[!NOTE] Shannon Lite achieves a 96.15% success rate on a hint-free, source-aware XBOW benchmark. →

Shannon is your fully autonomous AI pentester.

Shannon’s job is simple: break your web app before anyone else does. The Red Team to your vibe-coding Blue team. Every Claude (coder) deserves their Shannon.

Website • Discord

🔀 About This Fork

Shannon Max is a fork of the original Shannon by Keygraph, with the following key changes:

• Multi-provider LLM support — Migrated from the Anthropic Claude Agent SDK to the Vercel AI SDK, enabling native support for Anthropic, OpenAI, Google Gemini, DeepSeek, and OpenRouter
• Per-agent model selection — Assign different models to different pipeline phases (e.g., cheap models for recon, powerful models for exploitation)
• 60-95% cost reduction — A full pentest that cost ~$50 with Claude Sonnet 4 now costs ~$3-5 with minimax-m2.5 via OpenRouter
• Shannon owns the tools — All tool execution (bash, file I/O, browser automation) is handled directly by Shannon rather than delegated to a Claude Code subprocess

All credit for Shannon's core architecture, pipeline design, prompt engineering, and security methodology goes to the original Keygraph team. This fork builds on their excellent work.

🎯 What is Shannon?

Shannon is an AI pentester that delivers actual exploits, not just alerts.

Shannon's goal is to break your web app before someone else does. It autonomously hunts for attack vectors in your code, then uses its built-in browser to execute real exploits, such as injection attacks, and auth bypass, to prove the vulnerability is actually exploitable.

What Problem Does Shannon Solve?

Thanks to tools like Claude Code and Cursor, your team ships code non-stop. But your penetration test? That happens once a year. This creates a massive security gap. For the other 364 days, you could be unknowingly shipping vulnerabilities to production.

Shannon closes this gap by acting as your on-demand whitebox pentester. It doesn't just find potential issues. It executes real exploits, providing concrete proof of vulnerabilities. This lets you ship with confidence, knowing every build can be secured.

[!NOTE] From Autonomous Pentesting to Automated Compliance

Shannon is a core component of the Keygraph Security and Compliance Platform.

While Shannon automates the critical task of penetration testing for your application, our broader platform automates your entire compliance journey—from evidence collection to audit readiness. We're building the "Rippling for Cybersecurity," a single platform to manage your security posture and streamline compliance frameworks like SOC 2 and HIPAA.

➡️ Learn more about the Keygraph Platform

🎬 See Shannon in Action

Real Results: Shannon discovered 20+ critical vulnerabilities in OWASP Juice Shop, including complete auth bypass and database exfiltration. See full report →

✨ Features

• Fully Autonomous Operation: Launch the pentest with a single command. The AI handles everything from advanced 2FA/TOTP logins (including sign in with Google) and browser navigation to the final report with zero intervention.
• Pentester-Grade Reports with Reproducible Exploits: Delivers a final report focused on proven, exploitable findings, complete with copy-and-paste Proof-of-Concepts to eliminate false positives and provide actionable results.
• Critical OWASP Vulnerability Coverage: Currently identifies and validates the following critical vulnerabilities: Injection, XSS, SSRF, and Broken Authentication/Authorization, with more types in development.
• Code-Aware Dynamic Testing: Analyzes your source code to intelligently guide its attack strategy, then performs live, browser and command line based exploits on the running application to confirm real-world risk.
• Powered by Integrated Security Tools: Enhances its discovery phase by leveraging leading reconnaissance and testing tools—including Nmap, Subfinder, WhatWeb, and Schemathesis—for deep analysis of the target environment.
• Parallel Processing for Faster Results: Get your report faster. The system parallelizes the most time-intensive phases, running analysis and exploitation for all vulnerability types concurrently.

📦 Product Line

Shannon is available in two editions:

| Edition | License | Best For | |---------|---------|----------| | Shannon Lite | AGPL-3.0 | Security teams, independent researchers, testing your own applications | | Shannon Pro | Commercial | Enterprises requiring advanced features, CI/CD integration, and dedicated support |

This repository contains Shannon Lite, which utilizes our core autonomous AI pentesting framework. Shannon Pro enhances this foundation with an advanced, LLM-powered data flow analysis engine (inspired by the LLMDFA paper) for enterprise-grade code analysis and deeper vulnerability detection.

