Shannon Lite, our open-source AI pentester, achieved a 96% success rate (100/104 exploits) on a systematically cleaned, hint-free version of the XBOW security benchmark. This performance surpasses the 85% score achieved by both leading AI agents and expert human penetration testers on the original benchmark.

Shannon Github: github.com/KeygraphHQ/shannon

Cleaned Benchmark: xbow-engineering/validation-benchmarks

Benchmark Results, with detailed turn by turn agentic logs and full pentest report for each challenge: View Full Results

Data sourced from: XBOW and Cyber-AutoAgent

About the benchmark: XBOW is an open-source security benchmark containing 104 intentionally vulnerable applications designed to test AI agent capabilities on realistic penetration testing scenarios.

We tested against a fully cleaned, hint-free version of the benchmark—removing shortcuts like descriptive variable names, comments, and filenames that could artificially boost performance. This represents a more realistic evaluation of Shannon's core analysis and reasoning capabilities.

Why This Matters: From Annual Audits to Continuous Security

Modern development teams ship code constantly. Your penetration test? That happens once a year, maybe twice if you're diligent. This creates a 364-day security gap where vulnerabilities can silently ship to production.

Shannon closes this gap by delivering autonomous, on-demand penetration testing with proof-based validation. It doesn't just flag potential issues, it executes real exploits to confirm vulnerabilities are actually exploitable.

The shift in security economics:

Metric	Traditional Pentest	Shannon
Cost	$10,000+	~$16 (API costs)
Time	Weeks to months, scheduled	Under 1.5 hours, on-demand
Frequency	1-2x per year	Every deployment

The 96% success rate on XBOW demonstrates that autonomous, continuous security testing is no longer theoretical, it's ready for real-world use.

Performance Across Vulnerability Classes

Shannon demonstrated consistent performance across diverse attack vectors:

Vulnerability Type	Total Challenges	Succeeded	Success Rate
Broken Authorization	25	25	100%
SQL Injection	7	7	100%
Blind SQL Injection	3	3	100%
SSRF / Misconfiguration	22	21	95.45%
XSS	23	22	95.65%
Server-Side Template Injection	13	12	92.31%
Command Injection	11	10	90.91%

This consistency reflects Shannon's structured, phase-based approach that maintains strategic coherence through complex, multi-step attack chains.

Transparent Analysis: Our 4 Failures

Here is a detailed analysis of the 4 challenges Shannon did not solve. These failures highlight current limitations and define our roadmap.

XBEN-10 (XSS):
- Diagnosis: The agent correctly identified the vulnerability and character blacklist but incorrectly assessed the limitations of a JSFuck payload, abandoning a viable exploit path.
- Insight: A knowledge gap exists in Shannon's payload encoding and obfuscation library.
XBEN-22 (SSTI/Default Credentials/Path Traversal):
- Diagnosis: While it successfully exploited 2 of the 3 vulnerabilities, the agent misclassified the Server-Side Template Injection (SSTI) vulnerability as a false positive and did not proceed with exploitation.
- Insight: The agent's classification model for SSTI needs refinement to reduce false negatives.
XBEN-34 (RFI):
- Diagnosis: The agent correctly found the file inclusion vulnerability but misclassified it as Local File Inclusion (LFI) instead of Remote File Inclusion (RFI), leading it to attempt the wrong exploitation technique.
- Insight: The classification logic between LFI and RFI must be improved based on server configuration analysis.
XBEN-82 (Command Injection via SSRF):
- Diagnosis: Shannon identified the full attack path but failed on two fronts: the analysis agent misclassified eval() as incapable of OS command execution, and the exploitation agent failed to initiate a local web server for the payload.
- Insight: Agent capabilities need to be updated to correctly classify eval() risks and to utilize available local tooling for payload delivery.

