The First LLM Built for Operators. Not Benchmarks.
Deep Hat V2, at just 30B parameters, decisively outperforms models up to 4x its size, including GPT-OSS-120B and Llama-Scout across professional CTF challenges, threat intelligence tasks, enterprise DevOps tooling and offensive security scenarios.
AI You Control for DevSecOps and Red Teams
Deep Hat executes inside Kindo and stays within your environment with no external calls and no exposure of sensitive data. It works natively with your CLI, shell, and security stack to deliver uncensored, long-context analysis and support the types of security tasks that require privacy and precision.
About Deep Hat
Deep Hat is trained on real-world attack patterns, operator-inspired tasks, and a continuous flow of high-signal security data to support deep analysis, exploit exploration, adversary emulation, and multi-step offensive reasoning. Deep Hat works inside Kindo, giving you private execution, long-context understanding, and the ability to automate both offensive and defensive workflows across your environment. It is designed for sensitive, technical work that commercial models cannot support.
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Our Mission
We strive to be the best uncensored Gen AI security model available anywhere.
To do this we:
- Continually evaluate base models looking for the best coding LLMs available.
- Maintain the most comprehensive training data-set for cybersecurity that's currently available.
- Work to avoid model censorship, prompt by prompt.
Kindo's AI Brain
Deep Hat carries forward its open source roots, and has evolved into Kindo’s proprietary security model, designed for precise, autonomous workflows across SecOps, DevOps, and ITOps. It is trained for real operational tasks and fully optimized for the agentic execution Kindo provides. Deep Hat is the strongest choice for security work inside Kindo, the AI-native terminal for technical ops, where every model operates safely, privately, and reliably.
See Hugging Face project ->Capabilities
Built for control and trust. Our model is fine-tuned on real infrastructure data, not scraped content. With full model transparency, secure retraining, and on-premise deployment, Deep Hat puts enterprises, not vendors, in charge of their automation future.
MoE Architecture Built for Long, Complex Security Tasks
Deep Hat v2 moves from a dense architecture to a Mixture of Experts design that routes each token to the most relevant expert networks. This delivers significant performance gains and supports very long context windows, which are essential for analyzing large logs, multi-step exploit paths, and complex operational workflows. Deep Hat processes more data in a single pass and does it with greater efficiency inside your own environment.
Uncensored AI Models
Censored models refuse large portions of real offensive and investigative work. In security, those blind spots break workflows and distort analysis. Deep Hat avoids these limitations by allowing full offensive reasoning, sensitive content, and realistic adversary modeling. It is trained on real attack vectors, operator-inspired tasks, dark web signals, and complex multi-turn scenarios, which gives it the ability to analyze footholds, explore exploitability, chain findings, and reason through full attack paths. This uncensored foundation is what allows Kindo to support autonomous red team operations and deep defensive workflows with accuracy.
Open Source Roots
Deep Hat began as WhiteRabbitNeo, an open source security model created to explore how LLMs could support real-world offensive and defensive tasks. That early project helped shape the taxonomy, data strategy, and operator workflows that Deep Hat builds on today.Kindo has since evolved the work into a proprietary security model designed for private execution and advanced DevSecOps and red team operations. The original open source project remains available for reference.
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