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feat: Shannon 3.0 Agentic SAST (#433)

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* feat(worker): add agentic static analysis

Add the ten-stage Agentic SAST pipeline, confined repository tools, model runtime, prompt templates, and SARIF export.

Make retries, repair sessions, reduced coverage, usage accounting, and model-output drift durable across Temporal replay and resume. Keep retry diagnostics in their actionable closed vocabulary. Package the Mantis-derived license material with the prompts that require it.

* feat(worker): deduplicate static and runtime findings before exploitation

Parse Agentic SAST SARIF into typed observations, enrich and route those observations, and reconcile them with pentest findings before exploitation.

Publish deterministic exploitation queues with stable lineage, exact-path Git commits, retry-safe manifests, named drop reasons, and confined task formation. Reject duplicate producer IDs before commit and adopt either legal provenance shape after a lost acknowledgement.

* feat(config)!: replace vuln_classes with agentic_sast

Wire Agentic SAST and reconciliation into the main pipeline, persist their durable state, and add the Miscellaneous finding and exploitation lane.

Make scan completion, cancellation, partial outcomes, resume identity, and report recovery use the integrated final workflow contract. Introduce the atomic finalization, ordering, renumbering, compaction, and output services that workflow calls. Keep completed Miscellaneous work and report drafts idempotent across resume, preserve public main's default-on exploit SARIF behavior, and describe stage-fallback candidates without claiming they were exported.

BREAKING CHANGE: `vuln_classes` has been removed. Configs containing it now fail validation, and all five core pentest classes run on every scan.

Workspaces created by Shannon 2.x cannot be resumed. Finish or discard in-flight scans before upgrading, then start a new workspace name.

* perf: overlap static analysis and the Miscellaneous lane with the pentest

Run Agentic SAST alongside vulnerability analysis and run Miscellaneous exploitation alongside the specialist exploitation lanes.

Keep reconciliation dependent on the completed static-analysis result while preserving parallel work everywhere that has no data dependency.

* feat(cli)!: default the scan target and add a JSON error contract

List local scans, resolve the active or most recent workspace automatically, and make logs, status, and stop use one canonical scan identity.

Add stable machine-readable failures, richer status output, explicit help errors, and seven-day Temporal retention. Treat absent Temporal pending-activity failures as absent whether the decoder represents them as `null` or missing.

BREAKING CHANGE: `status --json` now returns a fixed `failureMessage`. Read `partialReasons`, `agenticSast`, and `workflow.log` for diagnostic detail.

* feat(logging): trace tool calls and write a log per agent

Record complete tool-call arguments in the workflow log and project each agent's events into its own durable log.

Add agent listing and agent-specific log tailing while preserving byte-exact output and draining log handles before
activities return.

* feat(worker): standardize severity and reporting guidance in exploit prompts

Give every exploit agent the same status, confidence, severity-reasoning, report-writing, credential-handling, and
scope contract.

Apply the same task-formation and SAST-enrichment procedure to the Miscellaneous lane.

* feat(worker): disclose scan coverage and make reporting auditable

Build on the retry-safe finalization foundation to preserve correct identities, source locations, scan dates,
partial-coverage limitations, and consistent report JSON, Markdown, SARIF, and PDF output.

Report Agentic SAST, reconciliation wall-clock time, stage usage, retry spend, and background work without duplicate
or hardcoded totals. Keep report findings canonical, drop cross-class restatements, name enrichment losses, and render
the executive-summary narrative in the PDF.

* chore(license): attribute Mantis and Pi and refresh the docs

Add the final Mantis and Pi notices, license copies, acknowledgements, and residual copyright updates.

Update the README, maintained documentation, contributor guidance, and hand-maintained mirrors to describe Agentic
SAST, reconciliation, the Miscellaneous lane, current CLI behavior, and the final release contract. Correct stale
workspace and container guidance and annotate long-standing internals for maintainers.

* fix(logging): treat a slash as a word separator in agent labels

* feat(cli)!: rebuild scan status around model work

- show Capella stages beneath the concurrent Agentic SAST phase
- attach reconciliation time to the class row it feeds
- hide completed bookkeeping and the duplicate miscellaneous wrapper
- carry validated child-workflow progress into durable parent state
- derive the terminal tree and status JSON from the same phase shape

BREAKING CHANGE: `status --json` replaces phase `parallel` with `children` and `meta`, adds phase summaries and notes plus agent attachment fields, and removes the `analysis-engines` and `operational-work` phases.

