shannon

Thu, 23 Apr 2026 16:58:29 +0800

KeygraphHQ/shannon

[!NOTE] 📢 Sunsetting Router Mode (claude-code-router)`. →

Shannon — AI Pentester by Keygraph

Shannon is an autonomous, white-box 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.

What is Shannon?

Shannon is an AI pentester developed by Keygraph. It performs white-box security testing of web applications and their underlying APIs by combining source code analysis with live exploitation.

Shannon analyzes your web application’s source code to identify potential attack vectors, then uses browser automation and command-line tools to execute real exploits (injection attacks, authentication bypass, SSRF, XSS) against the running application and its APIs. Only vulnerabilities with a working proof-of-concept are included in the final report.

Why Shannon Exists

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 that gap by providing on-demand, automated penetration testing that can run against every build or release.

Shannon in Action

Shannon identified 20+ vulnerabilities in OWASP Juice Shop, including authentication bypass and database exfiltration. Full report →

Features

Fully Autonomous Operation: A single command launches the full pentest. Shannon handles 2FA/TOTP logins (including SSO), browser navigation, exploitation, and report generation without manual intervention.
Reproducible Proof-of-Concept Exploits: The final report contains only proven, exploitable findings with copy-and-paste PoCs. Vulnerabilities that cannot be exploited are not reported.
OWASP Vulnerability Coverage: Identifies and validates Injection, XSS, SSRF, and Broken Authentication/Authorization, with additional categories in development.
Code-Aware Dynamic Testing: Analyzes source code to guide attack strategy, then validates findings with live browser and CLI-based exploits against the running application.
Integrated Security Tooling: Leverages Nmap, Subfinder, WhatWeb, and Schemathesis during reconnaissance and discovery phases.
Parallel Processing: Vulnerability analysis and exploitation phases run concurrently across all attack categories.

Product Line

Shannon is developed by Keygraph and available in two editions:

Edition	License	Best For
Shannon Lite	AGPL-3.0	Local testing of your own applications.
Shannon Pro	Commercial	Organizations needing a single AppSec platform (SAST, SCA, secrets, business logic testing, autonomous pentesting) with CI/CD integration and self-hosted deployment.

This repository contains Shannon Lite, the core autonomous AI pentesting framework. Shannon Pro is Keygraph’s all-in-one AppSec platform, combining SAST, SCA, secrets scanning, business logic security testing, and autonomous AI pentesting in a single correlated workflow. Every finding is validated with a working proof-of-concept exploit.

[!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.

Shannon Pro: Architecture Overview

Shannon Pro is an all-in-one application security platform that replaces the need to stitch together separate SAST, SCA, secrets scanning, and pentesting tools. It operates as a two-stage pipeline: agentic static analysis of the codebase, followed by autonomous AI penetration testing. Findings from both stages are cross-referenced and correlated, so every reported vulnerability has a working proof-of-concept exploit and a precise source code location.

Stage 1: Agentic Static Analysis

Shannon Pro transforms the codebase into a Code Property Graph (CPG) combining the AST, control flow graph, and program dependence graph. It then runs five analysis capabilities:

Data Flow Analysis (SAST): Identifies sources (user input, API requests) and sinks (SQL queries, command execution), then traces paths between them. At each node, an LLM evaluates whether the specific sanitization applied is sufficient for the specific vulnerability in context, rather than relying on a hard-coded allowlist of safe functions.
Point Issue Detection (SAST): LLM-based detection of single-location vulnerabilities: weak cryptography, hardcoded credentials, insecure configuration, missing security headers, weak RNG, disabled certificate validation, and overly permissive CORS.
Business Logic Security Testing (SAST): LLM agents analyze the codebase to discover application-specific invariants (e.g., “document access must verify organizational ownership”), generate targeted fuzzers to violate those invariants, and synthesize full PoC exploits. This catches authorization failures and domain-specific logic errors that pattern-based scanners cannot detect.
SCA with Reachability Analysis: Goes beyond flagging CVEs by tracing whether the vulnerable function is actually reachable from application entry points via the CPG. Unreachable vulnerabilities are deprioritized.
Secrets Detection: Combines regex pattern matching with LLM-based detection (for dynamically constructed credentials, custom formats, obfuscated tokens) and performs liveness validation against the corresponding service using read-only API calls.

