CyberStrike — security grade SAFE, quality 65/100

Security audit verdict: SAFE · quality 65/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by CyberStrikeus · MCP Server · ★ 1.9k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About CyberStrike

English | 简体中文 | 繁體中文 | 한국어 | Deutsch | Español | Français | Italiano | Dansk | 日本語 | Polski | Русский | Bosanski | العربية | Norsk | Português (Brasil) | ไทย | Türkçe | Українська | বাংলা | Ελληνικά | Tiếng Việt | हिन्दी The first open-source AI agent built for offensive security. Automated penetration testing from your terminal — plug in your Claude, GPT, or any LLM subscription and turn it into an autonomous red team agent with 13+ specialize

aiai-agentai-securitybug-bountycybersecuritydevsecopsethical-hackinghackinghacking-toolllm

Quick Facts

Stars1,913
Forks291
LanguageTypeScript
CategoryMCP Server
LicenseAGPL-3.0
Quality Score64.5585582941523/100
Open Issues12
Last Updated2026-08-21
Created2026-02-14
Platformsaws, browser, mcp, node
Est. Tokens~19k

Compatible Skills

These tools work well together with CyberStrike for enhanced workflows:

  • apex — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • red-run — semantic(0.50)+complementary+rare_topics+similar_pop (61%)
  • BugTrace-AI — semantic(0.27)+complementary+rare_topics+same_lang+shared_platform (58%)

CyberStrike alternative? Top 6 similar tools

Looking for a CyberStrike alternative? If you're comparing CyberStrike with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • numasec by FrancescoStabile · ⭐ 466

    The AI Agent for Cyber Security.

  • pentest-ai-agents by 0xSteph · ⭐ 2.1k

    Turn Claude Code into your offensive security research assistant. Specialized AI subagents for authorized pene

  • pentest-ai by 0xSteph · ⭐ 1.7k

    Open-source AI pentester that proves every finding. Machine oracles re-run each exploit; verified bugs ship a

  • agentic-radar by splx-ai · ⭐ 1.1k

    A security scanner for your LLM agentic workflows

  • awesome-hacking-lists by taielab · ⭐ 1.4k

    A curated collection of top-tier penetration testing tools and productivity utilities across multiple domains.

  • h5i by h5i-dev · ⭐ 652

    An agent-native red-teaming workspace with a fast browser, direct HTTP traffic control, sandboxed execution, a

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Frequently Asked Questions

What is CyberStrike?

CyberStrike is Open-source AI-augmented offensive security harness. 13+ autonomous agents, 150+ LLM providers, 5,300+ models, 7,600+ Ed25519-signed attack skills, 56+ built-in tools, 176+ MCP tools. MITRE ATT&CK, OW. It is categorized as a MCP Server with 1.9k GitHub stars.

What programming language is CyberStrike written in?

CyberStrike is primarily written in TypeScript. It covers topics such as ai, ai-agent, ai-security.

How do I install or use CyberStrike?

You can find installation instructions and usage details in the CyberStrike GitHub repository at github.com/CyberStrikeus/CyberStrike. The project has 1.9k stars and 291 forks, indicating an active community.

What license does CyberStrike use?

CyberStrike is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to CyberStrike?

The top alternatives to CyberStrike on Agent Skills Hub include numasec, pentest-ai-agents, pentest-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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