* fix(report): drop the empty Critical Findings section from the PDF summary

* fix(sast): align Capella export with the submit-time code-path contract

The export gate required every code_paths entry to be file:line, but submit only
requires the primary sink to be file:line and accepts bare trace steps. A single
malformed trace step therefore dropped an otherwise-valid finding at export.

- add isValidPrimaryCodePath as the one shared primary-sink contract
- validate only the primary at export; buildResult already drops unusable steps
- route the submit-time validator through the same helper so the two cannot drift

* feat(sast): tolerate hygiene-only Capella reductions instead of going partial

A reduction only makes a run partial when it loses real coverage or a whole
finding. Malformed model output, salvaged turn-limit work, and rejected duplicate
verdicts are recorded as evidence but no longer flip the run to partial.

- add reductionIsTolerable: partial only when genuine-loss counts are nonzero
- drive runCapella's partial reasons and display coverage off non-tolerable ones
- keep every reduction in agenticSast.reductions so nothing is lost as evidence

* feat(logging): record the provider reason for a failed agent turn

A failed provider turn collapsed to AGENT_EXECUTION_FAILED/unknown with the
underlying reason discarded, so a model-side rejection or safeguard was
indistinguishable from a transport fault in the error log.

- add safeProviderTurnDetails: write bounded, non-sensitive fields (provider,
  model, responseId, stop reason, tool-in-flight, category, retryable) to error.log
- gate a sanitized errorMessage snippet behind SHANNON_DEBUG_PROVIDER_ERRORS, off by default
- forward SHANNON_DEBUG_PROVIDER_ERRORS from the CLI into the worker container

* fix(cli): keep shannon logs tailing through a Temporal blip

- End the interactive tail on the log's own terminal marker or Ctrl-C, so a
  transient Temporal outage no longer aborts the command with exit 1.
- Rebuild the memoized Temporal client after a failed poll: a wedged gRPC
  channel was cached forever, so "retrying…" could never reconnect.
- Keep start --follow (CI) bounded — a genuinely dead Temporal still fails
  the run instead of hanging.

* fix(worker): correct PDF finding reporting

- Render OWASP category, authentication state, and remediation
- Omit the redundant per-finding exploited status
- Preserve canonical category and field ordering across report modes
- Continue Proof of Impact numbering across embedded code blocks
- Wrap long PDF code lines without changing canonical report content

* fix: attribute a reconciliation failure to exploitation only

- Stop marking a class's vulnerability-analysis agent failed when that agent
  succeeded and only reconciliation failed; the status tree now renders the
  analysis row completed and the exploitation row failed
- Consume the worker's failedReconciliations signal in the CLI, which the
  mirrored PipelineState already declared but never read
- Correct the class_reconciliation_failed message, which claimed the class's
  analysis results were still in the report when the class is excluded from it

* fix(pi): give each task sub-session its own resource loader to prevent stale extension ctx

* fix(prompts): scope exploit agents to in-band proof, mark OOB-only findings blocked

* fix(cli): reject a shell credential that shadows a gateway config.toml key

* fix(cli): make scan shutdown verifiable

- preselect and persist workflow identity before worker launch
- cancel first, then verify bounded Temporal termination
- reconcile Docker workers with Temporal open workflows
- fail closed on stale images and unavailable lifecycle state
- mark cancellation only after confirmed shutdown

* feat(cli): prompt for setup on a bare npx invocation with no credentials

* fix(cli): don't blame anthropic when no credentials are configured at all

* chore(release): bump beta base version to 3.0.0

* feat(cli): show a 'start your first scan' box in help on a TTY

* docs: refresh README and platform overview for Shannon 3.0

- lead with the 3.0 launch note and rewrite key capabilities around security
  code analysis, the rebuilt terminal experience, native CI/CD, and PDF/SARIF
- recast the editions table as Shannon Open Source against the Keygraph
  Enterprise Platform, stating open source is not a trial edition
- rewrite the platform overview around exhaustive agentic SAST, canonical
  findings, automated remediation, targeted verification, and governance
- add five product screenshots under assets/keygraph-platform/, referenced
  relative to docs/

* docs: add the Shannon naming section and swap in the 3.0 demo GIF

- explain the Claude Shannon information-theory origin under "What is Shannon?"
- point "Shannon in Action" at the 3.0 recording in assets/Shannon3GIF.gif

Both taken from the README half of #438.