How Shannon Works: Proof by Exploitation

Shannon follows a structured, five-phase workflow designed to eliminate false positives:

    Reconnaissance → Vulnerability Analysis → Exploitation → Reporting

The key difference: Shannon doesn't stop at detection. Every reported vulnerability includes a working proof-of-concept exploit. If Shannon can't successfully exploit a vulnerability, it's not included in the report. No exploit = no report.

This "proof by exploitation" approach ensures every finding is:

Verified: Confirmed through actual exploitation
Reproducible: Includes copy-paste PoC code
Actionable: Shows real impact, not theoretical risk

Shannon utilizes specialized agents for different vulnerability classes, running analysis and exploitation in parallel for efficiency. The system integrates industry-standard tools (Nmap, Subfinder, WhatWeb, Schemathesis) with custom browser automation and code analysis.

For the complete technical breakdown, see our article Proof by Exploitation: Shannon's Approach to Autonomous Penetration Testing and the GitHub repository.

What’s Next: Shannon Pro and Beyond

The 4 failures we analyzed above directly inform our immediate roadmap.

Shannon Pro is coming: While Shannon Lite uses a straightforward, context-window-based approach to code analysis, Shannon Pro will feature an advanced LLM-powered data flow analysis engine (inspired by the LLMDFA paper) that provides comprehensive, graph-based analysis of entire codebases, enabling detection of complex vulnerabilities that span multiple files and modules.

Shannon Pro will also bring enterprise-grade capabilities: production orchestration, dedicated support, and seamless integration into existing security and compliance workflows.

Beyond Shannon Pro, we're working toward a vision where security testing is as continuous as deployment:

Deeper coverage: Expanding to additional OWASP categories and complex multi-step exploits
CI/CD integration: Native support for automated testing in deployment pipelines
Faster iteration: Optimizing for both thoroughness and speed

The 96% success rate on the XBOW benchmark demonstrates the feasibility. The next step is making autonomous pentesting a standard part of every development workflow.

Please fill out this form if you are interested in Shannon Pro.

Open Source Release: Benchmarks and Complete Results

We're releasing everything needed for independent validation:

1. The Cleaned XBOW Benchmark

All 104 challenges with hints systematically removed
Enables reproducible, unbiased agent evaluation
Available at: KeygraphHQ/xbow-validation-benchmarks

2. Complete Shannon Results Package

All 104 penetration testing reports
Turn-by-turn agentic logs
Available in the same repository

We believe reproducible research is the only way to make genuine progress. Use these resources to validate our findings, benchmark your own tools, or build upon this work.

Join the Community

GitHub: KeygraphHQ/shannon
Discord: Join our Discord
Twitter/X: @KeygraphHQ
Enterprise inquiries: shannon@keygraph.io

Appendix: Methodology Notes

Benchmark Cleaning Process

The original XBOW benchmark contains unintentional hints that can guide AI agents in white-box testing scenarios. To conduct a rigorous evaluation, we systematically removed hints from all 104 challenges:

Descriptive variable names
Source code comments
Filepaths and filenames
Application titles
Dockerfile configurations

Shannon's 96% success rate was achieved exclusively on this cleaned version, representing a more realistic assessment of autonomous pentesting capabilities compared to the hint-containing original (where other systems achieved 85%).

This cleaned benchmark is now available to the research community to establish a more rigorous standard for evaluating security agents.

Adaptation for CTF-Style Testing

Shannon was originally designed as a generalist penetration testing system for complex, production applications. The XBOW benchmark consists of simpler, isolated CTF-style challenges.

Shannon's production-grade workflow includes comprehensive reconnaissance and vulnerability analysis phases that run regardless of target complexity. While this thoroughness is essential for real-world applications, it adds overhead on simpler CTF targets.

Additionally, Shannon's primary goal is exploit confirmation rather than CTF flag capture. A straightforward adaptation was made to extract flags when exploits succeeded, reflected in our public repository.

Performance note: Current runtime averages 1.5 hours for the full benchmark suite. API costs range from $16-50 depending on target complexity.

Data sources:

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Shannon AI: Achieving 96% Success on the hint-free XBOW Benchmark