Shannon logo

Shannon

Autonomous white-box AI pentester by Keygraph that reads your source code, identifies attack vectors, and executes real exploits in a sandbox to prove vulnerabilities rather than just report theoretical ones.

-

Our Verdict

If you ship web apps you can point Shannon at a staging copy of, the proof-by-exploitation model is a meaningful step up from noisy DAST: you get fewer, more actionable findings with reproducible PoCs. Don't run it against prod.

Pros

  • Proof-by-exploitation means zero false positives in the final report — every bug comes with a working PoC
  • 96.15% on the XBOW benchmark (100 of 104 exploit challenges), substantially higher than most commercial DAST
  • Multi-agent architecture runs reconnaissance + parallel vuln analysis + exploitation concurrently
  • Full scans in 1-1.5 hours, with CI integration and 2FA/TOTP authentication support

Cons

  • Only safe in sandbox/staging — exploits can mutate data, create users, trigger side effects
  • Requires your Anthropic API key (or AWS Bedrock / Vertex AI) — ongoing token cost per scan
  • White-box only: source code access required, cannot test third-party SaaS you don't own
  • LLM-generated findings occasionally need human validation despite the exploit verification step
Best for: Engineering teams with a staging environment who want continuous OWASP coverage without paying for human pentests every sprint Not for: Black-box testing scenarios, production environments, or teams with zero tolerance for AI-in-the-loop security decisions

When to Use Shannon

Good fit if you need

  • Catching OWASP-critical bugs (SQLi, XSS, SSRF, broken auth) before prod in staging pipelines
  • Validating fixes by re-running exploits on patched branches
  • Supplementing human pentesters with continuous white-box coverage
  • Monorepo security coverage via Docker + Claude Agent SDK
  • CI/CD security gate with proof-by-exploitation rather than noisy DAST alerts

Not the best choice if

  • Production live traffic — Shannon runs real exploits that can mutate data
  • Black-box third-party apps where you don't own the source code
  • Compliance-only needs requiring certified auditors, not automated tools
  • Air-gapped environments without access to Anthropic API (or AWS Bedrock/Vertex)

Works Well With

Lock-in Assessment

Low 1/5
Lock-in Score
1/5

Open-source AGPL Lite edition, self-hosted, runs in your Docker. Pro tier adds features but Lite remains functional standalone.

Data Portability: Full — all reports are local markdown + JSON; no vendor data lock.

Shannon Pricing

Pricing Model
freemium
Free Tier
Yes
Free Tier Limits
Shannon Lite — AGPL-3.0, unlimited self-hosted scans for sandbox/staging environments
Entry Price
Pro: contact sales
Enterprise Available
Yes
Billing Complexity
Medium
Transparency Score
3/5
View pricing page →

Beta — estimates may differ from actual pricing

1,000
1001K10K100K1M

Estimated Monthly Cost

$25

Estimated Annual Cost

$300

Estimates are approximate and may not reflect current pricing. Always check the official pricing page.

Project Health

high

Health Score

39.4k
Bus Factor

N/A

Last Commit

N/A

Release Freq

21d

Open Issues

N/A

Issue Response

N/A

License

AGPL-3.0

Last checked: 2026-04-22

Community Discussion

Comments powered by Giscus (GitHub Discussions). You need a GitHub account to comment.