Stage 2: Autonomous Dynamic Penetration Testing

The same multi-agent pentest pipeline as Shannon Lite (reconnaissance, parallel vulnerability analysis, parallel exploitation, reporting), enhanced with static findings injected into the exploitation queue. Static findings are mapped to Shannon’s five attack domains (Injection, XSS, SSRF, Auth, Authz), and exploit agents attempt real proof-of-concept attacks against the running application for each finding.

Static-Dynamic Correlation

This is the core differentiator. A data flow vulnerability identified in static analysis (e.g., unsanitized input reaching a SQL query) is not reported as a theoretical risk. It is fed to the corresponding exploit agent, which attempts to exploit it against the live application. Confirmed exploits are traced back to the exact source code location, giving developers both proof of exploitability and the line of code to fix.

Deployment Model

Shannon Pro supports a self-hosted runner model (similar to GitHub Actions self-hosted runners). The data plane, which handles code access and all LLM API calls, runs entirely within the customer’s infrastructure using the customer’s own API keys. Source code never leaves the customer’s network. The Keygraph control plane handles job orchestration, scan scheduling, and the reporting UI, receiving only aggregate findings.

Capability	Shannon Lite	Shannon Pro (All-in-One AppSec)
Licensing	AGPL-3.0	Commercial
Static Analysis	Code review prompting	Full agentic SAST, SCA, secrets, business logic testing
Dynamic Testing	Autonomous AI pentesting	Autonomous AI pentesting with static-dynamic correlation
Analysis Engine	Code review prompting	CPG-based data flow with LLM reasoning at every node
Business Logic	None	Automated invariant discovery, fuzzer generation, exploit synthesis
CI/CD Integration	Manual / CLI	Native CI/CD, GitHub PR scanning
Deployment	CLI	Managed cloud or self-hosted runner
Boundary Analysis	None	Automatic service boundary detection with team routing

Full technical details →

Setup & Usage Instructions

Prerequisites

Docker - Container runtime (Install Docker)
Node.js 18+ - Required for npx usage (Install Node.js)
pnpm - Required for Clone and Build mode (Install pnpm)
AI Provider Credentials (choose one):
- Anthropic API key (recommended) - Get from Anthropic Console
- Claude Code OAuth token
- AWS Bedrock - Route through Amazon Bedrock with AWS credentials (see AWS Bedrock)
- Google Vertex AI - Route through Google Cloud Vertex AI (see Google Vertex AI)

[!NOTE] Docker is still required to use the npx workflow. Under the hood, the CLI pulls and runs a prebuilt Shannon worker image from Docker Hub, which is approximately 1 GB and contains Shannon plus all required dependencies. Shannon mounts the target repository as read-only inside the worker container to protect against accidental modifications during analysis. Run Shannon via npx @keygraph/shannon for the latest released version, or pull the latest main if building from source.

Quick Start (Recommended: npx)

[!WARNING] Please read the Disclaimers before running Shannon. Shannon is not a passive scanner — it actively executes exploits against the target. You must have explicit, written authorization from the system owner.

# 1. Configure credentials (interactive wizard — one-time setup)
npx @keygraph/shannon setup

# Or export env vars directly
export ANTHROPIC_API_KEY=your-api-key

# 2. Run a pentest
npx @keygraph/shannon start -u https://your-app.com -r /path/to/your-repo

Shannon will pull the worker image from Docker Hub, start the infrastructure, and launch an ephemeral worker container for the scan.

Clone and Build

Use this if you want to run Shannon from a local clone, modify Shannon itself, or keep the worker image built locally.

# 1. Clone Shannon
git clone https://github.com/KeygraphHQ/shannon.git
cd shannon

# 2. Configure credentials (choose one method)

# Option A: Create a .env file
cat > .env << 'EOF'
ANTHROPIC_API_KEY=your-api-key
CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000
EOF

# Option B: Export environment variables
export ANTHROPIC_API_KEY="your-api-key"              # or CLAUDE_CODE_OAUTH_TOKEN
export CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000           # recommended

# 3. Install dependencies and build
pnpm install
pnpm build

# 4. Run a pentest
./shannon start -u https://your-app.com -r /path/to/your-repo

Shannon will build the worker image locally, start the infrastructure, and launch an ephemeral worker container for the scan.

Prepare Your Repository

Shannon can scan any repository on your machine. Pass an absolute or relative path with -r.

