Lingyan Ding , Xingya Wang , Zhenyu Chen , Song Huang
{"title":"POSVIA:用于开源概念验证报告的不一致性分析器","authors":"Lingyan Ding , Xingya Wang , Zhenyu Chen , Song Huang","doi":"10.1016/j.infsof.2025.107868","DOIUrl":null,"url":null,"abstract":"<div><h3>Context:</h3><div>Proof-of-Concept (PoC) reports are indispensable for evaluating the exploitability of vulnerabilities. Various PoC data sources are responsible for collecting and sharing these reports. We have identified inconsistencies in the information pertaining to affected software versions across these data sources. These inconsistencies serve as red flags, alerting security experts to exercise caution during exploitability assessments and ensuring the effective allocation of resources.</div></div><div><h3>Objective:</h3><div>This paper analyzes software version inconsistencies in PoC reports and proposes “POSVIA” (<strong><u>P</u></strong>oC <strong><u>O</u></strong>riented <strong><u>S</u></strong>oftware <strong><u>V</u></strong>ersion <strong><u>I</u></strong>nconsistency <strong><u>A</u></strong>nalyzer), a deep learning tool designed to automatically detect and evaluate these inconsistencies across multiple PoC data sources, overcoming the impracticality of manual detection.</div></div><div><h3>Methods:</h3><div>A Named Entity Recognition (NER) model was developed with high performance: precision (93.76%) and recall (93.48%) for extracting CVE IDs, affected software names, and version data from PoC reports. Additionally, a Relation Extraction (RE) model was designed with metrics of 95.04% precision and 96.40% recall, to identify relationships between software and versions. These models analyzed 173,239 PoC reports from four data sources and assessed version inconsistencies using “POSVIA”.</div></div><div><h3>Results:</h3><div>Analysis revealed that Openwall had the lowest strict match rate (32.75%) for affected software versions, compared to other sources. The strict match rate for verified software versions ranged from 60.00% to 78.16%, indicating substantial inconsistencies. Over time, the match rate fluctuated, improving when using ExploitDB, Packet Storm Security, and CXSecurity as benchmarks. Openwall’s rate remained low, suggesting it should be considered alongside other sources for vulnerability exploitability assessments.</div></div><div><h3>Conclusion:</h3><div>This study introduces an automated tool named “POSVIA”, which is designed to address the challenge of detecting inconsistencies in software versions within PoC reports. By automating inconsistency detection across multiple data sources, POSVIA overcomes the limitations of manual methods and enhances the accuracy of exploitability assessments. This approach provides critical support for improving software security and resource allocation.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107868"},"PeriodicalIF":4.3000,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"POSVIA: Inconsistency analyzer for open-source Proof-of-Concept reports\",\"authors\":\"Lingyan Ding , Xingya Wang , Zhenyu Chen , Song Huang\",\"doi\":\"10.1016/j.infsof.2025.107868\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Context:</h3><div>Proof-of-Concept (PoC) reports are indispensable for evaluating the exploitability of vulnerabilities. Various PoC data sources are responsible for collecting and sharing these reports. We have identified inconsistencies in the information pertaining to affected software versions across these data sources. These inconsistencies serve as red flags, alerting security experts to exercise caution during exploitability assessments and ensuring the effective allocation of resources.</div></div><div><h3>Objective:</h3><div>This paper analyzes software version inconsistencies in PoC reports and proposes “POSVIA” (<strong><u>P</u></strong>oC <strong><u>O</u></strong>riented <strong><u>S</u></strong>oftware <strong><u>V</u></strong>ersion <strong><u>I</u></strong>nconsistency <strong><u>A</u></strong>nalyzer), a deep learning tool designed to automatically detect and evaluate these inconsistencies across multiple PoC data sources, overcoming the impracticality of manual detection.</div></div><div><h3>Methods:</h3><div>A Named Entity Recognition (NER) model was developed with high performance: precision (93.76%) and recall (93.48%) for extracting CVE IDs, affected software names, and version data from PoC reports. Additionally, a Relation Extraction (RE) model was designed with metrics of 95.04% precision and 96.40% recall, to identify relationships between software and versions. These models analyzed 173,239 PoC reports from four data sources and assessed version inconsistencies using “POSVIA”.</div></div><div><h3>Results:</h3><div>Analysis revealed that Openwall had the lowest strict match rate (32.75%) for affected software versions, compared to other sources. The strict match rate for verified software versions ranged from 60.00% to 78.16%, indicating substantial inconsistencies. Over time, the match rate fluctuated, improving when using ExploitDB, Packet Storm Security, and CXSecurity as benchmarks. Openwall’s rate remained low, suggesting it should be considered alongside other sources for vulnerability exploitability assessments.</div></div><div><h3>Conclusion:</h3><div>This study introduces an automated tool named “POSVIA”, which is designed to address the challenge of detecting inconsistencies in software versions within PoC reports. By automating inconsistency detection across multiple data sources, POSVIA overcomes the limitations of manual methods and enhances the accuracy of exploitability assessments. This approach provides critical support for improving software security and resource allocation.</div></div>\",\"PeriodicalId\":54983,\"journal\":{\"name\":\"Information and Software Technology\",\"volume\":\"188 \",\"pages\":\"Article 107868\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information and Software Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950584925002071\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Software Technology","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950584925002071","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
POSVIA: Inconsistency analyzer for open-source Proof-of-Concept reports
Context:
Proof-of-Concept (PoC) reports are indispensable for evaluating the exploitability of vulnerabilities. Various PoC data sources are responsible for collecting and sharing these reports. We have identified inconsistencies in the information pertaining to affected software versions across these data sources. These inconsistencies serve as red flags, alerting security experts to exercise caution during exploitability assessments and ensuring the effective allocation of resources.
Objective:
This paper analyzes software version inconsistencies in PoC reports and proposes “POSVIA” (PoC Oriented Software Version Inconsistency Analyzer), a deep learning tool designed to automatically detect and evaluate these inconsistencies across multiple PoC data sources, overcoming the impracticality of manual detection.
Methods:
A Named Entity Recognition (NER) model was developed with high performance: precision (93.76%) and recall (93.48%) for extracting CVE IDs, affected software names, and version data from PoC reports. Additionally, a Relation Extraction (RE) model was designed with metrics of 95.04% precision and 96.40% recall, to identify relationships between software and versions. These models analyzed 173,239 PoC reports from four data sources and assessed version inconsistencies using “POSVIA”.
Results:
Analysis revealed that Openwall had the lowest strict match rate (32.75%) for affected software versions, compared to other sources. The strict match rate for verified software versions ranged from 60.00% to 78.16%, indicating substantial inconsistencies. Over time, the match rate fluctuated, improving when using ExploitDB, Packet Storm Security, and CXSecurity as benchmarks. Openwall’s rate remained low, suggesting it should be considered alongside other sources for vulnerability exploitability assessments.
Conclusion:
This study introduces an automated tool named “POSVIA”, which is designed to address the challenge of detecting inconsistencies in software versions within PoC reports. By automating inconsistency detection across multiple data sources, POSVIA overcomes the limitations of manual methods and enhances the accuracy of exploitability assessments. This approach provides critical support for improving software security and resource allocation.
期刊介绍:
Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal''s scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include:
• Software management, quality and metrics,
• Software processes,
• Software architecture, modelling, specification, design and programming
• Functional and non-functional software requirements
• Software testing and verification & validation
• Empirical studies of all aspects of engineering and managing software development
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The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.