Uncensored Model Now Supports Expanded Parameter Size and Training Data Sources, Including the National Vulnerability Database (NVD) and Open Source Threat Intelligence
WhiteRabbitNeo today released a major new version trained on new cybersecurity and threat intelligence data on the Qwen 2.5 family of models, a top performing software engineering model on HumEval. The new release integrates critical real-world data sources for cybersecurity and infrastructure. These new sources, when deployed on Qwen, make WhiteRabbitNeo even more accurate when resolving prompts related to offensive cybersecurity, creating remediations for potential threats and integrating future threat intelligence and vulnerability data via techniques such as RAG.
“This new V2.5 series of WhiteRabbitNeo AI models represents a significant improvement over previous ones. The early models were fine-tuned using 100,000 samples of offensive and defensive cybersecurity data. The new models used an expanded dataset of 1.7 million samples. This improved WhiteRabbitNeo’s HumanEval score to 85.36, from 75 in our previous generation. And of course, they are still uncensored, so it’s a perfect fit for all cybersecurity use cases,” said Migel Tissera, creator of WhiteRabbitNeo.
“The new release of WhiteRabbitNeo is trained to act like the experienced, professional adversaries modern enterprise security teams face every day. By utilizing Qwen and training on new cybersecurity and DevOps infrastructure as code data sources, WhiteRabbitNeo helps DevSecOps teams expand their capability to combat modern threats by discovering, exploiting and remediating vulnerabilities and security issues in their infrastructure before their adversaries do,” said Andy Manoske, VP of Product at Kindo, the primary sponsor of open source project, WhiteRabbitNeo.
Enterprise security teams are overworked and understaffed and are often targeted by threat actors, such as state-sponsored adversaries and organized crime rings who utilize modern offensive security technology to exploit vulnerabilities within enterprises. Even when these teams are able to successfully defend themselves, rapid changes to identity or security infrastructure or new discoveries of zero day vulnerabilities may mean that defenders of enterprise infrastructure are outmatched by their modern attackers.
GenAI provides new opportunities for defenders to respond to modern adversaries who use attack automation suites. By integrating open threat intelligence, technical data from real world attacks and security research and contextual infrastructure data, LLMs trained in offensive security and DevSecOps provide practitioners in that field with a force multiplier to better respond to modern threats despite significant shortages in the enterprise security workforce.
WhiteRabbitNeo is an open source GenAI model for offensive and defensive cybersecurity. While popular foundation models censor many security use cases, WhiteRabbitNeo is uncensored and trained to act like a modern adversary. It knows threat intelligence, understands software engineering and infrastructure as code, and can utilize its deep corpus of knowledge to craft novel attacks in more than 180 programming and scripting languages and provide immediate remediation for the threats it detects and compromises.
Popular use cases include:
- DevOps professionals are able to write and instrument secure and reliable infrastructure as code.
- Security red teams, known as offensive teams, are able to rapidly improve their efficiency in how they construct code proofs of concept, create sample attacks, remediate vulnerabilities and more
- Security blue teams, known as defensive teams, are able to automate previously manual aspects of runbooks for intrusion detection and response, remediate security events and more
WhiteRabbitNeo’s new release integrates critical real-world data sources from Indicator of Compromise (IoC) and threat actor data from open source threat intelligence networks, CVEs and technical vulnerability data from NVD and common enterprise infrastructure and security tool suite documentation.
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