[!IMPORTANT] White-box only. Shannon Lite is designed for white-box (source-available) application security testing.
It expects access to your application's source code and repository layout.

See feature comparison

📑 Table of Contents

• What is Shannon?
• See Shannon in Action
• Features
• Product Line
• Setup & Usage Instructions
  • Prerequisites
  • Quick Start
  • Monitoring Progress
  • Stopping Shannon
  • Usage Examples
  • Configuration (Optional)
  • Multi-Provider Model Selection
  • Output and Results
• Sample Reports
• Architecture
• Coverage and Roadmap
• Disclaimers
• License
• Community & Support
• Get in Touch

🚀 Setup & Usage Instructions

Shannon Max is designed to be run from inside the repository you are authorised to test. The recommended workflow is now the npm CLI:

cd /path/to/your-app
npx shannon-max https://staging.your-app.com

Shannon mounts the current repository into its Docker worker, reads .env.local or .env from that repository, writes results to ./audit-logs/, and generates deliverables inside ./deliverables/.

Prerequisites

• Docker — Container runtime (Install Docker)
• Node.js 22+ — required for the npx shannon-max CLI
• AI Provider API Key (at least one) — This is the key Shannon's agents use to reason about vulnerabilities:
  • OpenRouter (recommended) — Get from OpenRouter — access many models via a single API, including the budget-friendly minimax-m2.5
  • Anthropic — Get from Anthropic Console
  • OpenAI — Get from OpenAI Platform
  • Google — Get from Google AI Studio
  • DeepSeek — Get from DeepSeek Platform

Quick Start

Step 1: Open the target repository

cd /path/to/your-app

Shannon performs white-box analysis, so run the command from the source checkout you want to assess.

Step 2: Configure a model provider

Create .env.local in the target repository and add your own provider key. OpenRouter is the easiest default:

cat > .env.local <<'EOF'
OPENROUTER_API_KEY=sk-or-...
SHANNON_DEFAULT_MODEL=minimax/minimax-m3
EOF

Do not commit .env.local. Shannon also supports .env and shell environment variables. You can use Anthropic, OpenAI, Google, DeepSeek, or OpenRouter keys.

Step 3: Start the pentest

npx shannon-max https://staging.your-app.com

With an authentication/scoping config:

npx shannon-max https://staging.your-app.com --config ./shannon.yaml

The first run builds the Shannon Docker worker image, so it will take longer than later runs.

Example session

cd ~/code/your-app
npx shannon-max https://staging.your-app.com --config ./configs/shannon.yaml

Running without Claude Code

You can run Shannon Max directly from the command line:

# Basic pentest
npx shannon-max https://example.com

# With a configuration file
npx shannon-max https://example.com --config ./configs/my-config.yaml

# Custom output directory
npx shannon-max https://example.com --output ./my-reports

Monitoring Progress

# View real-time worker logs
npx shannon-max logs <workflow-id>

# Query a specific workflow's progress
npx shannon-max query <workflow-id>

# Open the Temporal Web UI for detailed monitoring
open http://localhost:8233

Stopping Shannon

# Stop all containers (preserves workflow data)
npx shannon-max stop

# Full cleanup (removes all data)
npx shannon-max stop --clean

Prepare Your Repository

For the npm CLI, run Shannon from the repository you want to test:

cd /path/to/your-repo
npx shannon-max https://staging.your-repo.com

You can also pass an explicit repository path:

npx shannon-max https://staging.your-repo.com /path/to/your-repo

For monorepos or multi-repository applications, run from a wrapper directory that contains the relevant services, or pass that wrapper directory explicitly:

mkdir ~/security-targets/your-app
cd ~/security-targets/your-app
git clone https://github.com/your-org/frontend.git
git clone https://github.com/your-org/backend.git
git clone https://github.com/your-org/api.git
npx shannon-max https://staging.your-app.com

Platform-Specific Instructions

For Linux (Native Docker):

You may need to run commands with sudo depending on your Docker setup. If you encounter permission issues with output files, ensure your user has access to the Docker socket.

For macOS:

Works out of the box with Docker Desktop installed.

Testing Local Applications:

Docker containers cannot reach localhost on your host machine. Use host.docker.internal in place of localhost:

npx shannon-max http://host.docker.internal:3000

Configuration (Optional)

While you can run without a config file, creating one enables authenticated testing and customized analysis. Place configuration files inside the target repository and pass them with --config.

