CAI: An Open, Bug Bounty-Ready Cybersecurity AI
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Abstract (arXiv)
By 2028 most cybersecurity actions will be autonomous, with humans teleoperating. We present the first classification of autonomy levels in cybersecurity and introduce Cybersecurity AI (CAI), an open-source framework that democratizes advanced security testing through specialized AI agents. Through rigorous empirical evaluation, we demonstrate that CAI consistently outperforms state-of-the-art results in CTF benchmarks, solving challenges across diverse categories with significantly greater efficiency -up to 3,600x faster than humans in specific tasks and averaging 11x faster overall. CAI achieved first place among AI teams and secured a top-20 position worldwide in the "AI vs Human" CTF live Challenge, earning a monetary reward of 750. Based on our results, we argue against LLM-vendor claims about limited security capabilities. Beyond cybersecurity competitions, CAI demonstrates real-world effectiveness, reaching top-30 in Spain and top-500 worldwide on Hack The Box within a week, while dramatically reducing security testing costs by an average of 156x. Our framework transcends theoretical benchmarks by enabling non-professionals to discover significant security bugs (CVSS 4.3-7.5) at rates comparable to experts during bug bounty exercises. By combining modular agent design with seamless tool integration and human oversight (HITL), CAI addresses critical market gaps, offering organizations of all sizes access to AI-powered bug bounty security testing previously available only to well-resourced firms -thereby challenging the oligopolistic ecosystem currently dominated by major bug bounty platforms.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2025.
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Citing papers per year: 2025: 8; 2026: 5.
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- 2025Cyber-Zero: Training Cybersecurity Agents without Runtimecited by 22
- 2025Training Language Model Agents to Find Vulnerabilities with CTF-Dojocited by 15
- 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agentscited by 8
- 2025Evaluating AI cyber capabilities with crowdsourced elicitationcited by 5
- 2025Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learningcited by 4
- 2025Automated Penetration Testing with LLM Agents and Classical Planningcited by 4
- 2026Cochise: A Reference Harness for Autonomous Penetration Testingcited by 3
- 2025HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilitiescited by 2
- 2026AI Agents Enable Adaptive Computer Wormscited by 2
- 2025Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFscited by 1
- 2026A Dual-Loop Agent Framework for Automated Vulnerability Reproductioncited by 1
- 2026AWE: Adaptive Agents for Dynamic Web Penetration Testingcited by 1
- 2026Automating Attack Graph Construction for Agentic Pentesting. Towards Neuro-Symbolic Vulnerability Hunting14 Sep 2026
- 2026AI Agents Enable Adaptive Computer Worms2 Jun 2026
- 2026Cochise: A Reference Harness for Autonomous Penetration Testing12 May 2026
- 2026AWE: Adaptive Agents for Dynamic Web Penetration Testing1 Mar 2026
- 2026A Dual-Loop Agent Framework for Automated Vulnerability Reproduction5 Feb 2026
- 2025Automated Penetration Testing with LLM Agents and Classical Planning11 Dec 2025
- 2025Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFs20 Oct 2025
- 2025HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities14 Oct 2025
- 2025Training Language Model Agents to Find Vulnerabilities with CTF-Dojo25 Aug 2025
- 2025Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learning10 Aug 2025
- 2025Cyber-Zero: Training Cybersecurity Agents without Runtime29 Jul 2025
- 2025Evaluating AI cyber capabilities with crowdsourced elicitation26 May 2025
1 more
- 2025Cyber-Zero: Training Cybersecurity Agents without Runtimecited by 22
- 2025Training Language Model Agents to Find Vulnerabilities with CTF-Dojocited by 15
- 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agentscited by 8
- 2025Automated Penetration Testing with LLM Agents and Classical Planningcited by 4
- 2025HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilitiescited by 2
- 2026AI Agents Enable Adaptive Computer Wormscited by 2
- 2025Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFscited by 1
- 2026A Dual-Loop Agent Framework for Automated Vulnerability Reproductioncited by 1
- 2026AWE: Adaptive Agents for Dynamic Web Penetration Testingcited by 1
- 2026AI Agents Enable Adaptive Computer Worms2 Jun 2026
- 2026AWE: Adaptive Agents for Dynamic Web Penetration Testing1 Mar 2026
- 2026A Dual-Loop Agent Framework for Automated Vulnerability Reproduction5 Feb 2026
- 2025Automated Penetration Testing with LLM Agents and Classical Planning11 Dec 2025
- 2025Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFs20 Oct 2025
- 2025HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities14 Oct 2025
- 2025Training Language Model Agents to Find Vulnerabilities with CTF-Dojo25 Aug 2025
- 2025Cyber-Zero: Training Cybersecurity Agents without Runtime29 Jul 2025
- 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents15 May 2025