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Mining for Lags in Updating Critical Security Threats: A Case Study of Log4j Library

Hidetake Tanaka, Kazuma Yamasaki, Momoka Hirose, Takashi Nakano, Youmei Fan, Kazumasa Shimari, Raula Gaikovina Kula, Kenichi Matsumoto

TL;DR

The paper investigates update latency of Maven dependencies after the Log4j-Core Log4Shell CVE. Using the 2025 mining challenge dataset from Goblin Miner, it traces downstream adoption of patched Log4j-Core (version 2.17.0) and quantifies Lag as the time between the patch release and downstream updates. Key findings include that about 72.7% of packages update within 3 months and 95.1% within a year, while 4.9% experience substantial delays; there is a moderate positive correlation (r ≈ 0.43) between release frequency and responsiveness, with patch updates lagging about 10 days on median and major updates about 109 days. These results highlight the importance of frequent releases for timely vulnerability remediation in software ecosystems and offer actionable insights for improving dependency management and security practices.

Abstract

The Log4j-Core vulnerability, known as Log4Shell, exposed significant challenges to dependency management in software ecosystems. When a critical vulnerability is disclosed, it is imperative that dependent packages quickly adopt patched versions to mitigate risks. However, delays in applying these updates can leave client systems exposed to exploitation. Previous research has primarily focused on NPM, but there is a need for similar analysis in other ecosystems, such as Maven. Leveraging the 2025 mining challenge dataset of Java dependencies, we identify factors influencing update lags and categorize them based on version classification (major, minor, patch release cycles). Results show that lags exist, but projects with higher release cycle rates tend to address severe security issues more swiftly. In addition, over half of vulnerability fixes are implemented through patch updates, highlighting the critical role of incremental changes in maintaining software security. Our findings confirm that these lags also appear in the Maven ecosystem, even when migrating away from severe threats.

Mining for Lags in Updating Critical Security Threats: A Case Study of Log4j Library

TL;DR

The paper investigates update latency of Maven dependencies after the Log4j-Core Log4Shell CVE. Using the 2025 mining challenge dataset from Goblin Miner, it traces downstream adoption of patched Log4j-Core (version 2.17.0) and quantifies Lag as the time between the patch release and downstream updates. Key findings include that about 72.7% of packages update within 3 months and 95.1% within a year, while 4.9% experience substantial delays; there is a moderate positive correlation (r ≈ 0.43) between release frequency and responsiveness, with patch updates lagging about 10 days on median and major updates about 109 days. These results highlight the importance of frequent releases for timely vulnerability remediation in software ecosystems and offer actionable insights for improving dependency management and security practices.

Abstract

The Log4j-Core vulnerability, known as Log4Shell, exposed significant challenges to dependency management in software ecosystems. When a critical vulnerability is disclosed, it is imperative that dependent packages quickly adopt patched versions to mitigate risks. However, delays in applying these updates can leave client systems exposed to exploitation. Previous research has primarily focused on NPM, but there is a need for similar analysis in other ecosystems, such as Maven. Leveraging the 2025 mining challenge dataset of Java dependencies, we identify factors influencing update lags and categorize them based on version classification (major, minor, patch release cycles). Results show that lags exist, but projects with higher release cycle rates tend to address severe security issues more swiftly. In addition, over half of vulnerability fixes are implemented through patch updates, highlighting the critical role of incremental changes in maintaining software security. Our findings confirm that these lags also appear in the Maven ecosystem, even when migrating away from severe threats.

Paper Structure

This paper contains 8 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Study Design
  3. Data Preparation and Extraction
  4. Empirical Study Design
  5. Results
  6. RQ1 -- How promptly do packages integrating Log4j-Core address CVEs?
  7. RQ2 -- What factors influence the response time to critical CVEs in packages using Log4j-Core?
  8. Discussion and Future Work

Figures (5)

  • Figure 1: The structure of the data extracted from Neo4j.
  • Figure 2: Overview of the Lag between the fixing of the Log4j-Core vulnerability and the adoption of the fix to the package.
  • Figure 3: Relationship between the number of days to respond to the CVE and the number of packages.
  • Figure 4: Relationship between the number of days to respond to the CVE and the frequency of releases.
  • Figure 5: Relationship between the semantic versioning part and the number of days to respond to the CVE.