* docs: document CI/CD integrations and the reconciled analysis pipeline

- add a CI/CD Integrations section covering the official GitHub Action and
  GitLab component, pipeline artifacts, and exploit-only severity gates
- redraw the architecture section as a Mermaid flow: agentic code analysis
  and recon feed finding reconciliation, then exploitation and reporting
- describe open-source code analysis as a multi-stage agentic workflow and
  reserve parsed-code CPGs and exhaustive verification for Enterprise
- sharpen the privacy wording: results stay local, but model requests carry
  source context to whichever endpoint you configure
- drop the "not recommended" framing on local models and add a section on
  why Shannon complements rather than replaces human pentesters
- regenerate llms-full.txt from the updated README and docs

* docs: add the Photoview benchmark across three models

- Add a "Shannon in Action" table for Photoview 2.4.0 runs on
  DeepSeek v4 Flash, Grok 4.6, and Claude Opus 5, each linking its
  PDF report and SARIF output
- Store the per-model reports under benchmark/
- Link the (forthcoming) benchmark writeup from the section intro

* docs: add the Shannon vs XBOW/Aikido Photoview benchmark writeup

- Add docs/shannon-xbow-aikido-benchmark.md with methodology, per-model
  cost/coverage tables, and links to each model's report and SARIF
- Link the writeup from the README "Shannon in Action" section

