Heimdallr detects LLM-induced security risks in GitHub CI workflows by normalizing them into an LLM-Workflow Property Graph and combining triggerability analysis with LLM-assisted dataflow summarization, achieving over 0.91 F1 on threat detection in evaluation.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 4roles
background 1polarities
support 1representative citing papers
A catalog of ten cache smells in GitLab CI/CD, an automated detector achieving 0.98 F1, and empirical evidence that the smells appear in 89% of 228 mature open-source projects.
GitHub Actions workflows achieve only 28% overall compliance with best practices, with LLMs enabling an 81% reduction in verification effort via hybrid adjudication but still requiring expert oversight for security judgments.
An AI-enabled framework is proposed to assess CI suitability, recommend services, and guide configurations according to project characteristics.
citing papers explorer
-
Heimdallr: Characterizing and Detecting LLM-Induced Security Risks in GitHub CI Workflows
Heimdallr detects LLM-induced security risks in GitHub CI workflows by normalizing them into an LLM-Workflow Property Graph and combining triggerability analysis with LLM-assisted dataflow summarization, achieving over 0.91 F1 on threat detection in evaluation.
-
Cache-Related Smells in GitLab CI/CD: Comprehensive Catalog, Automated Detection, and Empirical Evidence
A catalog of ten cache smells in GitLab CI/CD, an automated detector achieving 0.98 F1, and empirical evidence that the smells appear in 89% of 228 mature open-source projects.
-
How Compliant Are GitHub Actions Workflows? A Checklist-Based Study with LLM-Assisted Auditing
GitHub Actions workflows achieve only 28% overall compliance with best practices, with LLMs enabling an 81% reduction in verification effort via hybrid adjudication but still requiring expert oversight for security judgments.
-
A Vision for Context-Aware CI Adoption Decisions
An AI-enabled framework is proposed to assess CI suitability, recommend services, and guide configurations according to project characteristics.