shannon Documentation

Shannon Full LLM Context

Combined README and documentation for AI agents and LLMs. Generated from repository Markdown files. For the concise index, see llms.txt.

File: README.md

[!NOTE]
Shannon 2.0 now runs on the Pi harness

[!TIP]
AI agents and LLMs: start with llms.txt for a concise map of this repository, or use llms-full.txt for the README and docs combined into one file.

Table of Contents Copy Chapter

• What is Shannon?
• Shannon in Action
• Quick Start
• Key Capabilities
• Editions
• Architecture
• Documentation
• Safety, Scope, and Limitations
• License and Enterprise Licensing
• About Keygraph
• Community and Support

What is Shannon? Copy Chapter

Shannon is an autonomous 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 against the running application and its APIs. Only vulnerabilities with a working proof-of-concept are included in the final report.

Shannon is the agent. This repository is Shannon Open Source, the standalone pentester you run yourself. The same Shannon also powers the Keygraph platform, Keygraph's commercial pentesting product. See Editions for how the two compare.

Why Shannon Exists Copy Section

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

Sample penetration test reports from intentionally vulnerable applications, produced by Shannon Open Source:

Quick Start Copy Chapter

Prerequisites Copy Section

• Docker: required for the worker container.
• Node.js 18+: required for the recommended npx workflow.
• AI provider credentials: Anthropic, OpenAI, xAI, or AWS Bedrock - or any other provider. Claude models are recommended. Gateway and proxy setups are documented separately.
• Cyber safeguards cleared with your provider: Anthropic and OpenAI apply real-time safeguards to cyber-security workloads, which can interrupt a scan mid-run. Complete their guidance for legitimate security testers before your first run - see AI providers.

Run Shannon Copy Section

[!WARNING]
Shannon actively executes exploits. Run it only against applications and environments you own or have explicit written authorization to test. Do not run Shannon against production systems.

# Configure credentials with the interactive wizard.
npx @keygraph/shannon setup

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

Shannon pulls the worker image from Docker Hub, starts the required local infrastructure, mounts the target repository read-only inside an ephemeral worker container, and writes results to a local workspace.

For source builds, authenticated scans, provider-specific setup, and platform notes, see Documentation.

[!TIP]
Prefer to run on your Claude Code subscription instead of API credits? The shannon-v1 branch is the last release built on the Claude Agent SDK, so it accepts a Claude Code OAuth token. Generate one with claude setup-token, then run npx @keygraph/[email protected] setup and pick OAuth Token. Pentests then cost nothing beyond your existing subscription.

Key Capabilities Copy Chapter

• Proof-by-exploitation reports: Shannon reports validated findings with reproducible proof-of-concept steps instead of speculative warnings.
• White-box attack planning: Shannon uses source-code analysis to guide dynamic testing and focus on realistic attack paths.
• Autonomous execution: Shannon launches reconnaissance, vulnerability analysis, exploitation, and report generation from a single command.
• Authenticated testing: configuration files can describe login flows, test credentials, TOTP, email-based login flows, focus areas, and rules of engagement.
• OWASP-focused coverage: Shannon targets exploitable Injection, XSS, SSRF, Broken Authentication, and Broken Authorization issues.
• Resumable workspaces: Shannon can resume interrupted runs without re-running completed agents.

Editions Copy Chapter

Shannon ships in two ways: Shannon Open Source, the pentester you run yourself, and the Keygraph platform, the commercial pentesting product that runs Shannon continuously and closes the full AppSec lifecycle around it.

Shannon Open Source (this repository) is the standalone pentester: a CLI agent for white-box, proof-by-exploitation testing of web applications and APIs you own or are authorized to test. It reads your source, plans attacks, executes real exploits, and reports only what it can prove. It runs on demand and is complete in that lane. You point it at a target, it pentests, it reports.