* docs: link the benchmark announcement discussion from the README

* fix(readme): restore theme-aware banner, badge, and buttons

* feat!: trigger the Shannon 3.0 major release

---------

Co-authored-by: ezl-keygraph <ezhil@keygraph.io>
This commit is contained in:
Arjun Malleswaran
2026-09-02 14:34:26 +05:30
committed by GitHub
co-authored by ezl-keygraph
parent 6108de3cfc
commit 9767ebe633
267 changed files with 127389 additions and 3268 deletions
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README.md
+185 -94
View File
@@ -1,5 +1,5 @@
> [!NOTE]
> **[Shannon 2.0 is officially here](https://github.com/KeygraphHQ/shannon/discussions/405)**
> **Shannon 3.0 is live:** deeper security code analysis, a rebuilt terminal experience, native CI/CD workflows, professional PDF reports, and SARIF—still fully open source, self-hosted, and bring-your-own-model.
<div align="center">
@@ -11,9 +11,9 @@
<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, AI pentester for web applications and APIs.
### Shannon is an autonomous, AI pentester for web applications and APIs.
It analyzes your source code, identifies attack paths, and executes real exploits to prove vulnerabilities before they reach production.
It analyzes your source code, identifies attack paths, and executes real exploits to prove vulnerabilities before they reach production. **No exploit, no report.**
**This repository is Shannon Open Source: the full agent, run locally from your command line.**
@@ -28,12 +28,22 @@ It analyzes your source code, identifies attack paths, and executes real exploit
> [!TIP]
> **AI agents and LLMs:** start with [llms.txt](llms.txt) for a concise map of this repository, or use [llms-full.txt](llms-full.txt) for the README and docs combined into one file.
## Table of Contents
- [Table of Contents](#table-of-contents)
- [What is Shannon?](#what-is-shannon)
- [Why Shannon Exists](#why-shannon-exists)
- [Why "Shannon"?](#why-shannon)
- [Not a replacement for human pentesters](#not-a-replacement-for-human-pentesters)
- [Shannon in Action](#shannon-in-action)
- [Quick Start](#quick-start)
- [Prerequisites](#prerequisites)
- [Run Shannon](#run-shannon)
- [Key Capabilities](#key-capabilities)
- [CI/CD Integrations](#cicd-integrations)
- [GitHub Actions](#github-actions)
- [Editions](#editions)
- [Architecture](#architecture)
- [Documentation](#documentation)
@@ -42,6 +52,14 @@ It analyzes your source code, identifies attack paths, and executes real exploit
- [About Keygraph](#about-keygraph)
- [Community and Support](#community-and-support)
- [Common Questions](#common-questions)
- [Can I self-host Shannon?](#can-i-self-host-shannon)
- [Does Shannon support bring your own key (BYOK)?](#does-shannon-support-bring-your-own-key-byok)
- [Does Shannon output SARIF?](#does-shannon-output-sarif)
- [Which AI providers does Shannon support?](#which-ai-providers-does-shannon-support)
- [Can I run Shannon on a local or self-hosted model?](#can-i-run-shannon-on-a-local-or-self-hosted-model)
- [Does Shannon actually exploit vulnerabilities, or just scan?](#does-shannon-actually-exploit-vulnerabilities-or-just-scan)
## What is Shannon?
@@ -57,22 +75,43 @@ Thanks to tools like Claude Code and Cursor, your team ships code non-stop. But
Shannon closes that gap by providing on-demand, automated penetration testing that can run against every build or release.
### Why "Shannon"?
It's named after Claude Shannon, the father of information theory. At its core, pentesting is an information problem: every probe reduces uncertainty about a system's state. The best tools maximize the signal gained from every request, turning those bits of knowledge into an exploit path.
Also, we wanted you to be able to say, "Hey Claude, run Shannon" to find all the security flaws in your vibe-coded app.
### Not a replacement for human pentesters
Shannon is built to work alongside expert pentesters and red teamers, not replace them. Great pentesters understand the business, chain attacks in ways nobody anticipated, and bring years of judgment that current models can't match.
Shannon solves a different problem: there is far more software to test than security teams have time to cover. Critical systems get periodic expert assessments, while the long tail of internal apps, APIs, and fast-moving services rarely gets tested at all.
Shannon shifts pentesting left into the software development lifecycle (SDLC). Use it to run exploitation-backed tests against staging environments and releases at the cadence they actually ship, and save expert human time for the risks that need someone who knows the organization.
## Shannon in Action
<p align="center">
<img src="assets/shannon-action.gif" alt="Shannon running an autonomous pentest" width="100%">
</p>
![Shannon running an autonomous pentest](assets/Shannon3GIF.gif)
Penetration test reports from Shannon Open Source scanning Photoview 2.4.0. Read the [announcement][announcement] and the full [benchmark writeup][benchmark] for methodology, cost, and the comparison against Aikido and XBOW.
| Model | Report | SARIF |
