# 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](https://github.com/KeygraphHQ/shannon) **Cleaned Benchmark**: [xbow-validation-benchmarks](https://github.com/KeygraphHQ/xbow-validation-benchmarks) **Benchmark Results, with detailed turn by turn agentic logs and full pentest report for each challenge**: [View Full Results](https://github.com/KeygraphHQ/shannon/blob/main/xben-benchmark-results/) ![XBOW Performance Comparison](../assets/xbow-performance-comparison.png) *Data sourced from: [XBOW](https://xbow.com/blog/xbow-vs-humans) and [Cyber-AutoAgent](https://medium.com/data-science-collective/building-the-leading-open-source-pentesting-agent-architecture-lessons-from-xbow-benchmark-f6874f932ca4)* **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. --- ## 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. --- ## 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](https://medium.com/@parathan/proof-by-exploitation-shannons-approach-to-autonomous-penetration-testing-010eac3588d3) and the [GitHub repository](https://github.com/KeygraphHQ/shannon). --- ## What’s Next: Shannon Pro and Beyond The 4 failures we analyzed above directly inform our immediate roadmap. **[Shannon Pro](https://keygraph.io/shannon) 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](https://arxiv.org/abs/2402.10754)) 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](https://docs.google.com/forms/d/e/1FAIpQLSf-cPZcWjlfBJ3TCT8AaWpf8ztsw3FaHzJE4urr55KdlQs6cQ/viewform?usp=header). --- ## 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](https://github.com/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. --- ## Join the Community - **GitHub:** [KeygraphHQ/shannon](https://github.com/KeygraphHQ/shannon) - **Discord:** [Join our Discord](https://discord.gg/KAqzSHHpRt) - **Twitter/X:** [@KeygraphHQ](https://x.com/KeygraphHQ) - **Enterprise inquiries:** [shannon@keygraph.io](mailto: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 96.15% 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. --- *Built with ❤️ by the Keygraph team* *Making application security accessible to everyone*