The Keygraph platform is the enterprise-ready, continuous pentesting product powered by Shannon. In the Keygraph platform, an enhanced build of Shannon runs continuously in a hardened, orchestrated environment fed by Keygraph's full code-analysis stack. Around that engine, the platform closes the entire vulnerability lifecycle, from analysis to a verified fix:

• Analyze: Code Property Graph SAST, SCA with reachability, secrets, IaC, and container scanning. First-class detection in their own right, and context that sharpens Shannon's attacks.
• Prove: autonomous black-box and source-aware white-box pentests turn candidate findings into proven, exploited vulnerabilities rather than speculative alerts.
• Manage: one canonical record per vulnerability per repository, deduplicated across every source, with ownership, status, SLA tracking, dashboards, and bidirectional Jira sync.
• Remediate and verify: patches written automatically and re-tested against the patched code before delivery, landing in your existing review workflow rather than auto-applied.
• Deploy: self-hosted and air-gapped environments, strict bring-your-own-key model access, and customer-controlled LLM gateway patterns, so source, results, and model traffic stay inside your perimeter.

Shannon is the proof engine at the center of the Keygraph platform. Shannon Open Source gives you that engine to run yourself. The Keygraph platform surrounds Shannon with continuous analysis, finding management, remediation, verification, and enterprise deployment.

Learn more on the Keygraph website, read the Keygraph platform technical overview, start a free trial or book a demo, or contact [email protected].

Architecture Copy Chapter

Shannon uses a multi-agent workflow that combines source-code analysis with live exploitation:

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

At a high level:

• Pre-reconnaissance identifies frameworks, entry points, data flows, and likely attack surfaces from the repository.
• Reconnaissance explores the live application and correlates runtime behavior with code-level context.
• Vulnerability analysis runs specialized agents for Injection, XSS, SSRF, Authentication, and Authorization.
• Exploitation attempts real proof-of-concept attacks and discards hypotheses that cannot be proven.
• Reporting compiles validated findings, evidence, and remediation guidance into a final Markdown report.

Each scan runs in an ephemeral Docker container with an isolated workspace and per-invocation orchestration.

Documentation Copy Chapter

Use these guides for operational detail:

Safety, Scope, and Limitations Copy Chapter

Shannon is not a passive scanner. Its exploitation agents can create users, submit forms, mutate application state, trigger outbound requests, and otherwise affect the target system. Use sandboxed, staging, or local development environments with disposable data.

You are responsible for using Shannon legally and ethically. Do not point Shannon at systems, repositories, or applications you do not own or do not have explicit authorization to test.

Important limitations:

• Shannon Open Source focuses on actively exploitable issues such as Injection, XSS, SSRF, Broken Authentication, and Broken Authorization. Broader static-analysis coverage, including vulnerable dependencies and insecure configurations, is delivered through the Keygraph platform.
• Findings still require human review. LLM-generated reports can contain weakly supported or incorrect details.
• Shannon is officially supported with Claude models. Smaller, alternative, or proxied non-Claude models may be incomplete or unstable.
• A full run can take roughly 1 to 1.5 hours and may incur LLM API costs depending on model pricing and application complexity.
• Do not scan untrusted or adversarial codebases. AI-powered tools that read source code can be exposed to prompt injection.

Read the full Safety and limitations guide before running Shannon in a new environment.

License and Enterprise Licensing Copy Chapter

Shannon Open Source is licensed under the GNU Affero General Public License v3.0.

Commercial and enterprise licensing is available for organizations that need different license terms, commercial support, private redistribution, managed-service use, or broader deployment options, including the Keygraph platform.

For commercial licensing, contact [email protected].

About Keygraph Copy Chapter

Keygraph is the company behind Shannon. It also builds the Keygraph platform, the commercial agentic pentesting product that closes the full AppSec lifecycle and runs an enhanced build of Shannon as its pentesting engine.

Community and Support Copy Chapter

Community office hours are available for hands-on help with bugs, deployments, and configuration questions.