Examples:

`1`	`npx @keygraph/shannon start -u https://example.com -r /path/to/repo`

Clone and Build command equivalents

`1`	`./shannon start -u https://example.com -r ./relative/path`

Common Commands

Monitoring Progress

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npx @keygraph/shannon logs <workspace>
npx @keygraph/shannon status

Open the Temporal Web UI for detailed monitoring:

`1`	`open http://localhost:8233`

Clone and Build command equivalents

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./shannon logs <workspace>
./shannon status

Stopping Shannon

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npx @keygraph/shannon stop
npx @keygraph/shannon stop --clean
npx @keygraph/shannon uninstall

Clone and Build command equivalents

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./shannon stop
./shannon stop --clean

Usage Examples

# Basic pentest
npx @keygraph/shannon start -u https://example.com -r /path/to/repo

# With a configuration file
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -c /path/to/my-config.yaml

# Custom output directory
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -o ./my-reports

# Named workspace
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w q1-audit

# List all workspaces
npx @keygraph/shannon workspaces

Clone and Build command equivalents

# Basic pentest
./shannon start -u https://example.com -r /path/to/repo

# With a configuration file
./shannon start -u https://example.com -r /path/to/repo -c /path/to/my-config.yaml

# Custom output directory
./shannon start -u https://example.com -r /path/to/repo -o ./my-reports

# Named workspace
./shannon start -u https://example.com -r /path/to/repo -w q1-audit

# List all workspaces
./shannon workspaces

# Rebuild worker image
./shannon build --no-cache

Workspaces and Resuming

Shannon supports workspaces that allow you to resume interrupted or failed runs without re-running completed agents.

How it works:

Every run creates a workspace (auto-named by default, for example example-com_shannon-1771007534808)
Workspaces are stored in ./workspaces/ (local mode) or ~/.shannon/workspaces/ (npx mode)
Use -w <name> to give your run a custom name for easier reference
To resume any run, pass its workspace name via -w — Shannon detects which agents completed successfully and picks up where it left off
Each agent’s progress is checkpointed via git commits, so resumed runs start from a clean, validated state

# Start with a named workspace
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w my-audit

# Resume the same workspace (skips completed agents)
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w my-audit

# Resume an auto-named workspace from a previous run
npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w example-com_shannon-1771007534808

# List all workspaces and their status
npx @keygraph/shannon workspaces

Clone and Build command equivalents

./shannon start -u https://example.com -r /path/to/repo -w my-audit
./shannon start -u https://example.com -r /path/to/repo -w my-audit
./shannon start -u https://example.com -r /path/to/repo -w example-com_shannon-1771007534808
./shannon workspaces

[!NOTE] The URL must match the original workspace URL when resuming. Shannon will reject mismatched URLs to prevent cross-target contamination.

Credentials and Configuration

Credential Precedence

Local mode resolves credentials from:

Environment variables - export ANTHROPIC_API_KEY=...
.env file - ./.env

npx mode uses TOML instead of .env:

Environment variables - export ANTHROPIC_API_KEY=...
~/.shannon/config.toml - created by npx @keygraph/shannon setup

Environment variables always win, so you can override saved config for a single session without editing files.

Configuration (Optional)

While you can run without a config file, creating one enables authenticated testing and customized analysis. Pass any configuration file path with -c.

Create Configuration File

Copy and modify the example configuration:

`1`	`cp configs/example-config.yaml ./my-app-config.yaml`

Basic Configuration Structure

# Optional: describe your target environment (max 500 chars)
description: "Next.js e-commerce app on PostgreSQL. Local dev environment — .env files contain local-only credentials, not deployed to production."

authentication:
  login_type: form
  login_url: "https://your-app.com/login"
  credentials:
    username: "test@example.com"
    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"

Run with:

`1`	`npx @keygraph/shannon start -u https://example.com -r /path/to/repo -c ./my-app-config.yaml`

Clone and Build command equivalents

`1`	`./shannon start -u https://example.com -r /path/to/repo -c ./my-app-config.yaml`

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.

Subscription Plan Rate Limits

Anthropic subscription plans reset usage on a rolling 5-hour window. The default retry strategy (30-min max backoff) will exhaust retries before the window resets. Add this to your config:

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pipeline:
  retry_preset: subscription          # Extends max backoff to 6h, 100 retries
  max_concurrent_pipelines: 2         # Run 2 of 5 pipelines at a time (reduces burst API usage)

max_concurrent_pipelines controls how many vulnerability pipelines run simultaneously (1-5, default: 5). Lower values reduce the chance of hitting rate limits but increase wall-clock time.

AWS Bedrock

Shannon also supports Amazon Bedrock instead of using an Anthropic API key.

Quick Setup

Run npx @keygraph/shannon setup and select AWS Bedrock. The wizard will prompt for your region, bearer token, and model IDs.

