As we speak, we’re excited to share that the SecureBERT 2.0 mannequin is out there 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 functions. Simply see this unparalleled efficiency throughout real-world duties:
In 2022, the primary SecureBERT mannequin was launched by Ehsan and a staff 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 programs 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 accessible on the repository. It gained vital recognition throughout the cybersecurity neighborhood and stays in energetic use by main organizations, together with the MITRE Menace Report ATT&CK Mapper (TRAM) and CyberPeace Institute.
On this weblog, we’ll replicate on the influence of the unique SecureBERT mannequin, element the numerous developments made in SecureBERT 2.0, and discover some real-world functions of this highly effective new mannequin.
The influence of the unique SecureBERT mannequin
Safety analysts at enterprises and businesses dedicate an incredible period of time to parsing via varied safety alerts to determine, analyze, categorize, and report on potential threats. It’s an essential course of that, when carried out completely manually, is time-consuming, costly, and vulnerable to human error.
SecureBERT gave researchers and analysts a device that might course of safety experiences, malware analyses, and vulnerability write-ups with contextual accuracy by no means earlier than attainable. Even immediately, it serves as a useful device for cybersecurity specialists at a few of the world’s high businesses, universities, and labs.
Nevertheless, SecureBERT had a number of limitations. It struggled to deal with long-context inputs comparable to detailed menace intelligence experiences and mixed-format knowledge combining textual content and code. Since SecureBERT was educated on RoBERTa-base, a traditional 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 decreased reminiscence utilization.
With SecureBERT 2.0, we addressed these gaps in coaching knowledge 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 varied real-world cybersecurity functions.
Introducing SecureBERT 2.0
SecureBERT 2.0 brings better contextual relevance and area experience for cybersecurity, understanding code sources and programming logic in a means its predecessor merely couldn’t. The important thing here’s a coaching dataset that’s bigger, extra numerous, and strategically curated to assist the mannequin higher seize refined safety nuances and ship extra correct, dependable, and context-aware menace evaluation.
Whereas massive autoregressive fashions comparable to GPT-5 excel at producing language, encoder-based fashions like SecureBERT 2.0 are designed to grasp, characterize, and retrieve info with precision—a basic want in cybersecurity. Generative fashions predict the following token; encoder fashions remodel total inputs into dense, semantically wealthy embeddings that seize relationships, context, and which means with out fabricating content material.
This distinction makes SecureBERT 2.0 perfect for high-precision, security-critical functions the place factual accuracy, explainability, and pace are paramount. Constructed on the ModernBERT structure, it makes use of hierarchical long-context encoding and multi-modal text-and-code understanding to research complicated menace knowledge and supply code effectively.
Let’s check out how SecureBERT 2.0 helps safety analysts in real-world functions.
Actual world functions of SecureBERT 2.0
Think about you’re a SOC analyst tasked with investigating a suspected provide chain compromise. Historically, this could contain correlating open-source intelligence, inner alerts, and vulnerability experiences in a course of which may take a number of weeks of guide knowledge evaluation and cross-referencing.
With SecureBERT 2.0, you possibly can merely embed all related property—experiences, codes, CVE knowledge, and menace 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 help and streamline a wealth of potential safety functions:
- Menace Intelligence Correlation: Linking indicators of compromise throughout a number of sources to uncover marketing campaign patterns and adversary ways
- Incident Triage & Alert Prioritization: Embedding alerts and experiences to detect duplicates, associated incidents, or recognized 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 inner information bases, menace 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 constructions info to ship sooner inference, decrease compute prices, and decrease the chance of hallucination. This makes it a trusted basis mannequin for enterprise, protection, and analysis environments the place precision and knowledge integrity matter most.
Below 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 knowledge enlargement, 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 experiences.
The mannequin is educated on 13 occasions extra knowledge than the unique SecureBERT with a brand new corpus that features curated safety articles and technical blogs, filtered cybersecurity knowledge, code vulnerability repositories, and incident narratives. In whole, 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 knowledge, 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 main points might be present in full analysis paper.
Trying forward: AI for safety at Cisco
SecureBERT 2.0 demonstrates what’s attainable when structure and knowledge are purpose-built for cybersecurity. It joins different fashions, just like the generative Basis-Sec-8B from Cisco’s Basis AI staff, as a part of Cisco’s continued dedication to making use of AI responsibly throughout the area of cybersecurity.
We’re excited to share this mannequin with the world, to see a few of the progressive methods it is going to be embraced by the safety neighborhood, and to proceed exploring potential usages for taxonomy creation, information graph technology, and different cutting-edge functions.
You may get began with the SecureBERT 2.0 mannequin on HuggingFace and GitHub immediately, and dig into our analysis paper for extra element and efficiency benchmarking.
The way forward for cybersecurity AI is securely clever.