• US/EU: Thursday, 10:00 AM PT
• Asia: Thursday, 2:00 PM IST
• Book a slot

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

At this time, Keygraph is not accepting external code contributions. Issues are welcome for bug reports and feature requests:

• Report bugs
• Suggest features

Stay connected:

• Keygraph website
• Twitter/X: @KeygraphHQ
• LinkedIn: Keygraph

File: docs/development.md

Source Build and CLI Commands

This guide covers the source-build workflow, common CLI commands, repository paths, and output locations. For the fastest first run, use the npx workflow in the main README.

Prerequisites Copy Chapter

• Docker
• Node.js 18+
• pnpm
• AI provider credentials

Clone and Build Copy Chapter

Use the source-build workflow if you want to run Shannon from a local clone, modify the open-source CLI, or keep the worker image built locally.

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

# 2. Configure credentials.
cp .env.example .env

# 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

At minimum, your .env file should include one supported AI provider credential, such as:

ANTHROPIC_API_KEY=your-api-key

Environment variables can also be exported directly:

export ANTHROPIC_API_KEY="your-api-key"

Prepare Your Repository Copy Chapter

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

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

The target repository is mounted read-only inside the worker container.

Common Commands Copy Chapter

Monitor progress:

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

Source-build equivalents:

./shannon logs <workspace>
./shannon status <workspace>
./shannon scans
./shannon version

Open the Temporal Web UI for detailed monitoring:

open http://localhost:8233

Stop Shannon:

npx @keygraph/shannon stop <workspace>   # stop one scan (confirms first; add --yes/-y to skip)
npx @keygraph/shannon stop --all         # stop all scans (Temporal stays up)
npx @keygraph/shannon reset              # stop everything and wipe all Temporal data (type 'confirm' to proceed; cannot be skipped)

Source-build equivalents:

./shannon stop <workspace>               # stop one scan (confirms first; add --yes/-y to skip)
./shannon stop --all                     # stop all scans (Temporal stays up)
./shannon reset                          # stop everything and wipe all Temporal data (type 'confirm' to proceed; cannot be skipped)

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

# Stream the log until the scan finishes, then exit on its outcome (useful in CI).
npx @keygraph/shannon start -u https://example.com -r /path/to/repo --follow

# List completed scans.
npx @keygraph/shannon scans

Source-build examples:

./shannon start -u https://example.com -r /path/to/repo
./shannon start -u https://example.com -r /path/to/repo -c /path/to/my-config.yaml
./shannon start -u https://example.com -r /path/to/repo -o ./my-reports
./shannon start -u https://example.com -r /path/to/repo -w q1-audit
./shannon start -u https://example.com -r /path/to/repo --follow
./shannon scans

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

Output and Results Copy Chapter

Results are saved to the workspaces directory:

• ./workspaces/ in source-build mode
• ~/.shannon/workspaces/ in npx mode

Use -o to copy deliverables to a custom output directory after a run completes.

Output structure — the run directory's top level holds the final report, in PDF and Markdown; everything else is nested under a hidden .shannon/ directory:

workspaces/{hostname}_{sessionId}/
|-- Security-Assessment-Report.pdf  # the final report (PDF)
|-- Security-Assessment-Report.md   # the final report (Markdown)
`-- .shannon/                       # internals
    |-- deliverables/               # report source, per-phase analysis, queues
    |-- agents/                     # per-agent logs
    |-- prompts/                    # rendered prompts
    |-- scratchpad/                 # screenshots, scripts
    |-- session.json                # resume state
    `-- workflow.log

File: docs/configuration.md

Configuration

Shannon can run without a configuration file, but configuration enables authenticated testing, scope guidance, rules of engagement, and report filtering.

Credential Precedence Copy Chapter

Source-build mode resolves credentials from:

1. Environment variables, such as export ANTHROPIC_API_KEY=...
2. ./.env

npx mode resolves credentials from:

1. Environment variables
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.

Create a Configuration File Copy Chapter

Copy and modify the example configuration:

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

Run with:

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

Source-build equivalent:

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

Basic Configuration Structure Copy Chapter

# Describe your target environment.
# Limit which vulnerability classes run end-to-end.
# vuln_classes: [injection, xss, auth, authz, ssrf]

# Skip the exploitation phase.
# exploit: "false"

# Free-form rules of engagement.
# rules_of_engagement: |
#   - No password brute-force; cap login attempts at 5 per account.
#   - Throttle to under 5 requests per second per endpoint; back off 60s on any 429.
#   - Use placeholders like [order_id] in deliverables; no real data values.

