google/google-ctf

Google CTF

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Summary Information

Updated 46 minutes ago
Added to GitGenius on September 13th, 2026
Created on April 27th, 2018
Open Issues & Pull Requests: 1 (+0)
GitHub issues: Enabled
Number of forks: 596
Total Stargazers: 5,027 (+0)
Total Subscribers: 141 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 1.7 hours
Mean response time: 21.8 days
90th percentile: 65.4 days
Tracked items: 3

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Issue Activity (beta)

Open issues: 1
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 127 days
Stale 30+ days: 1
Stale 90+ days: 1

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Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Google CTF is a repository of capture-the-flag challenges and supporting infrastructure used in Google's security competition.

The repository serves as a public archive of CTF challenges spanning multiple years, along with the tooling and infrastructure needed to host and run them. The challenges are organized by year and cover a range of security topics. The repository includes not only the challenge code itself but also the systems required to deploy and manage a CTF event, making it useful both as a learning resource and as a foundation for running similar competitions.

This repository is most valuable for security professionals and event organizers who want to study real-world CTF problems or build their own competition infrastructure. It suits organizations planning to host a CTF event and individuals seeking to understand the types of challenges used in a major security competition. The repository explicitly notes that challenge code in the year-specific folders contains unfixed security vulnerabilities by design, so it should not be deployed on production infrastructure without remediation.

The project maintains a collection of historical challenges with their associated infrastructure code, reflecting an archive-oriented approach to preserving past competition materials. Development activity centers on preserving and documenting challenges from previous years rather than on active feature development or rapid iteration.