Or export env vars directly:

export CLAUDE_CODE_USE_BEDROCK=1
export AWS_REGION=us-east-1
export AWS_BEARER_TOKEN_BEDROCK=your-bearer-token
export ANTHROPIC_SMALL_MODEL=us.anthropic.claude-haiku-4-5-20251001-v1:0
export ANTHROPIC_MEDIUM_MODEL=us.anthropic.claude-sonnet-4-6
export ANTHROPIC_LARGE_MODEL=us.anthropic.claude-opus-4-6

Clone and Build: add to .env instead

CLAUDE_CODE_USE_BEDROCK=1
AWS_REGION=us-east-1
AWS_BEARER_TOKEN_BEDROCK=your-bearer-token
ANTHROPIC_SMALL_MODEL=us.anthropic.claude-haiku-4-5-20251001-v1:0
ANTHROPIC_MEDIUM_MODEL=us.anthropic.claude-sonnet-4-6
ANTHROPIC_LARGE_MODEL=us.anthropic.claude-opus-4-6

Shannon uses three model tiers: small (claude-haiku-4-5-20251001) for summarization, medium (claude-sonnet-4-6) for security analysis, and large (claude-opus-4-6) for deep reasoning. Set ANTHROPIC_SMALL_MODEL, ANTHROPIC_MEDIUM_MODEL, and ANTHROPIC_LARGE_MODEL to the Bedrock model IDs for your region.

Google Vertex AI

Shannon also supports Google Vertex AI instead of using an Anthropic API key.

Create a service account with the roles/aiplatform.user role in the GCP Console, then download a JSON key file.

Quick Setup

Run npx @keygraph/shannon setup and select Google Vertex AI. The wizard will prompt for your region, project ID, service account key file path, and model IDs. The key file is securely copied to ~/.shannon/google-sa-key.json.

Or export env vars directly:

export CLAUDE_CODE_USE_VERTEX=1
export CLOUD_ML_REGION=us-east5
export ANTHROPIC_VERTEX_PROJECT_ID=your-gcp-project-id
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your-sa-key.json
export ANTHROPIC_SMALL_MODEL=claude-haiku-4-5@20251001
export ANTHROPIC_MEDIUM_MODEL=claude-sonnet-4-6
export ANTHROPIC_LARGE_MODEL=claude-opus-4-6

Clone and Build: add to .env instead

CLAUDE_CODE_USE_VERTEX=1
CLOUD_ML_REGION=us-east5
ANTHROPIC_VERTEX_PROJECT_ID=your-gcp-project-id
GOOGLE_APPLICATION_CREDENTIALS=./credentials/google-sa-key.json
ANTHROPIC_SMALL_MODEL=claude-haiku-4-5@20251001
ANTHROPIC_MEDIUM_MODEL=claude-sonnet-4-6
ANTHROPIC_LARGE_MODEL=claude-opus-4-6

Set CLOUD_ML_REGION=global for global endpoints, or a specific region like us-east5. Some models may not be available on global endpoints — see the Vertex AI Model Garden for region availability.

Custom Base URL

Shannon supports pointing the SDK at any Anthropic-compatible endpoint via ANTHROPIC_BASE_URL. For users who need proxy-based routing, the supported path is to use an LLM proxy such as LiteLLM configured to expose an Anthropic-compatible endpoint.

[!IMPORTANT] Only Claude models are officially supported. Shannon’s evaluations, internal testing, and agent harness are all optimized for Claude. Smaller or alternative models — including non-Claude models routed through a proxy — may not reliably follow Shannon’s instructions or tool-use constraints, and are not officially supported. Use them at your own risk; results may be incomplete, inaccurate, or unstable.

The previously experimental claude-code-router integration is being removed in an upcoming release. If you currently rely on it, migrate to an Anthropic-compatible proxy such as LiteLLM before upgrading.

Run npx @keygraph/shannon setup and select Custom Base URL. The wizard will prompt for your endpoint URL, auth token, and optionally let you override the default model tiers.