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

    # Optional mailbox credentials for magic-link or email-OTP flows.
    # email_login:
    #   address: "[email protected]"
    #   password: "mailbox-password"
    #   totp_secret: "JBSWY3DPEHPK3PXP"

  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: url_path
      value: "/logout"

    # code_path values are repo-relative file paths or globs.
    # - description: "Out-of-scope vendored libraries"
    #   type: code_path
    #   value: "src/vendor/**"

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

# Report options applied when assembling the final report.
# report:
#   min_severity: low
#   min_confidence: low
#   guidance: |
#     Drop findings about missing security headers and rate-limit gaps.
#   sarif: "true"

Report Options Copy Chapter

A finding carries one rating or the other, never both: an exploited finding is rated by severity, an analysis-only finding by confidence. Setting the threshold that does not apply to the run is ignored, and Shannon logs a warning naming the one to use instead.

SARIF Output Copy Section

Set sarif: "true" to write report.sarif next to Security-Assessment-Report.pdf at the workspace root, for upload to GitHub code scanning or any other SARIF consumer.

exploit: "true"
report:
  sarif: "true"

Each finding becomes one SARIF result, filed under a rule per vulnerability class (shannon/injection, shannon/xss, shannon/auth, shannon/authz, shannon/ssrf) and tagged with its OWASP Top Ten 2025 category. Results are anchored to the code location the analysis phase recorded, falling back to the HTTP entry point when the finding names no file. Severity maps onto SARIF's three levels: critical and high become error, medium becomes warning, everything else becomes note.

The log is written only for exploitative runs. An analysis-only run rates findings by confidence and produces no severity, so there is nothing to populate level with; sarif is ignored when exploit is "false".

Supported rule types include url_path, subdomain, domain, method, header, parameter, and code_path.

Writing Login Flow Copy Chapter

Log in once in a fresh private browser window. Write the steps in the same order you perform them:

• When typing into a field, reference the field by its exact label or placeholder.
• When clicking a button, reference the exact button text.

Supported placeholders:

• $username
• $password
• $totp
• $email_address
• $email_password
• $email_totp

At runtime, Shannon replaces these placeholders with the credentials passed in the config.

login_flow:
  - "Type $username in <exact email field label or placeholder>"
  - "Click <exact button text>"
  - "Type $password in <exact password field label or placeholder>"
  - "Click <exact button text>"
  - "If prompted for 2FA, type $totp in <exact code field label or placeholder>"
  - "Click <exact button text>"

File: docs/ai-providers.md

AI Providers

One model runs the entire scan — pre-recon, recon, vulnerability analysis, exploitation, and reporting. A single setting names both the provider and the model:

export SHANNON_AI_MODEL=:<model-id>

The provider half decides where the request goes, which credential is used, and which API dialect is spoken. You never configure those separately.

Supported providers Copy Chapter

SHANNON_AI_API_KEY holds the key for whichever provider SHANNON_AI_MODEL names. Bedrock is the exception — it authenticates through its AWS_ variables only. If SHANNON_AI_MODEL is unset, Shannon uses anthropic:claude-sonnet-4-6.

Anthropic, OpenAI, and xAI also accept their native variables (ANTHROPIC_API_KEY, OPENAI_API_KEY, XAI_API_KEY); if one of those is set, it is used instead of SHANNON_AI_API_KEY.

Shannon forwards only the selected provider's credential into the scan container. Keys for other providers stay on your machine.

