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Shannon is an AI pentester for web applications and APIs. It analyzes your source code, identifies attack vectors, and executes real exploits to prove vulnerabilities before they reach production.
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
Traditional penetration testing is infrequent (e.g., once a year), leaving a security gap between releases. Shannon provides on-demand, automated pentesting that can run against every build or release, closing the gap and catching vulnerabilities before they reach production. It also reduces reliance on manual security expertise by automating the entire process.
Deployment reality
The available setup signal starts with: npx @keygraph/shannon setup. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is AGPL-3.0. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build automated security testing pipelines that scan web apps and APIs for vulnerabilities like SQL injection, authentication bypass, IDOR, SSRF, and command injection. The agent can be integrated into CI/CD to test every build, and it produces detailed reports with proof-of-concept exploits. The ceiling includes running comprehensive pentests on complex applications, as demonstrated by reports on OWASP Juice Shop and crAPI, and it can be extended to various AI providers and custom environments.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
KeygraphHQ/shannon is tracked as a TypeScript project in the Developer Tool, Automation, Data Tool area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 47k total stars, with +70 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 10 days ago, and the open issue queue is 31, about 0.07% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 5.4k forks and 47k watchers suggest how often the project is reused or followed. License signal: AGPL-3.0. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need TypeScript teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 70. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyAGPL-3.0 is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
47k Stars
Reuse
5.4k Forks
Attention
47k Watchers
Maintenance
active
License
AGPL-3.0
Open issues
31
Shannon is an autonomous AI pentester for web applications and APIs. It analyzes source code, identifies attack paths, and executes real exploits to prove vulnerabilities before they reach production. This repository is Shannon Open Source, the full agent run locally from the command line.
Key Features
- Proof-by-exploitation reports with reproducible proof-of-concept steps - White-box attack planning using source-code analysis to guide dynamic testing - Autonomous execution from a single command, including reconnaissance, exploitation, and report generation - Authenticated testing with configurable login flows, TOTP, and rules of engagement - OWASP-focused coverage of Injection, XSS, SSRF, Broken Authentication, and Broken Authorization - Resumable workspaces for interrupted runs
Developer Tool
Tools that improve coding, testing, build, and local workflow
Automation
Workflow automation, integration glue, and orchestration
Data Tool
Databases, data pipelines, ETL, analytics, and vector search
npx @keygraph/shannon setup90
Health Score
Active
Commit Activity
Sep 27, 2025
Created
Aug 14, 2026
Last push
+0
Today's growth
+0
7-day growth
+0
30-day growth
Forks
Open
Watchers
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✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
Traditional penetration testing is infrequent (e.g., once a year), leaving a security gap between releases. Shannon provides on-demand, automated pentesting that can run against every build or release, closing the gap and catching vulnerabilities before they reach production. It also reduces reliance on manual security expertise by automating the entire process.
Capabilities
Developers can build automated security testing pipelines that scan web apps and APIs for vulnerabilities like SQL injection, authentication bypass, IDOR, SSRF, and command injection. The agent can be integrated into CI/CD to test every build, and it produces detailed reports with proof-of-concept exploits. The ceiling includes running comprehensive pentests on complex applications, as demonstrated by reports on OWASP Juice Shop and crAPI, and it can be extended to various AI providers and custom environments.
Bottom Line
Shannon is ideal for development teams and security-conscious organizations that want continuous, automated pentesting without manual effort. It is not suitable for those without proper authorization to test targets or who lack the infrastructure (Docker, AI credentials). The key trade-off is automation and speed versus the need for human oversight to ensure ethical use and handle edge cases.