Repository: KeygraphHQ/shannon

Stars: 38457

CLAUDE.md Copy Chapter

CLAUDE.md

AI-powered penetration testing agent for defensive security analysis. Automates vulnerability assessment by combining reconnaissance tools with AI-powered code analysis.

Commands Copy Chapter

Prerequisites: Docker, AI provider credentials (.env for local, shn setup or env vars for npx)

Dual CLI Copy Section

Shannon supports two CLI modes, auto-detected based on the current working directory:

| | npx (npx @keygraph/shannon) | Local (./shannon) |
|---|---|---|
| Install | Zero-install via npm | Clone the repo |
| Image | Pulled from Docker Hub (keygraph/shannon:latest) | Built locally (shannon-worker) |
| State | ~/.shannon/ | Project directory |
| Credentials | ~/.shannon/config.toml (via shn setup) or env vars | ./.env |
| Config | ~/.shannon/config.toml (via shn setup) | N/A |
| Prompts | Bundled in Docker image | Mounted from ./apps/worker/prompts/ (live-editable) |

Mode auto-detection: local mode activates when env var SHANNON_LOCAL=1 is set by the ./shannon entry point (apps/cli/src/mode.ts). Otherwise npx mode.

npx Quick Start Copy Section

Configure credentials (interactive wizard)

npx @keygraph/shannon setup
Or export env vars directly (non-interactive / CI)

export ANTHROPIC_API_KEY=your-key
Run

npx @keygraph/shannon start -u <url> -r /path/to/repo

Local (Development) Quick Start Copy Section

Setup

echo "ANTHROPIC_API_KEY=your-key" > .env
Build (auto-runs if image missing)

./shannon build
Run

./shannon start -u <url> -r my-repo
./shannon start -u <url> -r my-repo -c ./apps/worker/configs/my-config.yaml
./shannon start -u <url> -r /any/path/to/repo

Common Commands Copy Section

Setup (npx mode only — one-time credential configuration)

npx @keygraph/shannon setup
Workspaces & Resume

./shannon start -u <url> -r my-repo -w my-audit    # New named workspace
./shannon start -u <url> -r my-repo -w my-audit    # Resume (same command)
./shannon workspaces                                 # List all workspaces
Monitor

./shannon logs <workspace>            # Tail workflow log
./shannon status                      # Show running workers
Temporal Web UI: http://localhost:8233
Stop

./shannon stop                        # Preserves workflow data
./shannon stop --clean                # Full cleanup including volumes (confirms first)
Image management

./shannon build [--no-cache]          # Local mode: build worker image
npx @keygraph/shannon uninstall             # npx mode: remove ~/.shannon/ (confirms first)
Build TypeScript (development)

pnpm run build                       # Build all packages via Turborepo
pnpm run check                       # Type-check all packages
pnpm biome                           # Biome lint + format + import sorting check
pnpm biome:fix                       # Auto-fix lint, format, and import sorting

Monorepo tooling: pnpm workspaces, Turborepo for task orchestration, Biome for linting/formatting. TypeScript compiler options shared via tsconfig.base.json at the root. All packages extend it, overriding only rootDir and outDir. Shared devDependencies (typescript, @types/node, turbo, @biomejs/biome) are hoisted to the root workspace.

Options: -c <file> (YAML config), -o <path> (output directory), -w <name> (named workspace; auto-resumes if exists), --pipeline-testing (minimal prompts, 10s retries), --router (multi-model routing via claude-code-router)

Architecture Copy Chapter

Monorepo Layout Copy Section

apps/cli/        — @keygraph/shannon (published to npm, bundled with tsdown)
apps/worker/     — @shannon/worker (private, Temporal worker + pipeline logic)

CLI Package (apps/cli/) apps/cli/)')" title="Copy section prompt for LLMs"> Copy Section

- apps/cli/src/index.ts — CLI dispatcher (setup, start, stop, logs, workspaces, status, build, uninstall, info)
- apps/cli/src/mode.ts — Auto-detection: local mode if SHANNON_LOCAL=1 env var is set
- apps/cli/src/docker.ts — Compose lifecycle, image pull/build, ephemeral docker run worker spawning
- apps/cli/src/home.ts — State directory management (~/.shannon/ for npx, ./ for local)
- apps/cli/src/env.ts — .env loading, TOML fallback (npx only) via apps/cli/src/config/resolver.ts, credential validation, env flag building
- apps/cli/src/config/resolver.ts — Cascading config (npx only): env vars → ~/.shannon/config.toml (parsed with smol-toml)
- apps/cli/src/config/writer.ts — TOML serialization and secure file persistence (0o600)
- apps/cli/src/commands/setup.ts — Interactive TUI wizard (@clack/prompts) for provider credential setup (npx only)
- apps/cli/src/paths.ts — Repo/config path resolution (bare name → ./repos/<name>, or any absolute/relative path)
- apps/cli/src/commands/ — Command handlers
- apps/cli/infra/compose.yml — Bundled Temporal + router compose file for npx mode
- apps/cli/tsdown.config.ts — tsdown bundler config
- shannon — Node.js entry point (#!/usr/bin/env node) that delegates to apps/cli/dist/index.mjs