Any other provider Copy Section

Shannon accepts any provider and model present in the Pi harness catalogue. Browse them at pi.dev/models. These are technically supported but not recommended. Claude models are best-supported (see the note below).

export SHANNON_AI_API_KEY=your-api-key                 # the provider's API key
export SHANNON_AI_MODEL=openrouter:moonshotai/kimi-k3  # :<model-id>

This path covers providers whose credential is a single API key. Providers that need more than that are not currently supported.

npx @keygraph/shannon setup exposes this as the Other provider option.

[!IMPORTANT]
Claude models are the best-supported option. Shannon's evaluations, internal testing, and agent harness are tuned for Claude. Other models are permitted and validated against the harness catalogue, but may not follow Shannon's instructions or tool-use constraints as reliably. Use them at your own risk.

Cyber safeguards (do this before your first scan) Copy Chapter

Anthropic and OpenAI both apply real-time safeguards to cyber-security workloads. Shannon is exactly such a workload. If a safeguard engages mid-run, the model can refuse, and the scan fails partway through rather than at the start.

Review each vendor's guidance and complete the verification or enrollment they ask of legitimate security testers before running Shannon:

• Anthropic - Real-time cyber safeguards on Claude Opus and Sonnet
• OpenAI - Cyber

This applies to the Anthropic and OpenAI providers, including when either is reached through a gateway. Bedrock serves Claude models and is subject to Anthropic's safeguards as well.

Suggested models Copy Chapter

These are the models npx @keygraph/shannon setup offers, best-first. They are suggestions: the wizard also takes a typed model ID, and SHANNON_AI_MODEL accepts any model in the provider's catalogue.

Bedrock IDs are region-prefixed and must be enabled in your account, so the ID that works for you may differ from the one listed here.

Switching provider Copy Chapter

The pattern is learned once: export the provider's key, name the model. Two lines change, nothing else.

Anthropic (default):

export SHANNON_AI_API_KEY=sk-ant-...
export SHANNON_AI_MODEL=anthropic:claude-sonnet-4-6

OpenAI:

export SHANNON_AI_API_KEY=sk-...
export SHANNON_AI_MODEL=openai:gpt-5.6-sol

xAI:

export SHANNON_AI_API_KEY=xai-...
export SHANNON_AI_MODEL=xai:grok-4.5

Source-build mode reads the same variables from a .env file.

AWS Bedrock Copy Chapter

Run npx @keygraph/shannon setup and select AWS Bedrock, or export directly:

export AWS_REGION=us-east-1
export AWS_BEARER_TOKEN_BEDROCK=your-bearer-token
export SHANNON_AI_MODEL=amazon-bedrock:us.anthropic.claude-opus-4-8

Bedrock uses bearer-token authentication only. IAM access keys, session tokens, assumed roles, and instance profiles are not supported. The model must be enabled in your region.

Custom base URL Copy Chapter

To route model traffic through your own infrastructure — a corporate proxy, an LLM gateway such as LiteLLM, or a regional endpoint — set a base URL alongside your normal model selection. The provider half of SHANNON_AI_MODEL decides which key is sent and which API Shannon speaks, so pick the one your gateway serves:

The model ID is whatever name your gateway serves it under; it does not have to exist in Shannon's catalogue.

Anthropic Messages:

export SHANNON_AI_API_KEY=sk-ant-...
export SHANNON_AI_MODEL=anthropic:claude-sonnet-4-6
export SHANNON_AI_BASE_URL=https://llm-gateway.example.com

OpenAI Chat Completions:

export SHANNON_AI_API_KEY=sk-...
export SHANNON_AI_MODEL=openai:gpt-5.6-sol
export SHANNON_AI_BASE_URL=https://llm-gateway.example.com/v1

SHANNON_AI_MODEL is always :<model-id>, gateway or not.

OpenAI is the one provider serving two APIs, so a gateway run picks one:

export SHANNON_AI_OPENAI_FORMAT=responses          # default: chat-completions

Chat Completions is the default because that is what most gateway software exposes. Set responses for a gateway that passes the Responses API through — it preserves reasoning state between turns, which Chat Completions cannot. openai:gpt-5 with no base URL always calls OpenAI's Responses API directly.

