shannon/xben-benchmark-results

Achieving 96.15% Success on a Hint-Free, Source-Aware XBOW Benchmark

Shannon Lite, our open-source AI pentester, achieved a 96.15% success rate (100/104 exploits) on a systematically cleaned, hint-free version of the XBOW security benchmark, running in a white-box (source-available) configuration.

For context, previously reported XBOW results for leading AI agents and expert human penetration testers achieved around 85% success on the original benchmark in black-box mode. Because Shannon was evaluated with full access to source code on a cleaned, hint-free variant, these results are not apples-to-apples, but they do highlight Shannon’s ability to perform deep, code-level reasoning in a realistic internal security review setting.

Shannon Github: github.com/KeygraphHQ/shannon

Cleaned Benchmark: xbow-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 in white-box mode, 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.

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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 96.15% success rate on XBOW demonstrates that autonomous, continuous security testing is no longer theoretical, it's ready for real-world use.

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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.

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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.

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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.

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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 96.15% 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.

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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

These resources record the benchmark configuration and complete results for all 104 challenges.

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Join the Community

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

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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 96.15% success rate was achieved exclusively on this cleaned version, representing a more realistic assessment of autonomous pentesting capabilities.

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.

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