Cybersecurity AI: An open Bug Bounty-ready Artificial Intelligence

A lightweight, ergonomic framework for building bug bounty-ready Cybersecurity AIs (CAIs).

Why CAI?

The cybersecurity landscape is undergoing a dramatic transformation as AI becomes increasingly integrated into security operations. We predict that by 2028, AI-powered security testing tools will outnumber human pentesters. This shift represents a fundamental change in how we approach cybersecurity challenges. AI is not just another tool – it’s becoming essential for addressing complex security vulnerabilities and staying ahead of sophisticated threats. As organizations face more advanced cyber attacks, AI-enhanced security testing will be crucial for maintaining robust defenses.

This work builds upon prior efforts and similarly, we believe that democratizing access to advanced cybersecurity AI tools is vital for the entire security community. That’s why we’re releasing Cybersecurity AI (CAI) as an open source framework. Our goal is to empower security researchers, ethical hackers, and organizations to build and deploy powerful AI-driven security tools. By making these capabilities openly available, we aim to level the playing field and ensure that cutting-edge security AI technology isn’t limited to well-funded private companies or state actors.

Bug Bounty programs have become a cornerstone of modern cybersecurity, providing a crucial mechanism for organizations to identify and fix vulnerabilities in their systems before they can be exploited. These programs have proven highly effective at securing both public and private infrastructure, with researchers discovering critical vulnerabilities that might have otherwise gone unnoticed. CAI is specifically designed to enhance these efforts by providing a lightweight, ergonomic framework for building specialized AI agents that can assist in various aspects of Bug Bounty hunting – from initial reconnaissance to vulnerability validation and reporting. Our framework aims to augment human expertise with AI capabilities, helping researchers work more efficiently and thoroughly in their quest to make digital systems more secure.

You might be wondering if releasing CAI in-the-wild given its capabilities and security implications is ethical. Our decision to open-source this framework is guided by two core ethical principles:

1. Democratizing Cybersecurity AI: We believe that advanced cybersecurity AI tools should be accessible to the entire security community, not just well-funded private companies or state actors. By releasing CAI as an open source framework, we aim to empower security researchers, ethical hackers, and organizations to build and deploy powerful AI-driven security tools, leveling the playing field in cybersecurity.

2. Transparency in AI Security Capabilities: Based on our research results, understanding of the technology, and dissection of top technical reports, we argue that current LLM vendors are undermining their cybersecurity capabilities. This is extremely dangerous and misleading. By developing CAI openly, we provide a transparent benchmark of what AI systems can actually do in cybersecurity contexts, enabling more informed decisions about security postures.

CAI is built on the following core principles:

• Cybersecurity oriented AI framework: CAI is specifically designed for cybersecurity use cases, aiming at semi- and fully-automating offensive and defensive security tasks.
• Open source, free for research: CAI is open source and free for research purposes. We aim at democratizing access to AI and Cybersecurity. For professional or commercial use, including on-premise deployments, dedicated technical support and custom extensions reach out to obtain a license.
• Lightweight: CAI is designed to be fast, and easy to use.
• Modular and agent-centric design: CAI operates on the basis of agents and agentic patterns, which allows flexibility and scalability. You can easily add the most suitable agents and pattern for your cybersecuritytarget case.
• Tool-integration: CAI integrates already built-in tools, and allows the user to integrate their own tools with their own logic easily.
• Logging and tracing integrated: using phoenix, the open source tracing and logging tool for LLMs. This provides the user with a detailed traceability of the agents and their execution.
• Multi-Model Support: more than 300 supported and empowered by LiteLLM. The most popular providers:
  • Anthropic: Claude 3.7, Claude 3.5, Claude 3, Claude 3 Opus
  • OpenAI: O1, O1 Mini, O3 Mini, GPT-4o, GPT-4.5 Preview
  • DeepSeek: DeepSeek V3, DeepSeek R1
  • Ollama: Qwen2.5 72B, Qwen2.5 14B, etc

Install