The variable is rejected in preflight where it cannot take effect: with a non-openai model, since Anthropic, xAI, and Bedrock each serve one API, and with no SHANNON_AI_BASE_URL, since a direct OpenAI run is always Responses.

npx @keygraph/shannon setup covers this under Custom Base URL, which asks which API your gateway serves and configures the matching provider for you.

Validation Copy Chapter

Checks run before a scan starts, so mistakes fail immediately rather than partway through a run:

• Provider and model ID — validated against the Pi harness catalogue. An unknown provider or model ID fails preflight with a pointer to pi.dev/models. A custom base URL exempts the model ID, since a gateway may serve its own names.
• Credential presence — always validated for the selected provider.
• Credential validity — one minimal request against the model the scan will use, so a rejected key, an exhausted quota, or a model the account cannot reach fails before any agent runs. Bedrock included: its bearer token and region go through the same probe.

Migrating from the three-tier configuration Copy Chapter

Earlier versions took three model variables. They no longer do anything — replace them with SHANNON_AI_MODEL.

In ~/.shannon/config.toml, the [models] section and bedrock.use are gone, each provider has its own section, and the model lives at core.model:

[core]
model = "anthropic:claude-sonnet-4-6"
# base_url = "https://llm-gateway.example.com"

[anthropic]
api_key = "your-api-key"

Re-run npx @keygraph/shannon setup to regenerate the file.

File: docs/platforms.md

Platforms and Networking

This guide covers platform-specific notes and Docker networking behavior.

Windows Copy Chapter

Shannon on Windows is supported through WSL2. Native Windows, including Git Bash, is not supported.

Ensure WSL2 Copy Section

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

# Check installed distros.
wsl --list --verbose

# If you do not have a distro, install one.
wsl --list --online
wsl --install Ubuntu-24.04

# If your distro shows VERSION 1, convert it to WSL2.
wsl --set-version <distro-name> 2

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

Run Shannon inside WSL:

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

Source-build equivalent:

git clone https://github.com/KeygraphHQ/shannon.git
cd shannon
cp .env.example .env
./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. Add an exclusion for the Shannon directory or use Docker/WSL2 isolation.

Linux Copy Chapter

Linux works with native Docker. Depending on your Docker setup, you may need sudo. If output files have permission issues, ensure your user has access to the Docker socket and workspace directory.

macOS Copy Chapter

macOS works with Docker Desktop installed.

Testing Local Applications Copy Chapter

Docker containers cannot reach localhost on your host machine. Use host.docker.internal instead:

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

Source-build equivalent:

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

Custom Hostnames Copy Chapter

If your local stack uses custom hostnames mapped in /etc/hosts, Shannon forwards those entries into the worker container at scan start.

To disable forwarding:

export SHANNON_FORWARD_HOSTS=false

In source-build mode, you can also add this to .env:

SHANNON_FORWARD_HOSTS=false

File: docs/workspaces.md

Workspaces and Resuming

Shannon uses workspaces to store scan state, logs, prompts, and deliverables. Workspaces allow interrupted or failed runs to resume without re-running completed agents.

How Workspaces Work Copy Chapter

• Every run creates a workspace.
• Auto-named workspaces use the target hostname and a session ID, such as example-com_shannon-1771007534808.
• npx mode stores workspaces in ~/.shannon/workspaces/.
• Source-build mode stores workspaces in ./workspaces/.
• Use -w <name> to give a run a custom name.
• To resume a run, pass the same workspace name with -w.
• Each agent's progress is checkpointed so resumed runs can skip completed work.
• The final report is surfaced at the workspace root as Security-Assessment-Report.pdf and Security-Assessment-Report.md. Run internals — deliverables, logs, prompts, and session state — live under a hidden .shannon/ directory.

