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Armur-Ai / Pentest-Swarm-AI

git clone https://github.com/Armur-Ai/Pentest-Swarm-AI.git v0.2.31 Β· 21d ago πŸ”₯ on GitHub Trending today

Autonomous penetration testing using a swarm of AI agents. Orchestrates recon, classification, exploitation, and reporting specialists with ReAct reasoning β€” supports bug bounty, continuous monitoring, and CTF modes. Built with Go and 7+ native security tools.

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stars, 7 days Β· #317
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stars, 30 days Β· #527
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β˜… total Β· +6% this week
559
forks Β· 23 open issues

Star history starts 2026-10-20; the chart appears after the second daily snapshot.

LanguageGo
LicenseAGPL-3.0
Created2024-03-26 (2y ago)
Last push1d ago
Watchers24
OwnerOrganization

Seen on GitHub Trending

  • 2026-10-20 β€” #12 monthly in go Β· +528 β˜…
  • 2026-10-20 β€” #17 weekly in go Β· +176 β˜…

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README excerpt
The open-source XBOW alternative β€” an autonomous API & web-app pentester. 
 Dozens of AI agents don't just scan β€” they exploit . They chain attacks (BOLA/IDOR, JWT forgery, mass assignment, SSRF, injection), build and score attack paths, and prove real vulnerabilities at machine speed. 
 Built for pentesters Β· bug bounty hunters Β· red teamers Β· security researchers 
 πŸ“– Documentation Β·
 Quick Start Β·
 vs. XBOW Β·
 Swarm vs. Multi-Agent Β·
 How It Works Β·
 Roadmap 
## Hack at machine speed.
> πŸ“– **New here? Start with the documentation** β€” quickstart, provider setup, CLI reference, and troubleshooting. (Or run `pentestswarm docs` to open it.)
**XBOW** proved a point: AI can top the bug-bounty leaderboards. But XBOW is a **closed, hosted SaaS** β€” one agent, on their cloud, on their model, at their price. We think the future of offense is **open, self-hosted, and a swarm** β€” so we built it.
### Pentest Swarm vs. XBOW
| | **Pentest Swarm AI** | XBOW |
|---|:---:|:---:|
| **Open source** | βœ… AGPL, fork it | ❌ closed |
| **Self-hosted** | βœ… your infra, your model, $0 floor | ❌ their cloud, their bill |
| **A real swarm** | βœ… dozens of agents, concurrent | ❌ single agent |
Three structural wins β€” and the swarm is the one that compounds. This isn't a single planner LLM walking recon β†’ classify β†’ exploit β†’ report down a fixed line. **Dozens of agents work your surface at once**, coordinating through a shared stigmergic blackboard: the instant a finding lands it wakes whichever agent it's relevant to, so a 1,000-subdomain target gets hit in parallel β€” **attacking at machine speed.** And unlike a scanner that spits out unverified "maybes," the swarm *exploits what it finds, proves it* with captured evidence, then writes the report.
**Who it's for:** pentesters covering a whole scope overnight Β· **bug bounty hunters** racing to first-blood on a fresh target Β· **red teamers** who need breadth fast Β· **researchers** pushing autonomous offense.
Run it on **any model** β€” Claude, anything OpenAI-compatible (incl. **Together AI**'s hosted Llama/Qwen/DeepSeek), the new **security-tuned open models** (Pentest-R1 and the wave behind it), or **fully local Ollama / LM Studio**. We don't compete with those models β€” **we're the harness that gives them hands**: real tools, swarm coordination, scope safety, and evidence-backed reports. Air-gapped, zero API cost, and not one byte of your data leaving your box.
*For **authorized testing only** β€” see the disclaimer below.*
---
> ### Credits & Inspiration
> This project stands on the shoulders of giants. We credit and thank these projects for pioneering AI-powered offensive security:
>
> - **PentestGPT** β€” the OG that proved LLMs can pentest
> - **PentAGI** β€” fully autonomous agent architecture
> - **Strix** β€” AI hackers that find and fix vulns
> - **CAI** β€” cybersecurity AI framework, 3600x faster than humans
> - **HackingBuddyGPT** β€” LLM hacking in 50 lines of code
> - **Shannon** β€” white-box AI pentester
> - **BlacksmithAI** β€”