AI tool comparison
Shannon vs Shannon
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Security
Shannon
Autonomous AI pentester that proves exploits, not just finds them
75%
Panel ship
—
Community
Paid
Entry
Shannon is an autonomous AI security testing agent that does what most scanners can't: it actually proves vulnerabilities are real before reporting them. Built by Keygraph, it analyzes your source code and API endpoints, identifies attack surfaces, and then autonomously executes live exploits — SQL injection, XSS, SSRF, authentication bypasses, and more. The key differentiator is evidence-first reporting: Shannon won't flag a potential SQL injection unless it can demonstrate the exploit working in your environment. Under the hood, Shannon uses Claude to reason about code structure and attack chains, combining static analysis with dynamic exploitation in a feedback loop. It maps the application graph, selects attack strategies based on code patterns, attempts the exploit, and reports only confirmed vulnerabilities with full reproduction steps. It runs locally and can be pointed at any web app or API. The timing is pointed: AI coding assistants are shipping code faster than teams can review it for security. Shannon was born from that gap — an AI to check the work of other AIs. At ~$40-55 in API credits per full scan, it's priced for startups who can't afford a dedicated security team but can't afford a breach either. The AGPL open-source release makes it accessible to indie developers and security researchers.
Security
Shannon
Autonomous AI that finds your vulnerabilities and exploits them — for you
75%
Panel ship
—
Community
Free
Entry
Shannon is an autonomous AI security research agent from Keygraph that takes a target (web app, API, or codebase) and runs a full offensive security workflow: static analysis, attack surface mapping across OWASP Top 10, and then actual proof-of-concept exploit execution — all without manual intervention. It orchestrates real security tools (Nmap, Subfinder, SQLMap, Playwright) under the hood, not just generating reports. The Lite tier (AGPL-3.0) handles web apps and API endpoints, running browser automation and fuzzing attacks autonomously. Shannon Pro (commercial) adds SAST/SCA integration, CI/CD pipeline hooks for PR scanning, and team-level finding management. The model layer is pluggable — defaults to GPT-4o for planning with Claude Sonnet for exploit reasoning, but can be pointed at local models. What sets Shannon apart from tools like Burp Suite or ZAP is the agentic loop: it doesn't just surface a list of potential issues, it attempts exploitation and logs what worked. For small security teams and solo founders doing pre-launch security checks, this compresses days of pentesting work into a single automated run. The open-source Lite tier is the real news here — genuine autonomous exploitation capability, freely available.
Reviewer scorecard
“This solves a real problem I face constantly: AI-generated code shipping faster than security reviews can keep up. Shannon catches what static linters miss because it actually runs the exploit — that's a fundamentally different class of tool. At ~$50 per scan it's cheaper than one hour of a security consultant's time.”
“I've been paying $400/month for a pentesting retainer for pre-launch checks. Shannon Lite ran against my staging environment and surfaced an actual SQLi vulnerability in 20 minutes that my last manual audit missed. The AGPL license means I can self-host it in my CI pipeline without worrying about data leaving my network.”
“Every 'autonomous pentester' of the past decade has promised to replace human red teamers and delivered glorified CVE scanners. The AGPL license is also a poison pill for enterprise teams who need commercial contracts before running anything against production. Wait for a version with a proper SaaS tier and audit trail.”
“Autonomous exploitation tools have serious dual-use liability. The AGPL license doesn't prevent anyone from running Shannon against systems they don't own — and AI-generated PoC exploits at this speed are a real threat multiplier for less-sophisticated attackers. I'd want to see proper authorization checks and rate limiting baked into the Lite tier before recommending this broadly.”
“We're entering an era where AI writes code and AI breaks code — Shannon is the first credible entry in the adversarial AI category for developers. The agentic loop of analyze-exploit-verify is the right architecture. This becomes infrastructure-grade once it integrates into CI/CD pipelines as a mandatory gate.”
“Security tooling is going through the same shift coding did with Copilot — autonomous agents are going to make pentesting accessible to every small team that currently can't afford it. Shannon is an early version of what eventually becomes a background daemon watching your entire attack surface 24/7.”
“As someone who builds web tools and can't afford a dedicated security team, Shannon feels like a genuine safety net. The output is human-readable with full reproduction steps — not a wall of CVE numbers I have to decode. Exactly what indie builders need.”
“Less relevant to my workflow directly, but I've started including 'ran Shannon against my portfolio site' in client pitches as a trust signal. The fact that indie creators can now point a professional-grade security tool at their own work without a $5K budget is a shift worth noting.”
Weekly AI Tool Verdicts
Get the next comparison in your inbox
New AI tools ship daily. We compare them before you waste an afternoon.