Use A Configuration File

Create a YAML config in the target repository, then pass it to Shannon:

npx shannon-max https://example.com --config ./configs/my-app-config.yaml

Basic Configuration Structure

authentication:
  login_type: form
  login_url: "https://your-app.com/login"
  credentials:
    username: "[email protected]"
    password: "yourpassword"
    totp_secret: "LB2E2RX7XFHSTGCK"  # Optional for 2FA

  login_flow:
    - "Type $username into the email field"
    - "Type $password into the password field"
    - "Click the 'Sign In' button"

  success_condition:
    type: url_contains
    value: "/dashboard"

rules:
  avoid:
    - description: "AI should avoid testing logout functionality"
      type: path
      url_path: "/logout"

  focus:
    - description: "AI should emphasize testing API endpoints"
      type: path
      url_path: "/api"

TOTP Setup for 2FA

If your application uses two-factor authentication, simply add the TOTP secret to your config file. The AI will automatically generate the required codes during testing.

Multi-Provider Model Selection

Shannon supports native multi-provider LLM integration via the Vercel AI SDK. Choose the best model for your budget and performance needs.

Available Models

| Model | Provider | Input/Output per 1M tokens | Notes | |-------|----------|---------------------------|-------| | claude-sonnet-4 | Anthropic | $3.00 / $15.00 | Highest quality, default | | gpt-5.2 | OpenAI | $2.50 / $10.00 | Strong alternative | | gpt-5-mini | OpenAI | $0.15 / $0.60 | Fast, budget-friendly | | gemini-2.5-pro | Google | $2.50 / $10.00 | Strong reasoning | | gemini-2.5-flash | Google | $0.10 / $0.40 | Ultra-cheap | | deepseek-v3 | DeepSeek | $0.14 / $0.56 | Budget-friendly | | minimax-m2.5 | OpenRouter | $0.30 / $1.20 | 204K context, low cost |

Configuration

# Set default model for all agents
SHANNON_DEFAULT_MODEL=minimax-m2.5

# Per-agent model overrides (JSON)
# Use cheap models for recon, powerful models for exploitation
SHANNON_MODEL_MAP={"pre-recon":"deepseek-v3","recon":"deepseek-v3","report":"deepseek-v3"}

Cost Example

A full pentest using minimax-m2.5 via OpenRouter costs approximately $3-5 USD compared to $50+ USD with Claude Sonnet 4.

Output and Results

All results are saved to ./audit-logs/{hostname}_{sessionId}/ by default. Use --output <path> to specify a custom directory.

Output structure:

audit-logs/{hostname}_{sessionId}/
├── session.json          # Metrics and session data
├── agents/               # Per-agent execution logs
├── prompts/              # Prompt snapshots for reproducibility
└── deliverables/
    └── comprehensive_security_assessment_report.md   # Final comprehensive security report

📊 Sample Reports

Looking for quantitative benchmarks? See full benchmark methodology and results →

See Shannon's capabilities in action with penetration test results from industry-standard vulnerable applications:

🧃 OWASP Juice Shop • GitHub

A notoriously insecure web application maintained by OWASP, designed to test a tool's ability to uncover a wide range of modern vulnerabilities.

Performance: Identified over 20 high-impact vulnerabilities across targeted OWASP categories in a single automated run.

Key Accomplishments:

• Achieved complete authentication bypass and exfiltrated the entire user database via Injection attack
• Executed a full privilege escalation by creating a new administrator account through a registration workflow bypass
• Identified and exploited systemic authorization flaws (IDOR) to access and modify any user's private data and shopping cart
• Discovered a Server-Side Request Forgery (SSRF) vulnerability, enabling internal network reconnaissance

📄 View Complete Report →

🔗 c{api}tal API • GitHub

An intentionally vulnerable API from Checkmarx, designed to test a tool's ability to uncover the OWASP API Security Top 10.

Performance: Identified nearly 15 critical and high-severity vulnerabilities, leading to full application compromise.

