How Well Can LLMs Really Handle Real-World Security Tasks?
On Cybench’s autonomous CTF benchmark, most models struggled. WhiteRabbitNeo V3 matched the performance of models 10x its size, solving real tasks with a fraction of the compute.
Open source, uncensored AI for DevSecOps
Used by more than 1500 enterprise companies, each with $100M+ in annual revenue, WhiteRabbitNeo is Kindo's LLM that drives Gen AI value for (Dev)SecOps teams. Automate your manual SecOps tedium, so DevSecOps professionals can focus on creating innovative solutions to more complex problems.

About WhiteRabbitNeo
WhiteRabbitNeo is 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. Our community contributes and benefits from the largest open security dataset initiative available, with a focus on Red Team, Blue Team, and DevSecOps use cases.
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Our Mission
We strive to be the best uncensored Gen AI cybersecurity 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
WhiteRabbitNeo 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 WhiteRabbitNeo or your own, Kindo gives you safe, real-time execution, on your infrastructure, on your terms.
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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, WhiteRabbitNeo puts enterprises—not vendors—in charge of their automation future.

Taxonomy-Informed Fine-Tuning
While other LLMs memorize stacks of random text, WhiteRabbitNeo V3 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. WhiteRabbitNeo 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.

WhiteRabbitNeo is a project from Kindo AI
WhiteRabbitNeo 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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