MODEL PROFILE · 模型档案

vulnerability-severity-classification-roberta-base

CIRCL · vulnerability-severity-classification-roberta-base

The model was presented in the paper VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification [arXiv]. **Abstract:** VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service. > **Note (October 2026):** since VulnTrain 3.4.0, > the dataset is split so that records of the same vulnerability (for example a CVE and its > GitHub advisory) never appear in both train and test. Accuracy measured this way (**74%**) > reflects performance on vulnerabilities the model has never seen. Earlier figures, including > the one in the abstract above, were measured on a split where many test descriptions also > appeared in training. Details in the technical report > Improving vulnerability-scores: Leakage-Free Splits and Consistent CVSS 4.0 Scoring.

译文排队中,当前展示来源原文。

文本分析license:cc-by-4.0
上下文窗口来源未披露
输入 → 输出文本 → 文本
模型权重待核验
档案更新时间2026/10/31 00:25:47
OVERVIEW

模型介绍

The model was presented in the paper VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification [arXiv]. **Abstract:** VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service. > **Note (October 2026):** since VulnTrain 3.4.0, > the dataset is split so that records of the same vulnerability (for example a CVE and its > GitHub advisory) never appear in both train and test. Accuracy measured this way (**74%**) > reflects performance on vulnerabilities the model has never seen. Earlier figures, including > the one in the abstract above, were measured on a split where many test descriptions also > appeared in training. Details in the technical report > Improving vulnerability-scores: Leakage-Free Splits and Consistent CVSS 4.0 Scoring.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:cc-by-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。