Key Accomplishments:

• Executed a root-level Injection attack by bypassing a denylist via command chaining in a hidden debug endpoint
• Achieved complete authentication bypass by discovering and targeting a legacy, unpatched v1 API endpoint
• Escalated a regular user to full administrator privileges by exploiting a Mass Assignment vulnerability in the user profile update function
• Demonstrated high accuracy by correctly confirming the application's robust XSS defenses, reporting zero false positives

📄 View Complete Report →

🚗 OWASP crAPI • GitHub

A modern, intentionally vulnerable API from OWASP, designed to benchmark a tool's effectiveness against the OWASP API Security Top 10.

Performance: Identified over 15 critical and high-severity vulnerabilities, achieving full application compromise.

Key Accomplishments:

• Bypassed authentication using multiple advanced JWT attacks, including Algorithm Confusion, alg:none, and weak key (kid) injection
• Achieved full database compromise via Injection attacks, exfiltrating user credentials from the PostgreSQL database
• Executed a critical Server-Side Request Forgery (SSRF) attack that successfully forwarded internal authentication tokens to an external service
• Demonstrated high accuracy by correctly identifying the application's robust XSS defenses, reporting zero false positives

📄 View Complete Report →

These results demonstrate Shannon's ability to move beyond simple scanning, performing deep contextual exploitation with minimal false positives and actionable proof-of-concepts.

🏗️ Architecture

Shannon emulates a human penetration tester's methodology using a sophisticated multi-agent architecture. It combines white-box source code analysis with black-box dynamic exploitation across four distinct phases:

                    ┌──────────────────────┐
                    │    Reconnaissance    │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────┴───────────┐
                    │          │           │
                    ▼          ▼           ▼
        ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
        │ Vuln Analysis   │ │ Vuln Analysis   │ │      ...        │
        │  (Injection)    │ │     (XSS)       │ │                 │
        └─────────┬───────┘ └─────────┬───────┘ └─────────┬───────┘
                  │                   │                   │
                  ▼                   ▼                   ▼
        ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
        │  Exploitation   │ │  Exploitation   │ │      ...        │
        │  (Injection)    │ │     (XSS)       │ │                 │
        └─────────┬───────┘ └─────────┬───────┘ └─────────┬───────┘
                  │                   │                   │
                  └─────────┬─────────┴───────────────────┘
                            │
                            ▼
                    ┌──────────────────────┐
                    │      Reporting       │
                    └──────────────────────┘

Architectural Overview

Shannon is engineered to emulate the methodology of a human penetration tester. It leverages the Vercel AI SDK as its core reasoning engine with native multi-provider support (Anthropic, OpenAI, Google, DeepSeek, OpenRouter). Its true strength lies in the sophisticated multi-agent architecture built around it. This architecture combines the deep context of white-box source code analysis with the real-world validation of black-box dynamic exploitation, managed by an orchestrator through four distinct phases to ensure a focus on minimal false positives and intelligent context management.

Phase 1: Reconnaissance

The first phase builds a comprehensive map of the application's attack surface. Shannon analyzes the source code and integrates with tools like Nmap and Subfinder to understand the tech stack and infrastructure. Simultaneously, it performs live application exploration via browser automation to correlate code-level insights with real-world behavior, producing a detailed map of all entry points, API endpoints, and authentication mechanisms for the next phase.

Phase 2: Vulnerability Analysis

To maximize efficiency, this phase operates in parallel. Using the reconnaissance data, specialized agents for each OWASP category hunt for potential flaws in parallel. For vulnerabilities like Injection and SSRF, agents perform a structured data flow analysis, tracing user input to dangerous sinks. This phase produces a key deliverable: a list of hypothesized exploitable paths that are passed on for validation.

Phase 3: Exploitation

Continuing the parallel workflow to maintain speed, this phase is dedicated entirely to turning hypotheses into proof. Dedicated exploit agents receive the hypothesized paths and attempt to execute real-world attacks using browser automation, command-line tools, and custom scripts. This phase enforces a strict "No Exploit, No Report" policy: if a hypothesis cannot be successfully exploited to demonstrate impact, it is discarded as a false positive.

Phase 4: Reporting

The final phase compiles all validated findings into a professional, actionable report. An agent consolidates the reconnaissance data and the successful exploit evidence, cleaning up any noise or hallucinated artifacts. Only verified vulnerabilities are included, complete with reproducible, copy-and-paste Proof-of-Concepts, delivering a final pentest-grade report focused exclusively on proven risks.