Docker Architecture Copy Section

- docker-compose.yml — Infra only: shannon-temporal (port 7233/8233) and shannon-router (port 3456, optional via profile). Network: shannon-net
- Dockerfile — 2-stage build (builder + Chainguard Wolfi runtime). Uses pnpm. Entrypoint: CMD ["node", "apps/worker/dist/temporal/worker.js"]
- No docker-compose.docker.yml — host gateway handled via --add-host flag in CLI

Worker Package (apps/worker/) apps/worker/)')" title="Copy section prompt for LLMs"> Copy Section

Temporal Orchestration Copy Section

- apps/worker/src/temporal/workflows.ts — Main workflow (pentestPipelineWorkflow)
- apps/worker/src/temporal/activities.ts — Thin wrappers — heartbeat loop, error classification, container lifecycle. Business logic delegated to apps/worker/src/services/
- apps/worker/src/temporal/activity-logger.ts — TemporalActivityLogger implementation of ActivityLogger interface
- apps/worker/src/temporal/summary-mapper.ts — Maps PipelineSummary to WorkflowSummary
- apps/worker/src/temporal/worker.ts — Combined worker + client entry point (per-invocation task queue, submits workflow, waits for result)
- apps/worker/src/temporal/shared.ts — Types, interfaces, query definitions

Five-Phase Pipeline Copy Section

1. Pre-Recon (pre-recon) — External scans (nmap, subfinder, whatweb) + source code analysis
2. Recon (recon) — Attack surface mapping from initial findings
3. Vulnerability Analysis (5 parallel agents) — injection, xss, auth, authz, ssrf
4. Exploitation (5 parallel agents, conditional) — Exploits confirmed vulnerabilities
5. Reporting (report) — Executive-level security report

Supporting Systems Copy Section

Development Notes Copy Chapter

Adding a New Agent Copy Section

Modifying Prompts Copy Section

Key Design Patterns Copy Section

Security Copy Section

Code Style Guidelines Copy Chapter

Formatting Copy Section

Clarity Over Brevity Copy Section

Structure Copy Section

TypeScript Conventions Copy Section

Avoid Copy Section

Comments Copy Section

Patterns used in this codebase:
- / JSDoc */ — file headers (after license) and exported functions/interfaces
- // N. Description — numbered sequential steps inside function bodies. Use when a
function has 3+ distinct phases where at least one isn't immediately obvious from the
code. Each step marks the start of a logical phase. Reference: AgentExecutionService.execute
(steps 1-9) and injectModelIntoReport (steps 1-5)
- // === Section === — high-level dividers between groups of functions in long files,
or to label major branching/classification blocks (e.g., // === SPENDING CAP SAFEGUARD ===).
Not for sequential steps inside function bodies — use numbered steps for that
- // NOTE: / // WARNING: / // IMPORTANT: — gotchas and constraints

Never: obvious comments, conversation references ("as discussed"), history ("moved from X")

Key Files Copy Chapter

CLI: shannon (entry point), apps/cli/src/index.ts (dispatcher), apps/cli/src/docker.ts (orchestration), apps/cli/src/mode.ts (auto-detection)

Entry Points: apps/worker/src/temporal/workflows.ts, apps/worker/src/temporal/activities.ts, apps/worker/src/temporal/worker.ts

Core Logic: apps/worker/src/session-manager.ts, apps/worker/src/ai/claude-executor.ts, apps/worker/src/config-parser.ts, apps/worker/src/services/, apps/worker/src/audit/

Config: docker-compose.yml, apps/cli/infra/compose.yml, apps/worker/configs/, apps/worker/prompts/, tsconfig.base.json (shared compiler options), turbo.json, biome.json

CI/CD: .github/workflows/release.yml (Docker Hub push + npm publish + GitHub release, manual dispatch)

Package Installation Copy Chapter

Package managers are configured with a minimum release age (7 days). Requires pnpm >= 10.16.0. If pnpm install fails due to a package being too new, do not attempt to bypass it — report the blocked package to the user and stop.