Or export env vars directly:

export ANTHROPIC_BASE_URL=https://your-proxy.example.com
export ANTHROPIC_AUTH_TOKEN=your-auth-token

# Optionally override model tiers (defaults are used if not set)
export ANTHROPIC_SMALL_MODEL=claude-haiku-4-5-20251001
export ANTHROPIC_MEDIUM_MODEL=claude-sonnet-4-6
export ANTHROPIC_LARGE_MODEL=claude-opus-4-6

Clone and Build: add to .env instead

ANTHROPIC_BASE_URL=https://your-proxy.example.com
ANTHROPIC_AUTH_TOKEN=your-auth-token
ANTHROPIC_SMALL_MODEL=claude-haiku-4-5-20251001
ANTHROPIC_MEDIUM_MODEL=claude-sonnet-4-6
ANTHROPIC_LARGE_MODEL=claude-opus-4-6

Platform-Specific Instructions

For Windows:

Shannon on Windows is only supported via WSL2. Native Windows (including Git Bash) is not supported.

Step 1: Ensure WSL 2

wsl --install
wsl --set-default-version 2

# Check installed distros
wsl --list --verbose

# If you don't have a distro, install one (Ubuntu 24.04 recommended)
wsl --list --online
wsl --install Ubuntu-24.04

# If your distro shows VERSION 1, convert it to WSL 2:
wsl --set-version <distro-name> 2

See WSL basic commands for reference.

Step 2: Install Docker Desktop on Windows and enable WSL2 backend under Settings > General > Use the WSL 2 based engine.

Step 3: Run Shannon inside WSL using either flow.

npx inside WSL:

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npx @keygraph/shannon setup
npx @keygraph/shannon start -u https://your-app.com -r /path/to/your-repo

Clone and Build command equivalents

git clone https://github.com/KeygraphHQ/shannon.git
cd shannon
cp .env.example .env  # Edit with your API key
./shannon start -u https://your-app.com -r /path/to/your-repo

To access the Temporal Web UI, run ip addr inside WSL to find your WSL IP address, then navigate to http://<wsl-ip>:8233 in your Windows browser.

Windows Defender may flag exploit code in reports as false positives; see Antivirus False Positives below.

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:

`1`	`npx @keygraph/shannon start -u http://host.docker.internal:3000 -r /path/to/repo`

Clone and Build command equivalents

`1`	`./shannon start -u http://host.docker.internal:3000 -r /path/to/repo`

Output and Results

All results are saved to the workspaces directory: ./workspaces/ (local mode) or ~/.shannon/workspaces/ (npx mode). Use -o <path> to copy deliverables to a custom output directory after the run completes.

Output structure:

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

Sample Reports

Sample penetration test reports 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.

Results: Identified over 20 vulnerabilities across targeted OWASP categories in a single automated run.

Notable findings:

Authentication bypass and full user database exfiltration via SQL injection
Privilege escalation to administrator through registration workflow bypass
IDOR vulnerabilities enabling access to other users’ data and shopping carts
SSRF 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.

Results: Identified approximately 15 critical and high-severity vulnerabilities.

Notable findings:

Root-level command injection via denylist bypass in a hidden debug endpoint
Authentication bypass through a legacy, unpatched v1 API endpoint
Privilege escalation via Mass Assignment in the user profile update function
Zero false positives for XSS (correctly confirmed robust XSS defenses)

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.

Results: Identified over 15 critical and high-severity vulnerabilities.

Notable findings:

Authentication bypass via multiple JWT attacks (Algorithm Confusion, alg:none, weak key injection)
Full PostgreSQL database compromise via injection, exfiltrating user credentials
SSRF attack forwarding internal authentication tokens to an external service
Zero false positives for XSS (correctly identified robust XSS defenses)

View Complete Report →

Benchmark

Shannon Lite scored 96.15% (100/104 exploits) on a hint-free, source-aware variant of the XBOW security benchmark.

Full results with detailed agent logs and per-challenge pentest reports →

Architecture

Shannon uses a multi-agent architecture that combines white-box source code analysis with dynamic exploitation across five phases:

        ┌──────────────────────┐
        │   Pre-Reconnaissance │
        │  (nmap, subfinder,   │
        │  whatweb, code scan) │
        └──────────┬───────────┘
                   │
                   ▼
        ┌──────────────────────┐
        │   Reconnaissance     │
        │  (attack surface     │
        │   mapping)           │
        └──────────┬───────────┘
                   │
                   ▼
        ┌──────────┴───────────┐
        │          │           │
        ▼          ▼           ▼
  ┌───────────┐ ┌───────────┐ ┌───────────┐
  │ Vuln      │ │ Vuln      │ │   ...     │
  │(Injection)│ │  (XSS)    │ │           │
  └─────┬─────┘ └─────┬─────┘ └─────┬─────┘
        │              │             │
        ▼              ▼             ▼
  ┌───────────┐ ┌───────────┐ ┌───────────┐
  │ Exploit   │ │ Exploit   │ │   ...     │
  │(Injection)│ │  (XSS)    │ │           │
  └─────┬─────┘ └─────┬─────┘ └─────┬─────┘
        │              │             │
        └──────┬───────┴─────────────┘
               │
               ▼
        ┌──────────────────────┐
        │      Reporting       │
        └──────────────────────┘

Architectural Overview

Shannon uses Anthropic’s Claude Agent SDK as its reasoning engine within a multi-agent architecture. The system combines white-box source code analysis with black-box dynamic exploitation, managed by an orchestrator across five phases. The architecture is designed for minimal false positives through a “no exploit, no report” policy.