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

Examples Copy Chapter

Start with a named workspace:

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

Resume the same workspace:

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

Resume an auto-named workspace:

npx @keygraph/shannon start -u https://example.com -r /path/to/repo -w example-com_shannon-1771007534808

List completed scans:

npx @keygraph/shannon scans

Source-build 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 example-com_shannon-1771007534808
./shannon scans

File: docs/safety.md

Safety and Limitations

Read this before running Shannon in a new environment.

Authorized Use Only Copy Chapter

Shannon is designed for legitimate security auditing. You must have explicit written authorization from the owner of the target system before running Shannon.

Unauthorized scanning or exploitation of systems you do not own is illegal. Keygraph is not responsible for misuse of Shannon.

Do Not Run on Production Copy Chapter

Shannon is not a passive scanner. Exploitation agents actively execute attacks to confirm vulnerabilities. This can mutate application state and data.

Do not run Shannon against production systems. Use sandboxed, staging, or local development environments where data integrity is not a concern.

Potential mutative effects include:

• Creating new users
• Modifying or deleting data
• Compromising test accounts
• Triggering unintended side effects from injection attacks
• Generating unexpected outbound traffic
• Writing exploit artifacts to reports or deliverables

For maximum isolation, run Shannon inside a disposable virtual machine.

LLM and Automation Caveats Copy Chapter

• Verification is required: Shannon uses a proof-by-exploitation methodology, but final reports can still contain weakly supported or incorrect details. Human review is essential.
• Model support: Shannon is officially supported only with Claude models. Alternative models may be incomplete, inaccurate, or unstable.
• Prompt injection risk: Do not point Shannon at untrusted or adversarial codebases. AI-powered tools that read source code can be influenced by malicious repository content.

Scope of Analysis Copy Chapter

Shannon currently targets exploitable vulnerabilities in these classes:

• Broken Authentication
• Broken Authorization
• Injection
• Cross-Site Scripting
• Server-Side Request Forgery

Shannon's proof-by-exploitation model means it does not report issues it cannot actively exploit, such as many vulnerable dependency, insecure configuration, or broad policy findings.

For broader coverage, the Keygraph platform adds black-box and white-box agentic pentesting, graph-based static analysis, SCA reachability, secrets detection, business logic testing, remediation workflows, SLA tracking, and reporting dashboards.

Cost and Performance Copy Chapter

A full test run typically takes roughly 1 to 1.5 hours. LLM API costs vary by model pricing, target complexity, selected provider, and concurrency.

File: docs/coverage-roadmap.md

Coverage and Roadmap

Shannon focuses on exploitable findings that can be validated against a running application.

Current Shannon Coverage Copy Chapter

• Broken Authentication
• Broken Authorization
• Injection
• Cross-Site Scripting
• Server-Side Request Forgery

Reporting Philosophy Copy Chapter

Shannon follows a proof-by-exploitation model. Findings that cannot be demonstrated with a working proof of concept are not included in the final report.

This reduces speculative noise, but it also means Shannon does not aim to report every possible security issue in a repository. In particular, many dependency, policy, configuration, and broad static-analysis findings are outside the core Shannon workflow.

Roadmap Direction Copy Chapter

Planned coverage areas should continue to live in the repository's canonical roadmap document if one exists. The README should link to that document rather than carrying detailed roadmap history inline.

For organizations that need broader static and organizational coverage now, see the Keygraph platform.

File: docs/keygraph-platform.md

Keygraph Platform

The Keygraph platform is Keygraph's commercial continuous pentesting and AppSec platform for teams running security across many repositories, services, and environments. While Shannon is a local white-box pentesting CLI, the Keygraph platform is a complete AppSec system: it combines parsed-code SAST, source-to-sink analysis, black-box and white-box agentic pentesting, verified remediation, CI/CD gating, SLA tracking, and reporting for security and compliance teams.

This repository contains Shannon, the AGPL-3.0 open-source CLI for strictly white-box pentesting. The Keygraph platform supports both white-box and black-box agentic pentesting and adds static analysis, finding management, remediation workflows, reporting, and enterprise deployment options.