| ----------------- | ---------------------------------------------------------------------------- | --------------------------------------------------------------- |
| DeepSeek v4 Flash | [View report](benchmark/photoview-deepseek-v4-flash.pdf) | [SARIF](benchmark/photoview-deepseek-v4-flash.sarif) |
| Grok 4.6 | [View report](benchmark/photoview-grok-4-6.pdf) | [SARIF](benchmark/photoview-grok-4-6.sarif) |
| Claude Opus 5 | [View report](benchmark/photoview-opus-5.pdf) | [SARIF](benchmark/photoview-opus-5.sarif) |
[announcement]: https://github.com/KeygraphHQ/shannon/discussions/439
[benchmark]: docs/shannon-xbow-aikido-benchmark.md
Sample penetration test reports from intentionally vulnerable applications, produced by Shannon Open Source:
| Target | Summary | Report |
| --- | --- | --- |
| OWASP Juice Shop | 20+ vulnerabilities, including authentication bypass, SQL injection, IDOR, and SSRF. | [View report](sample-reports/shannon-report-juice-shop.md) |
| c{api}tal API | Approximately 15 critical and high-severity API findings, including command injection, auth bypass, and mass assignment. | [View report](sample-reports/shannon-report-capital-api.md) |
| OWASP crAPI | 15+ critical and high-severity findings across JWT, injection, SSRF, and API authorization paths. | [View report](sample-reports/shannon-report-crapi.md) |
## Quick Start
### Prerequisites
- **Docker**: required for the worker container.
@@ -80,6 +119,8 @@ Sample penetration test reports from intentionally vulnerable applications, prod
- **AI provider credentials**: Shannon runs on Anthropic, OpenAI, xAI, AWS Bedrock, [any other provider](docs/ai-providers.md#any-other-provider) in the harness catalogue, and any endpoint that speaks the Anthropic Messages API or the OpenAI Chat Completions or Responses API through a [custom base URL](docs/ai-providers.md#custom-base-url). You bring your own key, and Keygraph never proxies your model traffic. Shannon is provider-agnostic. See [AI providers](docs/ai-providers.md#suggested-models) for suggested model IDs.
- **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](docs/ai-providers.md#cyber-safeguards-do-this-before-your-first-scan).
### Run Shannon
> [!WARNING]
@@ -87,10 +128,12 @@ Sample penetration test reports from intentionally vulnerable applications, prod
```bash
# Configure credentials with the interactive wizard.
npx @keygraph/shannon setup
npx @keygraph/shannon@latest setup
# Run a pentest against a source-available target.
npx @keygraph/shannon start -u https://your-app.com -r /path/to/your-repo
npx @keygraph/shannon@latest 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.
@@ -104,91 +147,131 @@ For source builds, authenticated scans, provider-specific setup, and platform no
> - **xAI (Grok):** The latest version of Shannon supports xAI subscriptions. Follow the [xAI subscription setup guide](docs/ai-providers.md#xai-grok-subscription) to get started.
> - **Claude Code:** The latest version of Shannon does not support Claude Code subscriptions. Follow the [Claude Code subscription setup guide](docs/ai-providers.md#claude-code-subscription) to use version `1.9.0`, which is the final release built on the Claude Agent SDK.
## Key Capabilities
- **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.
- **No exploit, no report**: Shannon includes a vulnerability only after validating it with a working, reproducible proof of concept—eliminating the speculative warnings typical of scanners.
- **Advanced security code analysis**: Before it sends a single payload, Shannon reads the codebase and builds a picture of the application: architecture, trust boundaries, exposed interfaces, data flows, and the assets worth attacking. From there it opens targeted investigations and filters the candidates they turn up. What survives goes to the live pentesting agents.
- **Autonomous execution**: Shannon launches reconnaissance, vulnerability analysis, exploitation, and report generation from a single command.
- **Live terminal experience**: A rebuilt CLI makes scans easy to configure and shows agent progress and clean results without requiring operators to inspect the underlying orchestration logs.
- **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.
- **Machine-readable output**: Shannon emits findings as structured JSON, and as SARIF 2.1.0 by default on exploit-mode scans (opt out with `report.sarif: "false"`). SARIF is the OASIS standard for static analysis results, so findings flow into any code scanning service, vulnerability management platform, security dashboard, or CI/CD pipeline that reads it.
- **Bring your own key, provider-agnostic**: Shannon runs on Anthropic, OpenAI, xAI, AWS Bedrock, and any endpoint speaking the Anthropic Messages API or the OpenAI Chat Completions or Responses API, including self-hosted models served through Ollama, vLLM, or LM Studio and gateways such as OpenRouter and LiteLLM. You supply the credentials, so source code and model traffic stay inside your infrastructure. Local and self-hosted models are technically supported but not recommended: they may not follow Shannon's instructions or tool-use constraints as reliably as frontier models, so take that path only if you know how your chosen model behaves.