Each scan runs in its own ephemeral Docker container (docker run --rm) with a per-invocation Temporal task queue, enabling concurrent scans with different target repositories.

Phase 1: Pre-Reconnaissance

External scanning using nmap, subfinder, and whatweb to fingerprint the target’s infrastructure and tech stack. Simultaneously performs source code analysis to identify the application framework, entry points, and potential attack surface from the codebase.

Phase 2: Reconnaissance

Builds a comprehensive attack surface map from the pre-recon findings. Shannon 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.

Phase 3: Vulnerability Analysis

To maximize efficiency, this phase operates in parallel with 5 concurrent agents. Using the reconnaissance data, specialized agents for each OWASP category (injection, XSS, auth, authz, SSRF) 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 4: 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 5: 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.

For maximum security and isolation, run Shannon inside a virtual machine (VM). This confines any side effects from exploitation — including unexpected outbound traffic, file writes from agent tooling, or interactions with local services — to a disposable environment.

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.
Model Support: Shannon is officially supported only with Claude models. Our evaluations, internal testing, and agent harness are all optimized for Claude. Smaller or alternative models — including non-Claude models routed through a proxy — may not reliably follow Shannon’s instructions or tool-use constraints, and are not officially supported.
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: Running the full test using Anthropic’s Claude 4.5 Sonnet model may incur costs of approximately $50 USD. Costs vary based on model pricing and application complexity.

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.

7. Security Considerations

Shannon Lite is designed for scanning repositories and applications you own or have explicit permission to test. Do not point it at untrusted or adversarial codebases. Like any AI-powered tool that reads source code, Shannon Lite is susceptible to prompt injection from content in the scanned repository.

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

1:1 Office Hours — Thursdays, two time zones Book a free 15-min session for hands-on help with bugs, deployments, or config questions. → US/EU: 10:00 AM PT | Asia: 2:00 PM IST → Book a slot

Join our Discord to ask questions, share feedback, and connect with other Shannon users.

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

Stay Connected

Twitter: @KeygraphHQ
LinkedIn: Keygraph
Website: keygraph.io

Get in Touch

Shannon Pro

Shannon Pro is Keygraph’s all-in-one AppSec platform. For organizations that need unified SAST, SCA, and autonomous pentesting with static-dynamic correlation, CI/CD integration, or self-hosted deployment, see the Shannon Pro technical overview.

Email: shannon@keygraph.io

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Shannon — AI Pentester by Keygraph

What is Shannon?

Shannon in Action

Features

Product Line

Shannon Pro: Architecture Overview

Table of Contents

Setup & Usage Instructions

Prerequisites

Quick Start (Recommended: npx)

Clone and Build

Prepare Your Repository

Common Commands

Monitoring Progress

Stopping Shannon

Usage Examples

Workspaces and Resuming

Credentials and Configuration

Credential Precedence

Configuration (Optional)

Create Configuration File

Basic Configuration Structure

TOTP Setup for 2FA

Subscription Plan Rate Limits

AWS Bedrock

Quick Setup

Google Vertex AI

Quick Setup

Custom Base URL

Platform-Specific Instructions

Output and Results

Sample Reports

OWASP Juice Shop • GitHub

c{api}tal API • GitHub

OWASP crAPI • GitHub

Benchmark

Architecture

Architectural Overview

Phase 1: Pre-Reconnaissance

Phase 2: Reconnaissance

Phase 3: Vulnerability Analysis

Phase 4: Exploitation

Phase 5: Reporting

Coverage and Roadmap

Disclaimers

Important Usage Guidelines & Disclaimers

1. Potential for Mutative Effects & Environment Selection

2. Legal & Ethical Use

3. LLM & Automation Caveats

4. Scope of Analysis

5. Cost & Performance

6. Windows Antivirus False Positives

7. Security Considerations

License

Community & Support

Community Resources

Stay Connected

Get in Touch

Shannon Pro