arXiv:2510.14113v2 Announce Type: replace Abstract: Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domain-specific models and training datasets. To address this gap, we present CyberPal 2.0, a family of cybersecurity-expert small language models (SLMs) ranging from 4B-20B parameters. To train CyberPal 2.0, we generate an enriched chain-of-thought cybersecurity instruction dataset built with our data enrichment and formatting pipeline, SecKnowledge 2.0, which integrates expert-in-the-loop steering of reaso
The increasing sophistication of large language models is driving a demand for specialized, domain-expert AI, particularly in critical areas like cybersecurity where generic models are insufficient.
This development indicates a maturation of AI, moving beyond general-purpose models to highly targeted applications, which can significantly enhance defensive capabilities against cyber threats.
The focus is shifting from general LLMs to specialized small language models (SLMs) tailored with high-quality, domain-specific data, enabling more effective use in sensitive sectors.
- · Cybersecurity companies
- · Organizations with advanced cyber defense needs
- · AI developers specializing in data curation
- · Generic LLM providers in specialized domains
- · Organizations relying solely on traditional cybersecurity tools
Specialized AI models will improve the accuracy and efficiency of cybersecurity operations, automating threat detection and response.
The proliferation of expert SLMs could lead to an AI arms race in cybersecurity, requiring continuous development to stay ahead of sophisticated AI-powered threats.
Enhanced cybersecurity via expert SLMs might inadvertently increase reliance on AI, potentially creating new vulnerabilities if these models are compromised or biased.
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