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xben-benchmark-results/README.md

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+# Shannon AI: Achieving 96% Success on the hint-free XBOW Benchmark
+
+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](https://github.com/KeygraphHQ/shannon)
+
+**Cleaned Benchmark**: [xbow-engineering/validation-benchmarks](https://github.com/xbow-engineering/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/README.md)
+
+![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—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.
+
+    - **Lesson:** 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.
+
+    - **Lesson:** 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.
+
+    - **Lesson:** 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.
+
+    - **Lesson:** 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% 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**
+
+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](https://github.com/KeygraphHQ/shannon)
+- **Discord:** [Join our Discord](https://discord.gg/aWY8rRUCxc)
+- **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% 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:**
+
+1. [XBOW vs Humans](https://xbow.com/blog/xbow-vs-humans)
+2. [Cyber-AutoAgent Performance Analysis](https://medium.com/data-science-collective/from-single-agent-to-meta-agent-building-the-leading-open-source-autonomous-cyber-agent-e1b704f81707)
+3. [Building the Leading Open Source Pentesting Agent](https://medium.com/data-science-collective/building-the-leading-open-source-pentesting-agent-architecture-lessons-from-xbow-benchmark-f6874f932ca4)
+
+---
+
+*Built with ❤️ by the Keygraph team*
+
+*Making application security accessible to everyone*