- **Native CI/CD integrations**: Run Shannon through the official GitHub Action or reusable GitLab CI/CD component. Preserve reports, SARIF, and logs as pipeline artifacts; publish findings into native security workflows; and gate releases only on vulnerabilities Shannon actually demonstrates.
- **Professional and machine-readable reports**: Shannon generates evidence-rich PDF and Markdown reports plus structured JSON and SARIF 2.1.0. SARIF is enabled by default on exploit-mode scans and can be disabled with `report.sarif: "false"`.
- **Bring your own key, provider-agnostic**: Shannon runs on Anthropic, OpenAI, xAI, AWS Bedrock, and any endpoint speaking the Anthropic Messages API or the OpenAI Chat Completions or Responses API, including self-hosted models served through Ollama, vLLM, or LM Studio and gateways such as OpenRouter and LiteLLM. You supply the credentials and choose exactly where model traffic goes. Local and self-hosted models are supported.
- **Private by design**: Shannon runs inside your infrastructure and writes results to a local workspace. Model requests go straight to the provider or endpoint you configure, and they carry source and application context with them, so choose that endpoint deliberately. Point Shannon at a local model endpoint and nothing leaves your environment.
## CI/CD Integrations
Shannon can run continuously against deployed staging and development environments through official integrations for [GitHub Actions](https://github.com/KeygraphHQ/shannon-action) and [GitLab CI/CD](https://gitlab.com/KeygraphHQ/shannon-ci).
Both integrations:
- analyze the checked-out source repository while attacking a running target;
- preserve PDF, Markdown, and SARIF reports as pipeline artifacts;
- preserve scan and agent logs for debugging, including incomplete runs;
- support pull-request, release, and scheduled pentests;
- distinguish an incomplete assessment from a completed scan with no findings; and
- optionally fail the pipeline when Shannon exploits a vulnerability at or above a configured severity threshold.
A code-analysis hypothesis does not fail the pipeline. Severity gates count only findings with `status: exploited`.
### GitHub Actions
```yaml
name: Shannon Pentest
on:
workflow_dispatch:
permissions:
security-events: write
jobs:
pentest:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Run Shannon
uses: KeygraphHQ/shannon-action@v1
with:
url: https://staging.example.com
api-key: ${{ secrets.SHANNON_AI_API_KEY }}
fail-on-severity: high
upload-sarif: true
```
The Action defaults `repo` to the checked-out GitHub workspace. It uploads one artifact containing the security assessment reports and SARIF, plus a separate run artifact containing scan and agent logs. Enabling `upload-sarif` publishes supported findings to GitHub code scanning.
Requirements:
- a private repository;
- a runner with Docker and Docker Compose v2;
- access to the running staging or development target; and
- a model-provider credential stored as a GitHub Actions secret.
See the [Shannon GitHub Action documentation](https://github.com/KeygraphHQ/shannon-action) and [GitHub Marketplace listing](https://github.com/marketplace/actions/shannon-ai-pentester).
## Editions
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** is the complete autonomous pentester for developers and security teams. It is optimized for fast local and CI/CD runs: understand the application, execute real attacks, and report only proven vulnerabilities.
**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.
**Keygraph Enterprise Platform** turns Shannon's proof engine into an organization-wide AppSec program, adding exhaustive analysis, centralized vulnerability management, automated remediation, enterprise governance, and continuous operation at scale.
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 Open Source | Keygraph Enterprise Platform |
| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Best for | Local and CI/CD pentesting | Continuous AppSec across teams and repositories |
| Security analysis | Multi-stage agentic review models architecture, trust boundaries, and data flows, filters candidate vulnerabilities, and hands the survivors to live pentesting agents | Exhaustive parsed-code agentic SAST: persistent Code Property Graphs, interprocedural source-to-sink and sanitizer modeling, cross-repository context, exploit-chain analysis, and business-logic testing |
| Additional coverage | Not included | SCA with reachability, secrets scanning, and business-logic testing |
| AppSec operations | N/A — standalone CLI | Canonical findings, deduplication, SLAs, analytics, automated remediation, and targeted verification |
| Governance | N/A — local, single-operator CLI | SSO, SCIM, granular access control, APIs, and full audit logging |
| Deployment | Self-hosted, air-gapped, BYOM, AGPL-3.0 | On-premises or air-gapped, granular model routing, commercial support |
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.
| AppSec lifecycle stage | Shannon Open Source | Keygraph platform |
