might otherwise miss in a sea of raw data. The project is built using Docker and integrates heavily with Dapr, a toolkit for building distributed applications. This architecture choice lets it run as a set of containerized services that can process files in parallel and scale up when needed. The README points to a quickstart guide and usage guide for the actual setup and operational details, and the project has been through several iterations, with version 2.0 representing a significant rewrite from the original release.
A data-processing platform for offensive security teams that ingests files collected during network break-ins, extracts useful patterns like credentials and config data, and makes everything searchable for analysts and AI assistants.
Mainly Python. The stack also includes Python, Docker, Dapr.
Active — commit in last 30 days (last push 2026-07-11).
No license information is provided in the explanation, so usage rights are unclear.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
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