一句话：提出将 SBOM 建模为异构图，用 HGAT 与 MLP 学习并预测多漏洞级联攻击链。
软件供应链安全事件常源于多个漏洞组件之间的级联交互，但现有基于软件物料清单（SBOM）的分析流水线通常把扫描器结果视为彼此独立的逐 CVE 记录，忽略了组件与漏洞之间的依赖结构。本文提出一个新的研究方向：以 SBOM 驱动的图学习方法学习多漏洞攻击链。作者将漏洞增强的 CycloneDX SBOM 表示为异构图，节点表示软件组件与已知漏洞（CVE），边表示依赖、漏洞关联等带类型关系，从而把 SBOM 结构与扫描输出视为受依赖约束的证据图。方法上，先训练异构图注意力网络（HGAT）预测某组件是否关联至少一个已知漏洞，作为该结构上可学习性的可行性检验；再把级联漏洞发现形式化为 CVE 对链接预测问题，用轻量多层感知机（MLP）在已记录的多漏洞链上训练。评估使用 Wild SBOMs 公开数据集中的 200 份真实 SBOM：HGAT 组件分类器达到 91.03% 准确率与 74.02% F1，级联预测模型在 35 条已记录攻击链的种子集上取得 0.93 的 ROC-AUC。
Software supply chain security compromises often stem from cascaded interactions of vulnerabilities, for example, between multiple vulnerable components. Yet, Software Bill of Materials (SBOM)-based pipelines for security analysis typically treat scanner findings as independent per-CVE (Common Vulnerabilities and Exposures) records. We propose a new research direction based on learning multi-vulnerability attack chains through a novel SBOM-driven graph-learning approach. This treats SBOM structure and scanner outputs as a dependency-constrained evidence graph rather than a flat list of vulnerabilities. We represent vulnerability-enriched CycloneDX SBOMs as heterogeneous graphs whose nodes capture software components and known vulnerabilities (i.e, CVEs), connected by typed relations, such as dependency and vulnerability links. We train a Heterogeneous Graph Attention Network (HGAT) to predict whether a component is associated with at least one known vulnerability as a feasibility check for learning over this structure. Additionally, we frame the discovery of cascading vulnerabilities as CVE-pair link prediction using a lightweight MultiLayer Perceptron (MLP) neural network trained on documented multi-vulnerability chains. Validated on 200 real-world SBOMs from the Wild SBOMs public dataset, the HGAT component classifier achieves 91.03% Accuracy and 74.02% F1-score, while the cascade predictor model (MLP) achieves a Receiver Operating Characteristic - Area Under Curve (ROC-AUC) of 0.93 on a seed set of 35 documented attack chains.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2604-04977,
title = {Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs},
author = {Laura Baird and Armin Moin},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805583}
}