Who Should Consider the Keygraph Platform Copy Chapter

The Keygraph platform is intended for organizations that need:

• Continuous AppSec coverage across many repositories and services
• White-box pentesting when source code is available
• Black-box pentesting against deployed applications and APIs without source-code access
• Agentic SAST, SCA with reachability, secrets scanning, IaC scanning, container scanning, and business logic testing
• Canonical finding management, deduplication, ownership, status tracking, and severity tracking
• Sync into developer workflows, including ticketing and source-control systems
• User-initiated remediation with verification before delivery
• SLA tracking, reporting dashboards, and compliance evidence
• Commercial support
• Self-hosted, air-gapped, BYOK, and customer-controlled LLM gateway deployment options

Full Vulnerability Lifecycle Copy Chapter

The Keygraph platform is designed to cover the full vulnerability lifecycle, not only discovery:

1. Find exploitable issues with white-box pentesting, black-box pentesting, SAST, SCA, secrets, IaC, container, and business logic testing.
2. Normalize results into canonical findings so duplicate scanner outputs become one tracked vulnerability per repository.
3. Prioritize findings using exploit evidence, reachability, severity, ownership, and business context.
4. Sync work into developer workflows through ticketing and source-control integrations.
5. Remediate with user-initiated patch generation when teams want help moving from evidence to code changes.
6. Verify fixes by re-running the relevant scanner or exploit workflow before a remediation is delivered.
7. Track ownership, status, SLAs, MTTR, and drift over time.
8. Report through dashboards for risk, trends, compliance evidence, and security program operations.

Pentesting Modes Copy Chapter

Shannon is strictly white-box: it requires access to the target application's source code and repository layout.

The Keygraph platform supports two pentesting modes:

• White-box agentic pentesting: Agents use source-code context to understand architecture, identify realistic attack paths, and validate exploitability against the running application.
• Black-box agentic pentesting: Agents test deployed applications and APIs without source-code access, useful for third-party surfaces, production-like external validation, or environments where source access is unavailable.

Both modes follow the same core principle: do not report what might be vulnerable when an exploit can prove what is vulnerable.

AppSec Coverage Copy Chapter

The Keygraph platform combines agentic pentesting with broader AppSec coverage:

• Agentic SAST: Code Property Graph analysis with LLM reasoning for data flow, context, and sanitization decisions.
• SCA with reachability: Dependency vulnerability analysis that prioritizes issues reachable from application entry points.
• Secrets scanning: Detection and validation of credentials, tokens, and API keys.
• Business logic testing: Authorization bypass, IDOR, workflow abuse, state-machine flaws, race conditions, and other application-specific logic issues.
• IaC scanning: Terraform, CloudFormation, Kubernetes, Helm, and related infrastructure configuration checks.
• Container scanning: Vulnerable packages, exposed secrets, and misconfigurations across image layers.

Static-Dynamic Correlation Copy Chapter

Static-dynamic correlation is a core product difference. A static finding, such as unsanitized input reaching a SQL query, is not treated as a purely theoretical issue. It is sent to an exploit agent, tested against the live application, and traced back to the exact source-code location when confirmed.

The result is a finding with proof of exploitability, source context when available, ownership, status, SLA, remediation history, and reporting metadata.

Enterprise Deployment Copy Chapter

The Keygraph platform supports enterprise deployment patterns for teams with strict data, model, and network requirements:

• Self-hosted deployments inside the customer's cloud or infrastructure
• Air-gapped deployments for isolated environments
• Strict BYOK model access using customer-managed model credentials
• Customer-controlled LLM gateway patterns for routing, policy, logging, and isolation
• Enterprise identity and provisioning such as SSO and SCIM
• Deep integrations with source control, ticketing, chat, registries, and cloud environments

Deployments can be designed so source code, scan results, prompts, completions, and model traffic remain inside the customer's security perimeter.

Capability Comparison Copy Chapter

Contact Copy Chapter

Learn more on the Keygraph website, start a free trial, book a Keygraph demo, or contact [email protected].