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.

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

• 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.

Shannon is developed by Keygraph and available in two editions:

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.

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.

Full technical details →

• What is Shannon?
• Shannon in Action
• Features
• Product Line
• Setup & Usage Instructions
  • Prerequisites
  • Quick Start (Recommended: npx)
  • Clone and Build
  • Prepare Your Repository
  • Common Commands
  • Workspaces and Resuming
  • Credentials and Configuration
  • AWS Bedrock
  • Google Vertex AI
  • Custom Base URL
  • Router Mode
  • Platform-Specific Instructions
  • Output and Results
• Sample Reports
• Benchmark
• Architecture
• Coverage and Roadmap
• Disclaimers
• License
• Community & Support
• Get in Touch

• 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)
  • [EXPERIMENTAL - UNSUPPORTED] Alternative providers via Router Mode - OpenAI or Google Gemini via OpenRouter (see Router Mode)

# 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.

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.

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

Examples:

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

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

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

Open the Temporal Web UI for detailed monitoring:

open http://localhost:8233

./shannon logs <workspace> ./shannon status

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

./shannon stop ./shannon stop --clean

# 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

# 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

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

./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

Local mode resolves credentials from:

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

npx mode uses TOML instead of .env:

1. Environment variables - export ANTHROPIC_API_KEY=...
2. ~/.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.

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

Copy and modify the example configuration:

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

# 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:

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

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

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.

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:

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.

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

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

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.

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.

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

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.

Shannon supports pointing the SDK at any Anthropic-compatible endpoint (proxies, gateways, etc.) via ANTHROPIC_BASE_URL.

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

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

Shannon can experimentally route requests through alternative AI providers using claude-code-router. This mode is not officially supported and is intended primarily for:

• Model experimentation — try Shannon with GPT-5.2 or Gemini 3-family models

Run npx @keygraph/shannon setup and select Router. The wizard will prompt you to choose a provider (OpenAI or OpenRouter), enter your API key, and select a default model.

Or export env vars directly:

export OPENAI_API_KEY=sk-... # or OPENROUTER_API_KEY=sk-or-... export ROUTER_DEFAULT=openai,gpt-5.2 # provider,model format

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

OPENAI_API_KEY=sk-... # OR OPENROUTER_API_KEY=sk-or-... ROUTER_DEFAULT=openai,gpt-5.2

./shannon start -u https://example.com -r /path/to/repo --router

This feature is experimental and unsupported. Output quality depends heavily on the model. Shannon is built on top of the Anthropic Agent SDK and is optimized and primarily tested with Anthropic Claude models. Alternative providers may produce inconsistent results (including failing early phases like Recon) depending on the model and routing setup.

For Windows:

Native (Git Bash):

Install Git for Windows and run Shannon from Git Bash with Docker Desktop installed. Both npx @keygraph/shannon and local clone mode are supported.

WSL2 (Recommended):

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:

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

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:

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

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

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 penetration test reports from industry-standard vulnerable applications:

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 →

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 →

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 →

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 →

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 │ └──────────────────────┘ 

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.

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.

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.

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.

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.

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.

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

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

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.

Shannon is designed for legitimate security auditing purposes only.

• 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.

• 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.

• 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.

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.

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.

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.

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

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

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

Built by Keygraph