| --- | --- | --- |
| Analyze | Basic LLM pass-through of source to plan attacks | Actual code-base parsing, plus Code Property Graph, SAST, SCA with reachability, secrets, IaC, and containers |
| Pentest and prove | White-box only, proof by exploitation | Enhanced white-box, plus black-box and grey-box modes, run continuously |
| Manage findings | Local Markdown report | Canonical findings system: deduplication across sources, ownership, SLA, dashboards, Jira sync, and professional pentest-grade PDF reports |
| Remediate and verify | Fix manually from the report, then re-run the full scan to verify | Automated remediation: opens a PR with the fix, verified by point re-test without re-running the full scan |
| Deploy and operate | Local CLI and Docker worker | Self-hosted, air-gapped, BYOK, continuous, enterprise integrations |
| License and support | AGPL-3.0, community | Commercial, supported |
Shannon Open Source is not a trial edition. Choose Keygraph Enterprise when you need deeper analysis and a governed, closed-loop AppSec program.
Learn more on the [Keygraph website](https://keygraph.io), read the [Keygraph platform technical overview](docs/keygraph-platform.md), start a free trial or book a [demo](https://cal.com/team/keygraph/shannon-pro), or contact [shannon@keygraph.io](mailto:shannon@keygraph.io).
[Explore the Keygraph Enterprise Platform →](docs/keygraph-platform.md)
## Architecture
Shannon uses a multi-agent workflow that combines source-code analysis with live exploitation:
Shannon combines multi-stage security code analysis with live reconnaissance and exploitation:
```text
┌──────────────────────┐
│ Pre-Reconnaissance │
│ (source code scan) │
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ Reconnaissance │
│ (attack surface │
│ mapping) │
└──────────┬───────────┘
│
▼
┌──────────┴───────────┐
│ │ │
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────┐
│ Vuln │ │ Vuln │ │ ... │
│(Injection)│ │ (XSS) │ │ │
└─────┬─────┘ └─────┬─────┘ └─────┬─────┘
│ │ │
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────┐
│ Exploit │ │ Exploit │ │ ... │
│(Injection)│ │ (XSS) │ │ │
└─────┬─────┘ └─────┬─────┘ └─────┬─────┘
│ │ │
└──────┬───────┴─────────────┘
│
▼
┌──────────────────────┐
│ Reporting │
└──────────────────────┘
```mermaid
flowchart TD
S["Source code"] --> EXISTING["Recon + vulnerability analysis"]
S --> SAST["Agentic security code analysis"]
EXISTING -- "Pentest candidates" --> REC["Finding reconciliation<br/>(merge + deduplicate)"]
SAST -- "SAST candidates" --> REC
REC -- "Reconciled exploitation queue" --> EXP["Exploitation agents"]
APP["Running application"] --> EXP
EXP -- "Exploit demonstrated" --> REPORT["Reporting<br/>PDF · Markdown · SARIF"]
EXP -- "No exploit demonstrated" --> DROP["Discard"]
REPORT --> CICD["CI/CD gate"]
```
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.
Stage by stage:
1. **Recon and vulnerability analysis** explores the running application, ties runtime behavior back to the source, and runs specialized agents across Injection, XSS, SSRF, Authentication, and Authorization.
2. **Agentic security code analysis** maps the application's architecture, trust boundaries, exposed interfaces, dependencies, data flows, and high-risk assets, then opens targeted investigations against them.
3. **Finding reconciliation** merges both streams of candidates, deduplicates the overlap, and groups what remains into an exploitation queue.
4. **Exploitation agents** attempt real proof-of-concept attacks against the running application.
5. **Validation** throws out every candidate Shannon can't demonstrate.
6. **Reporting** produces PDF and Markdown reports with the evidence attached, plus structured JSON and SARIF for downstream systems.
Only live-validated vulnerabilities become Shannon pentest findings or count toward CI/CD severity gates.
Each scan runs in an ephemeral Docker container with an isolated workspace and per-invocation orchestration.
@@ -196,16 +279,20 @@ Each scan runs in an ephemeral Docker container with an isolated workspace and p
Use these guides for operational detail:
| Guide | Use it for |
| --- | --- |
| [Source build and CLI commands](docs/development.md) | Cloning, building, common commands, output paths, and local development. |
| [Configuration](docs/configuration.md) | Authenticated testing, login flows, rules of engagement, and report filters. |
| [AI providers](docs/ai-providers.md) | Selecting the model, the supported providers (Anthropic, OpenAI, xAI, AWS Bedrock, and any other Pi-supported provider), and custom gateways. |
| [Platforms and networking](docs/platforms.md) | Windows/WSL2, Linux, macOS, Docker networking, local apps, and custom hostnames. |
| [Workspaces and resuming](docs/workspaces.md) | Naming workspaces, resuming interrupted scans, and workspace storage. |
| [Safety and limitations](docs/safety.md) | Authorized-use requirements, non-production guidance, mutative effects, cost, and model caveats. |
| [Coverage and roadmap](docs/coverage-roadmap.md) | Current vulnerability coverage and planned work. |