Troubleshooting Copy Chapter

- "Repository not found" — Pass a bare name (-r my-repo) for ./repos/my-repo, or a path (-r /path/to/repo) for any directory
- "Temporal not ready" — Wait for health check or docker compose logs temporal
- Worker not processing — Check docker ps --filter "name=shannon-worker-"
- Reset state — ./shannon stop --clean
- Local apps unreachable — Use host.docker.internal instead of localhost
- Missing tools — Use --pipeline-testing to skip nmap/subfinder/whatweb (graceful degradation)
- Container permissions — On Linux, may need sudo for docker commands

README.md Copy Chapter

>[!NOTE]

📢 New: Shannon is now available via npx @keygraph/shannon. →

<div align="center">

<img src="./assets/github-banner.png" alt="Shannon — AI Pentester for Web Applications and APIs" width="100%">

Shannon — AI Pentester by Keygraph

<a href="https://trendshift.io/repositories/15604" target="_blank"><img src="https://trendshift.io/api/badge/repositories/15604" alt="KeygraphHQ%2Fshannon | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>

Shannon is an autonomous, white-box AI pentester for web applications and APIs. <br />
It analyzes your source code, identifies attack vectors, and executes real exploits to prove vulnerabilities before they reach production.

---

<a href="https://discord.gg/9ZqQPuhJB7"><img src="./assets/discord.png" height="40" alt="Join Discord"></a>
<a href="https://keygraph.io/"><img src="./assets/Keygraph_Button.png" height="40" alt="Visit Keygraph.io"></a>

---
</div>

What is Shannon? Copy Chapter

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 Copy Chapter

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

!Demo

Features Copy Chapter

- 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 Copy Chapter

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.

It expects access to your application's source code and repository layout.

Shannon Pro: Architecture Overview Copy Section

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 →

Table of Contents Copy Chapter

- 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

---

Setup & Usage Instructions Copy Chapter

Prerequisites Copy Section

- 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)

Quick Start (Recommended: npx) Copy Section

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 Copy Section

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 Copy Section

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

<details>
<summary>Clone and Build command equivalents</summary>

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

</details>

Common Commands Copy Section

#### Monitoring Progress

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

Open the Temporal Web UI for detailed monitoring:

open http://localhost:8233

<details>
<summary>Clone and Build command equivalents</summary>

./shannon logs <workspace>
./shannon status

</details>

#### Stopping Shannon

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

<details>
<summary>Clone and Build command equivalents</summary>

./shannon stop
./shannon stop --clean

</details>

#### 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

<details>
<summary>Clone and Build command equivalents</summary>

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

</details>

Workspaces and Resuming Copy Section

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

<details>
<summary>Clone and Build command equivalents</summary>

./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

</details>

Credentials and Configuration Copy Section

#### Credential Precedence

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.

#### 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:

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: "[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"

Run with:

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

<details>
<summary>Clone and Build command equivalents</summary>

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

</details>

#### 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:

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 Copy Section

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

<details>
<summary>Clone and Build: add to .env instead</summary>

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

</details>

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 Copy Section

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

<details>
<summary>Clone and Build: add to .env instead</summary>

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

</details>

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 Copy Section

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

<details>
<summary>Clone and Build: add to .env instead</summary>

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

</details>

[EXPERIMENTAL - UNSUPPORTED] Router Mode (Alternative Providers) Copy Section

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

#### Quick Setup

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

<details>
<summary>Clone and Build: add to .env and run with --router</summary>

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

</details>

#### Experimental Models

| Provider | Models |
|----------|--------|
| OpenAI | gpt-5.2, gpt-5-mini |
| OpenRouter | google/gemini-3-flash-preview |

#### Disclaimer

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.

Platform-Specific Instructions Copy Section

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

<details>
<summary>Clone and Build command equivalents</summary>

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

</details>

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

<details>
<summary>Clone and Build command equivalents</summary>

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

</details>

Output and Results Copy Section

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 Copy Chapter

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 Copy Chapter

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 Copy Chapter

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 Copy Section

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 Copy Chapter

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

Disclaimers Copy Chapter

Important Usage Guidelines & Disclaimers Copy Section

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.

> - 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.

> 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: 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 Copy Chapter

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 Copy Chapter

Community Resources Copy Section

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 Copy Section

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

Get in Touch Copy Chapter

Shannon Pro Copy Section

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.

<p align="center">
<a href="https://cal.com/team/keygraph/shannon-pro" target="_blank">
<img src="./assets/Demo_Button.png" height="40" alt="Shannon Pro Inquiry">
</a>
</p>

Email: [email protected]

---

<p align="center">
<b>Built by <a href="https://keygraph.io">Keygraph</a></b>
</p>