Aligning Core Aspects: Improving Vulnerability Proof-of-Concepts via Cross-Source Insights

For vulnerabilities, Proof-of-Concept (PoC) plays an irreplaceable role in demonstrating the exploitability. PoC reports may include critical information such as specific usage, test platforms, and more, providing essential insights for researchers. However, in reality, due to various PoC templates across PoC platforms, PoC reports extensively suffer from information deficiency, leading the suboptimal quality and limited usefulness. Fortunately, we found that information deficiency of PoC reports could be mitigated by the completion from multiple sources given the same referred vulnerability.

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Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1145/3696630.3730572
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
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article

Aligning Core Aspects: Improving Vulnerability Proof-of-Concepts via Cross-Source Insights

L.N. Wang, Sen Chen, Mengyao Zhao, Yue Wang et al.
Adversarial Robustness in Machine Learning
article

Aligning Core Aspects: Improving Vulnerability Proof-of-Concepts via Cross-Source Insights

L.N. Wang, Sen Chen, Mengyao Zhao, Yue Wang, Xingkuan Wu, Wenjing Dang
article en

Abstract

For vulnerabilities, Proof-of-Concept (PoC) plays an irreplaceable role in demonstrating the exploitability. PoC reports may include critical information such as specific usage, test platforms, and more, providing essential insights for researchers. However, in reality, due to various PoC templates across PoC platforms, PoC reports extensively suffer from information deficiency, leading the suboptimal quality and limited usefulness. Fortunately, we found that information deficiency of PoC reports could be mitigated by the completion from multiple sources given the same referred vulnerability.

Tianjin University (CN), Nankai University (CN)
Openalex Percentile: Top 89%
Adversarial Robustness in Machine Learning
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