| [Keygraph platform](docs/keygraph-platform.md) | The continuous, agentic pentesting platform: code analysis, black-box and white-box testing, finding management, remediation, verification, and enterprise deployment. |
| Guide | Use it for |
| --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Source build and CLI commands](docs/development.md) | Cloning, building, common commands, output paths, and local development. |
| [Configuration](docs/configuration.md) | Authenticated testing, login flows, rules of engagement, and report filters. |
| [AI providers](docs/ai-providers.md) | Selecting the model, the supported providers (Anthropic, OpenAI, xAI, AWS Bedrock, and any other Pi-supported provider), and custom gateways. |
| [Platforms and networking](docs/platforms.md) | Windows/WSL2, Linux, macOS, Docker networking, local apps, and custom hostnames. |
| [Workspaces and resuming](docs/workspaces.md) | Naming workspaces, resuming interrupted scans, and workspace storage. |
| [Safety and limitations](docs/safety.md) | Authorized-use requirements, non-production guidance, mutative effects, cost, and model caveats. |
| [Coverage and roadmap](docs/coverage-roadmap.md) | Current vulnerability coverage and planned work. |
| [Keygraph Enterprise Platform](docs/keygraph-platform.md) | Exhaustive agentic SAST, continuous pentesting, full-lifecycle finding management, remediation, targeted verification, enterprise governance, and on-premises deployment. |
## Safety, Scope, and Limitations
@@ -215,7 +302,7 @@ You are responsible for using Shannon legally and ethically. Do not point Shanno
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.
- Shannon Open Source is tuned for fast, code-informed pentesting in everyday development and CI/CD. Exhaustive agentic SAST, broader scanner coverage, centralized governance, and full-lifecycle vulnerability management are delivered through the Keygraph Enterprise Platform.
- Findings still require human review. LLM-generated reports can contain weakly supported or incorrect details.
- Anthropic, OpenAI, xAI, and AWS Bedrock are built-in providers, and any Anthropic Messages API or OpenAI Chat Completions or Responses API endpoint works through a custom base URL. Model capability varies, and a model that does not follow Shannon's instructions or tool-use constraints reliably will produce weaker results.
- A full run can take roughly 1 to 1.5 hours and may incur LLM API costs depending on model pricing and application complexity.
@@ -256,11 +343,17 @@ Stay connected:
- [Twitter/X: @KeygraphHQ](https://twitter.com/KeygraphHQ)
- [LinkedIn: Keygraph](https://linkedin.com/company/keygraph)
## Common Questions
### Can I self-host Shannon?
Yes. Shannon Open Source runs entirely on your own infrastructure in an ephemeral Docker container. Your source code is mounted read-only and never leaves your environment.
Yes. Shannon Open Source runs inside your infrastructure in an ephemeral worker container. It mounts the repository read-only and writes results to a local workspace.
Keygraph never receives your source code and never proxies your model traffic. Your model requests go straight to the provider or endpoint you configure, and they carry source and application context with them. Point Shannon at a locally hosted endpoint and that traffic stays inside your environment too.
### Does Shannon support bring your own key (BYOK)?
@@ -276,12 +369,10 @@ Anthropic, OpenAI, xAI, and AWS Bedrock are built in and configured directly by
### Can I run Shannon on a local or self-hosted model?
Technically yes, but it is not recommended. Shannon works with local models served through Ollama, vLLM, or LM Studio, which expose an OpenAI-compatible endpoint, as well as routers such as OpenRouter and gateways such as LiteLLM. Point Shannon at the endpoint with a custom base URL. Capability varies, and a model that does not follow Shannon's instructions or tool-use constraints reliably will produce weaker pentests than a frontier model, so take this path only if you know how your chosen model behaves. See [AI providers](docs/ai-providers.md#custom-base-url).
Shannon works with local models served through Ollama, vLLM, or LM Studio, which expose an OpenAI-compatible endpoint, as well as routers such as OpenRouter and gateways such as LiteLLM. Point Shannon at the endpoint with a custom base URL. Capability varies, and a model that does not follow Shannon's instructions or tool-use constraints reliably will produce weaker pentests than a frontier model, so take this path only if you know how your chosen model behaves. See [AI providers](docs/ai-providers.md#custom-base-url).
### Does Shannon actually exploit vulnerabilities, or just scan?
Shannon executes real exploits. It reports a finding only when it has produced a working proof-of-concept, and discards hypotheses it cannot prove. It is a pentester, not a scanner.
Shannon executes real exploits. It reports a finding only when it has produced a working proof-of-concept, and discards hypotheses it cannot prove. It is a pentester, not a passive scanner.
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