Process-based Indicators of Vulnerability Re-Introducing Code Changes: An Exploratory Case Study

• URL: http://arxiv.org/abs/2510.26676v1
• Date: Thu, 30 Oct 2025 16:45:36 GMT
• Title: Process-based Indicators of Vulnerability Re-Introducing Code Changes: An Exploratory Case Study
• Authors: Samiha Shimmi, Nicholas M. Synovic, Mona Rahimi, George K. Thiruvathukal,
• Abstract summary: This work highlights the critical role of process metrics along with code changes in understanding and mitigating vulnerability reintroduction.<n>Our approach emphasizes that reintroduction is rarely the result of one isolated action, but emerges from cumulative development activities and socio-technical conditions.
• Score: 2.204918347869259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software vulnerabilities often persist or re-emerge even after being fixed, revealing the complex interplay between code evolution and socio-technical factors. While source code metrics provide useful indicators of vulnerabilities, software engineering process metrics can uncover patterns that lead to their introduction. Yet few studies have explored whether process metrics can reveal risky development activities over time -- insights that are essential for anticipating and mitigating software vulnerabilities. This work highlights the critical role of process metrics along with code changes in understanding and mitigating vulnerability reintroduction. We move beyond file-level prediction and instead analyze security fixes at the commit level, focusing not only on whether a single fix introduces a vulnerability but also on the longer sequences of changes through which vulnerabilities evolve and re-emerge. Our approach emphasizes that reintroduction is rarely the result of one isolated action, but emerges from cumulative development activities and socio-technical conditions. To support this analysis, we conducted a case study on the ImageMagick project by correlating longitudinal process metrics such as bus factor, issue density, and issue spoilage with vulnerability reintroduction activities, encompassing 76 instances of reintroduced vulnerabilities. Our findings show that reintroductions often align with increased issue spoilage and fluctuating issue density, reflecting short-term inefficiencies in issue management and team responsiveness. These observations provide a foundation for broader studies that combine process and code metrics to predict risky fixes and strengthen software security.

Related papers

• Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents [57.49020237126194]
  Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation.<n>We propose Co-RedTeam, a security-aware multi-agent framework designed to mirror real-world red-teaming.<n>Co-RedTeam decomposes vulnerability analysis into coordinated discovery and exploitation stages, enabling agents to plan, execute, validate, and refine actions.
  arXiv  Detail & Related papers  (2026-02-02T14:38:45Z)
• From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
  This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
  arXiv  Detail & Related papers  (2026-01-22T06:21:31Z)
• Evolaris: A Roadmap to Self-Evolving Software Intelligence Management [26.420587026083837]
  Evolaris is a self-evolving software intelligence system built on a multi-agent framework.<n>Agents operate independently but coordinate through shared context to perform tasks such as information discovery, reasoning, gap completion, validation, and risk detection.
  arXiv  Detail & Related papers  (2025-10-06T10:57:38Z)
• Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents [58.69865074060139]
  We study the case where an agent's self-evolution deviates in unintended ways, leading to undesirable or even harmful outcomes.<n>Our empirical findings reveal that misevolution is a widespread risk, affecting agents built even on top-tier LLMs.<n>We discuss potential mitigation strategies to inspire further research on building safer and more trustworthy self-evolving agents.
  arXiv  Detail & Related papers  (2025-09-30T14:55:55Z)
• Weakly Supervised Vulnerability Localization via Multiple Instance Learning [46.980136742826836]
  We propose a novel approach called WAVES for WeAkly supervised Vulnerability localization via multiplE inStance learning.<n>WAVES has the capability to determine whether a function is vulnerable (i.e., vulnerability detection) and pinpoint the vulnerable statements.<n>Our approach achieves comparable performance in vulnerability detection and state-of-the-art performance in statement-level vulnerability localization.
  arXiv  Detail & Related papers  (2025-09-14T15:11:39Z)
• CARE: Decoding Time Safety Alignment via Rollback and Introspection Intervention [68.95008546581339]
  Existing decoding-time interventions, such as Contrastive Decoding, often force a severe trade-off between safety and response quality.<n>We propose CARE, a novel framework for decoding-time safety alignment that integrates three key components.<n>The framework achieves a superior balance of safety, quality, and efficiency, attaining a low harmful response rate and minimal disruption to the user experience.
  arXiv  Detail & Related papers  (2025-09-01T04:50:02Z)
• Information Retrieval Induced Safety Degradation in AI Agents [52.15553901577888]
  This study investigates how expanding retrieval access affects model reliability, bias propagation, and harmful content generation.<n>Retrieval-enabled agents built on aligned LLMs often behave more unsafely than uncensored models without retrieval.<n>These findings underscore the need for robust mitigation strategies to ensure fairness and reliability in retrieval-enabled and increasingly autonomous AI systems.
  arXiv  Detail & Related papers  (2025-05-20T11:21:40Z)
• LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues [62.12404317786005]
  EvoCoder is a continuous learning framework for issue code reproduction. Our results show a 20% improvement in issue reproduction rates over existing SOTA methods.
  arXiv  Detail & Related papers  (2024-11-21T08:49:23Z)
• Trust, but Verify: Evaluating Developer Behavior in Mitigating Security Vulnerabilities in Open-Source Software Projects [0.11999555634662631]
  This study investigates vulnerabilities in dependencies of sampled open-source software (OSS) projects. We have identified common issues in outdated or unmaintained dependencies, that pose significant security risks. Results suggest that reducing the number of direct dependencies and prioritizing well-established libraries with strong security records are effective strategies for enhancing the software security landscape.
  arXiv  Detail & Related papers  (2024-08-26T13:46:48Z)
• Profile of Vulnerability Remediations in Dependencies Using Graph Analysis [40.35284812745255]
  This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model. We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
  arXiv  Detail & Related papers  (2024-03-08T02:01:47Z)
• Do Internal Software Metrics Have Relationship with Fault-proneness and Change-proneness? [1.9526430269580959]
  We identified 25 internal software metrics along with the measures of change-proneness and fault-proneness within the Apache and Eclipse ecosystems. Most of the metrics have little to no correlation with fault-proneness. metrics related to inheritance, coupling, and comments showed a moderate to high correlation with change-proneness.
  arXiv  Detail & Related papers  (2023-09-23T07:19:41Z)
• Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We? [6.438136820117887]
  In a typical maintenance scenario, security-relevant bug reports are prioritised by the development team when preparing corrective patches. Open security-relevant bug reports can become a critical leak of sensitive information that attackers can leverage to perform zero-day attacks. In recent years, approaches for the detection of security-relevant bug reports based on machine learning have been reported with promising performance.
  arXiv  Detail & Related papers  (2021-12-19T11:30:29Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.