Shannon: The AI That Hacks Better Than Humans. 96% Success Rate Finding Real Exploits.

Shannon AI pentester achieves 96% success rate finding real web exploits autonomously. Discovered 20+ critical vulnerabilities in OWASP Juic - Shannon:

What Shannon Does

Shannon is a fully autonomous AI penetration tester that doesn't just flag potential issues—it finds vulnerabilities and proves they're exploitable with working proof-of-concept attacks.

MetricShannonTraditional SASTHuman Pentester Success rate (XBOW)96.15%~40%~85% Time per assessment1-1.5 hoursMinutesDays-weeks Cost per engagement~$50~$500/year$10,000+ False positive rate<5%60-80%10-20% Proof of exploitYesNoYes

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How It Works

The Architecture

Shannon is powered by Anthropic's Claude Agent SDK and operates in three phases:

``` 1. RECONNAISSANCE └─ Ingests source code └─ Maps data flows └─ Identifies attack surface ↓ 2. PARALLEL EXPLOITATION └─ Deploys specialized agents └─ Targets OWASP vulnerabilities: • SQL Injection • XSS (Cross-Site Scripting) • SSRF (Server-Side Request Forgery) • Broken Authentication • IDOR (Insecure Direct Object Reference) ↓ 3. PROOF & REPORTING └─ Executes real exploits └─ Captures evidence └─ Generates pentester-grade reports ```

What Makes It Different

Traditional static analysis flags code patterns that might be vulnerable. Shannon actually exploits the vulnerability to prove it works: - Extracts data from databases via SQL injection - Executes JavaScript in victim browsers via XSS - Bypasses authentication to access admin functions - Provides reproducible proof-of-concept for every finding

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Real Results: OWASP Juice Shop

In testing against OWASP Juice Shop (a deliberately vulnerable application), Shannon discovered:

VulnerabilitySeverityExploit Complete auth bypassCriticalAccessed admin without credentials Database exfiltrationCriticalExtracted all user records Stored XSSHighPersistent script injection SSRF to internal servicesHighAccessed internal APIs IDOR on user profilesMediumViewed other users' data 15+ additional findingsVariousAll with working PoCs Total time: 1 hour 23 minutes. Cost: $47.

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Availability

Shannon Lite (Open Source)

- License: AGPL-3.0 - Repository: github.com/KeygraphHQ/shannon - Best for: Individual developers, open source projects

Shannon Pro (Commercial)

- Pricing: Enterprise licensing - Features: CI/CD integration, compliance reporting, SLA support - Best for: Organizations with security requirements

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Running Shannon

```bash

Clone the repository

git clone https://github.com/KeygraphHQ/shannon cd shannon

Set up environment

export ANTHROPIC_API_KEY=your_key

Run against target (Docker-based)

./shannon scan --target ./your-app --output report.html ```

Shannon supports: - Monorepos and consolidated setups - 2FA login handling - Docker-based isolation - CI/CD pipeline integration

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Security Implications

For Defenders

- Continuous testing becomes affordable - Pre-release scanning catches vulns before deployment - Proof of exploitability helps prioritize fixes

For Attackers

- The same capabilities are available to malicious actors - Attack automation is now accessible to less skilled adversaries - The asymmetry between offense and defense may shift

The Bigger Picture

'Shannon represents a fundamental shift. Security testing at this quality was previously only available to well-funded organizations. Now anyone can run enterprise-grade pentests for $50.' — Security Researcher

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Limitations

- White-box only: Requires source code access - Web apps only: Doesn't test mobile, API-only, or desktop apps - Known vulnerability classes: Won't find novel zero-days - Complex business logic: May miss flaws requiring domain knowledge

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What This Means

AI security tools are reaching a capability threshold where they outperform most human practitioners on routine tasks. Shannon isn't replacing security experts—it's giving every developer access to expert-level testing.

The question isn't whether to use AI for security. It's whether you can afford not to when your adversaries certainly will.

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