CyberStrikeus/CyberStrike

Open-source AI-powered offensive security harness for automated penetration testing.

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CyberStrikeus/CyberStrike has 3k+ stars and 500+ forks on GitHub, is written in TypeScript, and was last pushed on Oct 14, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 33 minutes ago
Added to GitGenius on October 6th, 2026
Created on February 14th, 2026
Open Issues & Pull Requests: 30 (+0)
GitHub issues: Enabled
Number of forks: 526
Total Stargazers: 3,186 (+1)
Total Subscribers: 21 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 20.2 hours
Mean response time: 7.2 days
90th percentile: 24.9 days
Tracked items: 52

Maintainer activity

1 person did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 18, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

Roughly one issue in three opened in the past year never receives a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 61% of issues opened in the past year have been closed, leaving a working backlog. Three people close 100% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 30
New in 7 days: 2
Closed in 7 days: 0
Avg open age: 35 days
Stale 30+ days: 26
Stale 90+ days: 0

Recent activity

Opened in 7 days: 2
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (25)
  • enhancement (10)

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Detailed Description

CyberStrike is an open-source AI-powered offensive security harness that automates penetration testing through autonomous agents.

The tool addresses the challenge of conducting comprehensive security assessments by leveraging large language models to orchestrate attack workflows. It works by connecting to LLM providers—supporting Claude, GPT, and other models across multiple providers—and deploying specialized agents that execute security testing tasks. The system includes 13 specialized agents, 7,600 security skills, and 120 OWASP test cases that guide the AI in identifying vulnerabilities. It provides 56 built-in tools and integrates with 176 MCP tools for extended functionality, allowing the AI to interact with various security utilities and services.

Teams conducting penetration testing, bug bounty hunting, red team exercises, or security audits should consider this tool if they want to automate reconnaissance and exploitation workflows. It suits organizations that already use LLM subscriptions and want to repurpose those investments for security testing. The tool offers both terminal-based operation and a web UI for remote access, plus a Bolt feature for remote tool execution. It includes post-exploitation capabilities and supports 150+ AI providers with 5,300+ available models, giving flexibility in choosing which LLM to power the testing.

The project maintains active development with a published build workflow and a community presence on Discord. Documentation is available through a dedicated docs site alongside the main project website. The codebase is written in TypeScript and distributed through npm, making it accessible to developers familiar with JavaScript ecosystems.