At this time, we’re excited to share that the SecureBERT 2.0 mannequin is offered on HuggingFace and GitHub with an accompanying analysis paper. This launch marks a major milestone, constructing on the already extensively adopted SecureBERT mannequin to unlock much more superior cybersecurity purposes. Simply see this unparalleled efficiency throughout real-world duties:
In 2022, the primary SecureBERT mannequin was launched by Ehsan and a workforce of researchers from Carnegie Mellon College and UNC Charlotte as a pioneering language mannequin designed particularly for the cybersecurity area. It bridged the hole between general-purpose NLP fashions like BERT and the specialised wants of cybersecurity professionals—enabling AI techniques to perceive the technical language of threats, vulnerabilities, and exploits.
By December 2023, SecureBERT ranked among the many high 100 most downloaded fashions on HuggingFace out of the roughly 500,000 fashions then obtainable on the repository. It gained vital recognition throughout the cybersecurity neighborhood and stays in energetic use by main organizations, together with the MITRE Risk Report ATT&CK Mapper (TRAM) and CyberPeace Institute.
On this weblog, we’ll mirror on the affect of the unique SecureBERT mannequin, element the numerous developments made in SecureBERT 2.0, and discover some real-world purposes of this highly effective new mannequin.
The affect of the unique SecureBERT mannequin
Safety analysts at enterprises and businesses commit an incredible period of time to parsing via numerous safety indicators to determine, analyze, categorize, and report on potential threats. It’s an necessary course of that, when completed totally manually, is time-consuming, costly, and vulnerable to human error.
SecureBERT gave researchers and analysts a instrument that would course of safety reviews, malware analyses, and vulnerability write-ups with contextual accuracy by no means earlier than doable. Even at the moment, it serves as a useful instrument for cybersecurity specialists at among the world’s high businesses, universities, and labs.
Nevertheless, SecureBERT had a number of limitations. It struggled to deal with long-context inputs equivalent to detailed risk intelligence reviews and mixed-format information combining textual content and code. Since SecureBERT was skilled on RoBERTa-base, a basic BERT encoder with a 512-token context restrict and no FlashAttention, it was slower and extra memory-intensive throughout coaching and inference. In distinction, SecureBERT 2.0, constructed on ModernBERT, advantages from an optimized structure with prolonged context, sooner throughput, decrease latency, and lowered reminiscence utilization.
With SecureBERT 2.0, we addressed these gaps in coaching information and superior the structure to ship a mannequin that was much more succesful and contextually conscious than ever. Whereas the unique SecureBERT was a standalone base mannequin, the two.0 model consists of a number of fine-tuned variants specializing in numerous real-world cybersecurity purposes.
Introducing SecureBERT 2.0
SecureBERT 2.0 brings better contextual relevance and area experience for cybersecurity, understanding code sources and programming logic in a method its predecessor merely couldn’t. The important thing here’s a coaching dataset that’s bigger, extra various, and strategically curated to assist the mannequin higher seize refined safety nuances and ship extra correct, dependable, and context-aware risk evaluation.
Whereas massive autoregressive fashions equivalent to GPT-5 excel at producing language, encoder-based fashions like SecureBERT 2.0 are designed to grasp, characterize, and retrieve data with precision—a elementary want in cybersecurity. Generative fashions predict the following token; encoder fashions remodel complete inputs into dense, semantically wealthy embeddings that seize relationships, context, and which means with out fabricating content material.
This distinction makes SecureBERT 2.0 very best for high-precision, security-critical purposes the place factual accuracy, explainability, and velocity are paramount. Constructed on the ModernBERT structure, it makes use of hierarchical long-context encoding and multi-modal text-and-code understanding to investigate complicated risk information and supply code effectively.
Let’s check out how SecureBERT 2.0 helps safety analysts in real-world purposes.
Actual world purposes of SecureBERT 2.0
Think about you’re a SOC analyst tasked with investigating a suspected provide chain compromise. Historically, this is able to contain correlating open-source intelligence, inside alerts, and vulnerability reviews in a course of which may take a number of weeks of handbook information evaluation and cross-referencing.
With SecureBERT 2.0, you possibly can merely embed all related property—reviews, codes, CVE information, and risk intelligence, for instance—within the system. The mannequin instantly surfaces connections between obscure indicators and beforehand unseen infrastructure patterns.
This is only one potential situation of many; SecureBERT 2.0 can assist and streamline a wealth of potential safety purposes:
- Risk Intelligence Correlation: Linking indicators of compromise throughout a number of sources to uncover marketing campaign patterns and adversary techniques
- Incident Triage & Alert Prioritization: Embedding alerts and reviews to detect duplicates, associated incidents, or identified CVEs—decreasing noise and analyst workload
- Safe Code & Vulnerability Detection: Figuring out dangerous patterns, insecure dependencies, and potential zero-day vulnerabilities in supply code
- Semantic Search & RAG for Safety Ops: Offering context-aware retrieval throughout inside data bases, risk feeds, and documentation for sooner analyst response
- Coverage and Compliance Search: Enabling correct semantic lookup throughout massive regulatory and governance corpora
Not like generative LLMs that create textual content, SecureBERT 2.0 interprets and buildings data to ship sooner inference, decrease compute prices, and reduce the chance of hallucination. This makes it a trusted basis mannequin for enterprise, protection, and analysis environments the place precision and information integrity matter most.
Underneath the hood of SecureBERT 2.0
There are three elements to the SecureBERT 2.0 structure that make this mannequin such a major development: its ModernBERT basis, its information growth, and smarter strategy to pretraining.
SecureBERT 2.0 is powered by ModernBERT, a next-generation transformer designed for long-document processing. Prolonged consideration mechanisms and hierarchical encoding permit the mannequin to seize each fine-grained syntax and high-level construction—vital for analyzing lengthy, multi-section safety reviews.
The mannequin is skilled on 13 occasions extra information than the unique SecureBERT with a brand new corpus that features curated safety articles and technical blogs, filtered cybersecurity information, code vulnerability repositories, and incident narratives. In complete, this dataset covers 13 billion textual content tokens and 53 million code tokens.
Lastly, a microannealing pretraining curriculum steadily transitions from curated to real-world information, balancing high quality and variety. Focused masking teaches the mannequin to foretell essential safety actions and entities like “bypass,” “encrypt,” or “CVE,” strengthening area illustration.
The efficiency of SecureBERT 2.0 is a marked enchancment over its predecessor and different evaluated fashions throughout benchmarks; the small print could be present in full analysis paper.
Wanting forward: AI for safety at Cisco
SecureBERT 2.0 demonstrates what’s doable when structure and information are purpose-built for cybersecurity. It joins different fashions, just like the generative Basis-Sec-8B from Cisco’s Basis AI workforce, as a part of Cisco’s continued dedication to making use of AI responsibly inside the area of cybersecurity.
We’re excited to share this mannequin with the world, to see among the progressive methods will probably be embraced by the safety neighborhood, and to proceed exploring potential usages for taxonomy creation, data graph technology, and different cutting-edge purposes.
You will get began with the SecureBERT 2.0 mannequin on HuggingFace and GitHub at the moment, and dig into our analysis paper for extra element and efficiency benchmarking.
The way forward for cybersecurity AI is securely clever.