  With the increase in cybersecurity vulnerabilities of software systems, the
ways to exploit them are also increasing. Besides these, malware threats,
irregular network interactions, and discussions about exploits in public forums
are also on the rise. To identify these threats faster, to detect potentially
relevant entities from any texts, and to be aware of software vulnerabilities,
automated approaches are necessary. Application of natural language processing
(NLP) techniques in the Cybersecurity domain can help in achieving this.
However, there are challenges such as the diverse nature of texts involved in
the cybersecurity domain, the unavailability of large-scale publicly available
datasets, and the significant cost of hiring subject matter experts for
annotations. One of the solutions is building multi-task models that can be
trained jointly with limited data. In this work, we introduce a generative
multi-task model, Unified Text-to-Text Cybersecurity (UTS), trained on malware
reports, phishing site URLs, programming code constructs, social media data,
blogs, news articles, and public forum posts. We show UTS improves the
performance of some cybersecurity datasets. We also show that with a few
examples, UTS can be adapted to novel unseen tasks and the nature of data
