Recurring Vulnerability Detection: How Far Are We? Article Swipe
Yuhua Cao
,
Susheng Wu
,
Ruisi Wang
,
Bihuan Chen
,
Yiheng Huang
,
Chenhao Lu
,
Zhuotong Zhou
,
Xin Peng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3728901
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3728901
With the rapid development of open-source software, code reuse has become a common practice to accelerate development. However, it leads to inheritance from the original vulnerability, which recurs at the reusing projects, known as recurring vulnerabilities (RVs). Traditional general-purpose vulnerability detection approaches struggle with scalability and adaptability, while learning-based approaches are often constrained by limited training datasets and are less effective against unseen vulnerabilities. Though specific recurring vulnerability detection (RVD) approaches have been proposed, their effectiveness across various RV characteristics remains unclear. In this paper, we conduct a large-scale empirical study using a newly constructed RV dataset containing 4,569 RVs, achieving a 953% expansion over prior RV datasets. Our study analyzes the characteristics of RVs, evaluates the effectiveness of the state-of-the-art RVD approaches, and investigates the root causes of false positives and false negatives, yielding key insights. Inspired by these insights, we design AntMan, a novel RVD approach that identifies both explicit and implicit call relations with modified functions, then employs inter-procedural taint analysis and intra-procedural dependency slicing within those functions to generate comprehensive signatures, and finally incorporates a flexible matching to detect RVs. Our evaluation has shown the effectiveness, generality and practical usefulness in RVD. AntManhas detected 4,593 RVs, with 307 confirmed by developers, and identified 73 new 0-day vulnerabilities across 15 projects, receiving 5 CVE identifiers.
Related Topics
Concepts
Computer science
False positive paradox
Program slicing
Vulnerability (computing)
False positives and false negatives
Dependency (UML)
Reuse
Scalability
Key (lock)
Generality
Adaptability
Comparability
Machine learning
Matching (statistics)
Data mining
Artificial intelligence
Software
Computer security
Engineering
Statistics
Combinatorics
Psychology
Programming language
Database
Ecology
Psychotherapist
Waste management
Biology
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3728901
- OA Status
- hybrid
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411522892
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411522892Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3728901Digital Object Identifier
- Title
-
Recurring Vulnerability Detection: How Far Are We?Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-22Full publication date if available
- Authors
-
Yuhua Cao, Susheng Wu, Ruisi Wang, Bihuan Chen, Yiheng Huang, Chenhao Lu, Zhuotong Zhou, Xin PengList of authors in order
- Landing page
-
https://doi.org/10.1145/3728901Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1145/3728901Direct OA link when available
- Concepts
-
Computer science, False positive paradox, Program slicing, Vulnerability (computing), False positives and false negatives, Dependency (UML), Reuse, Scalability, Key (lock), Generality, Adaptability, Comparability, Machine learning, Matching (statistics), Data mining, Artificial intelligence, Software, Computer security, Engineering, Statistics, Combinatorics, Psychology, Programming language, Database, Ecology, Psychotherapist, Waste management, Biology, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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