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Context: Supervised learning-based projects (SLPs), i.e., software projects that use supervised learning algorithms, such as decision trees are useful for performing classification-related tasks. Yet, security weaknesses, such as the use of hard-coded passwords in SLPs, can make SLPs susceptible to security attacks. A characterization of security weaknesses in SLPs can help practitioners understand the security weaknesses that are frequent in SLPs and adopt adequate mitigation strategies. Objective: The goal of this paper is to help practitioners securely develop supervised learning-based projects by conducting an empirical study of security weaknesses in supervised learning-based projects. Methodology: We conduct an empirical study by quantifying the frequency of security weaknesses in 278 open source SLPs. Results: We identify 22 types of security weaknesses that occur in SLPs. We observe ‘use of potentially dangerous function’ to be the most frequently occurring security weakness in SLPs. Of the identified 3,964 security weaknesses, 23.79% and 40.49% respectively, appear for source code files used to train and test models. We also observe evidence of co-location, e.g., instances of command injection co-locates with instances of potentially dangerous function. Conclusion: Based on our findings, we advocate for a shift left approach for SLP development with security-focused code reviews, and application of security static analysis.
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This content will become publicly available on July 6, 2027
Splits! Flexible Sociocultural Linguistic Investigation at Scale
Variation in language use, shaped by speakers' sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions. For example, Chinese students discuss healthy eating with words like timing, regularity, and digestion, whereas Americans use vocabulary like balancing food groups and avoiding fat and sugar, reflecting distinct cultural models of nutrition (Banna et al., 2016). The computational study of these Sociocultural Linguistic Phenomena (SLP) has traditionally been done in NLP via tailored analyses of specific groups or topics, requiring specialized data collection and experimental operationalization—a process not well-suited to quick hypothesis exploration and prototyping. To address this, we propose constructing a "sandbox" designed for systematic and flexible sociolinguistic research. Using our method, we construct a demographically/topically split Reddit dataset, Splits!, validated by self-identification and by replicating several known SLPs from existing literature. We showcase the sandbox's utility with a scalable, two-stage process that filters large collections of potential SLPs (PSLPs) to surface the most promising candidates for deeper, qualitative investigation.
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- Award ID(s):
- 2048001
- PAR ID:
- 10688183
- Publisher / Repository:
- Association for Computational Linguistics (ACL 2026)
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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