dorsal/arxiv
View SchemaOperational Runtime Behavior Mining for Open-Source Supply Chain Security
| Authors | Zhuoran Tan, Ke Xiao, Jeremy Singer, Christos Anagnostopoulos |
|---|---|
| Categories | |
| ArXiv ID | 2601.06948vv1 |
| URL | https://arxiv.org/abs/2601.06948 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Open-source software (OSS) is a critical component of modern software systems, yet supply chain security remains challenging in practice due to unavailable or obfuscated source code. Consequently, security teams often rely on runtime observations collected from sandboxed executions to investigate suspicious third-party components. We present HeteroGAT-Rank, an industry-oriented runtime behavior mining system that supports analyst-in-the-loop supply chain threat investigation. The system models execution-time behaviors of OSS packages as lightweight heterogeneous graphs and applies attention-based graph learning to rank behavioral patterns that are most relevant for security analysis. Rather than aiming for fully automated detection, HeteroGAT-Rank surfaces actionable runtime signals - such as file, network, and command activities - to guide manual investigation and threat hunting. To operate at ecosystem scale, the system decouples offline behavior mining from online analysis and integrates parallel graph construction for efficient processing across multiple ecosystems. An evaluation on a large-scale OSS execution dataset shows that HeteroGAT-Rank effectively highlights meaningful and interpretable behavioral indicators aligned with real-world vulnerability and attack trends, supporting practical security workflows under realistic operational constraints.
{
"annotation_id": "3959358f-0f36-48e3-ae96-65b0e5d35fa5",
"date_created": "2026-02-17T05:53:08.171000Z",
"date_modified": "2026-02-17T05:53:08.171000Z",
"file_hash": "79050098d9b884c9721d5d7d465e8b54881acd9c5a2b4e8c3b1601b37dc5259d",
"private": false,
"record": {
"abstract": "Open-source software (OSS) is a critical component of modern software systems, yet supply chain security remains challenging in practice due to unavailable or obfuscated source code. Consequently, security teams often rely on runtime observations collected from sandboxed executions to investigate suspicious third-party components. We present HeteroGAT-Rank, an industry-oriented runtime behavior mining system that supports analyst-in-the-loop supply chain threat investigation. The system models execution-time behaviors of OSS packages as lightweight heterogeneous graphs and applies attention-based graph learning to rank behavioral patterns that are most relevant for security analysis. Rather than aiming for fully automated detection, HeteroGAT-Rank surfaces actionable runtime signals - such as file, network, and command activities - to guide manual investigation and threat hunting. To operate at ecosystem scale, the system decouples offline behavior mining from online analysis and integrates parallel graph construction for efficient processing across multiple ecosystems. An evaluation on a large-scale OSS execution dataset shows that HeteroGAT-Rank effectively highlights meaningful and interpretable behavioral indicators aligned with real-world vulnerability and attack trends, supporting practical security workflows under realistic operational constraints.",
"arxiv_id": "2601.06948",
"authors": [
"Zhuoran Tan",
"Ke Xiao",
"Jeremy Singer",
"Christos Anagnostopoulos"
],
"categories": [
"cs.CR"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Operational Runtime Behavior Mining for Open-Source Supply Chain Security",
"url": "https://arxiv.org/abs/2601.06948",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "4618b18d-1393-4eaf-9866-862072c7cd1f",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}