Red Team–Grade LLM, Proven in Real Security Tasks
On Cybench’s autonomous CTF benchmark, most models failed. Deep Hat matched models 10× its size, while running fully self-managed in your cloud or on prem. It’s uncensored, built for red teaming and security automation, and understands real infrastructure. No filters, no outside dependencies. Use it with Kindo to unlock autonomous agents, live threat data, and tool access on your terms.
Private, Deploy-Anywhere AI Built for DevSecOps and Red Teams
Unlike generic chatbots or shared model platforms, it runs where you need it, in your own cloud, on-prem, or offline, and speaks natively to your CLI, shell, and security tools. No data sharing, no external inference calls. Deep Hat keeps full autonomy in your hands, with no censorship and zero reliance on third-party AI labs. It’s built for the messy, sensitive, unsolved problems most models can’t touch.
About Deep Hat
WhiteRabbitNeo is now Deep Hat, an open source security LLM built to solve real-world challenges. While most models are trained on generic code or synthetic benchmarks, we focus on rich, real-world security data to power autonomous offensive and defensive workflows. Updated July 2025, it’s trained for technical operations and integrates with Kindo to give you secure access to the latest security data, autonomous agents, your enterprise systems.
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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 is Kindo’s proprietary security LLM. It’s open source, but fully optimized inside Kindo’s agentic platform. It powers precise, autonomous workflows across SecOps, DevOps, and ITOps. Prefer another model? Kindo supports 20+ LLMs including OpenAI, Anthropic, and BYOM options. Whether you use Deep Hat or your own, Kindo gives you safe, real-time execution, on your infrastructure, on your terms.
See Hugging Face project ->Capabilities
Built for control and trust. Our LLM 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.
Taxonomy-Informed Fine-Tuning
While other LLMs memorize stacks of random text, Deep Hat tackles progressively complex security and operations tasks, moving from rote memorization to conceptual application of knowledge. By escalating difficulty and depth — from “greenfield code writing” to “pinpoint the root cause buried in a misconfigured Terraform file” — the model gains a kind of practical intuition. This approach involved training on over a million supervised Q&A pairs, each sourced from real incidents and usage patterns in web security, malware, infra-as-code, vulnerability databases, and more.
Uncensored AI Models
Censored models miss critical signals. In security, that’s not just a limitation, it’s a liability. Deep Hat is purpose-built to operate without guardrails that block offensive or sensitive content, enabling realistic threat modeling and autonomous response. It understands adversaries because it’s trained like one. That’s what gives Kindo the precision and depth generic models can’t match.
Deep Hat is a project from Kindo AI
Deep Hat is an open source project sponsored by Kindo AI, an enterprise security and infrastructure company focused on Gen AI for DevSecOps.
Kindo helps secure and automate enterprise infrastructure with Gen AI. Through their platform, enterprises can construct Agents that automate tedious Runbook tasks within existing systems of record such as IT ticketing systems, CI/CD systems, and modern infrastructure (on-prem, cloud, and hybrid environments).
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