📋 Coverage and Roadmap

For detailed information about Shannon's security testing coverage and development roadmap, see our Coverage and Roadmap documentation.

⚠️ Disclaimers

Important Usage Guidelines & Disclaimers

Please review the following guidelines carefully before using Shannon (Lite). As a user, you are responsible for your actions and assume all liability.

1. Potential for Mutative Effects & Environment Selection

This is not a passive scanner. The exploitation agents are designed to actively execute attacks to confirm vulnerabilities. This process can have mutative effects on the target application and its data.

[!WARNING] ⚠️ DO NOT run Shannon on production environments.

• It is intended exclusively for use on sandboxed, staging, or local development environments where data integrity is not a concern.
• Potential mutative effects include, but are not limited to: creating new users, modifying or deleting data, compromising test accounts, and triggering unintended side effects from injection attacks.

2. Legal & Ethical Use

Shannon is designed for legitimate security auditing purposes only.

[!CAUTION] You must have explicit, written authorization from the owner of the target system before running Shannon.

Unauthorized scanning and exploitation of systems you do not own is illegal and can be prosecuted under laws such as the Computer Fraud and Abuse Act (CFAA). Keygraph is not responsible for any misuse of Shannon.

3. LLM & Automation Caveats

• Verification is Required: While significant engineering has gone into our "proof-by-exploitation" methodology to eliminate false positives, the underlying LLMs can still generate hallucinated or weakly-supported content in the final report. Human oversight is essential to validate the legitimacy and severity of all reported findings.
• Comprehensiveness: The analysis in Shannon Lite may not be exhaustive due to the inherent limitations of LLM context windows. For a more comprehensive, graph-based analysis of your entire codebase, Shannon Pro leverages its advanced data flow analysis engine to ensure deeper and more thorough coverage.

4. Scope of Analysis

• Targeted Vulnerabilities: The current version of Shannon Lite specifically targets the following classes of exploitable vulnerabilities:
  • Broken Authentication & Authorization
  • Injection
  • Cross-Site Scripting (XSS)
  • Server-Side Request Forgery (SSRF)
• What Shannon Lite Does Not Cover: This list is not exhaustive of all potential security risks. Shannon Lite's "proof-by-exploitation" model means it will not report on issues it cannot actively exploit, such as vulnerable third-party libraries or insecure configurations. These types of deep static-analysis findings are a core focus of the advanced analysis engine in Shannon Pro.

5. Cost & Performance

• Time: As of the current version, a full test run typically takes 1 to 1.5 hours to complete.
• Cost: Costs vary significantly by model choice. Using Claude Sonnet 4: ~$50 USD. Using minimax-m2.5 via OpenRouter: ~$3-5 USD. See Multi-Provider Model Selection for details.

6. Windows Antivirus False Positives

Windows Defender may flag files in xben-benchmark-results/ or deliverables/ as malware. These are false positives caused by exploit code in the reports. Add an exclusion for the Shannon directory in Windows Defender, or use Docker/WSL2.

📜 License

Shannon Lite is released under the GNU Affero General Public License v3.0 (AGPL-3.0).

Shannon is open source (AGPL v3). This license allows you to:

• Use it freely for all internal security testing.
• Modify the code privately for internal use without sharing your changes.

The AGPL's sharing requirements primarily apply to organizations offering Shannon as a public or managed service (such as a SaaS platform). In those specific cases, any modifications made to the core software must be open-sourced.

👥 Community & Support

Community Resources

Contributing: At this time, we’re not accepting external code contributions (PRs).
Issues are welcome for bug reports and feature requests.

• 🐛 Report bugs via GitHub Issues
• 💡 Suggest features in Discussions
• 💬 Join our Discord for real-time community support

Stay Connected

• 🐦 Twitter: @KeygraphHQ
• 💼 LinkedIn: Keygraph
• 🌐 Website: keygraph.io

💬 Get in Touch

Interested in Shannon Pro?

Shannon Pro is designed for organizations serious about application security. It offers enterprise-grade features, dedicated support, and seamless CI/CD integration, all powered by our most advanced LLM-based analysis engine. Find and fix complex vulnerabilities deep in your codebase before they ever reach production.

For a detailed breakdown of features, technical differences, and enterprise use cases, see our complete comparison guide.

Or contact us directly:

📧 Email: [email protected]