Not In My Git Yard: Catching Backdoors at Commit and Release Time

2026-07-29

AI Summary

This work addresses the vulnerability of open-source software to code-level backdoor attacks during commit and release phases, a threat inadequately mitigated by existing CI pipelines and binary analysis tools. The authors propose Lily, a system that integrates automated backdoor detection into both continuous integration and release review processes. Lily introduces enhanced CI-compatible fuzzing to discover trigger points, combines historical and current execution behaviors to identify anomalies, and incorporates code change analysis to precisely localize malicious regions within large-scale updates. The system systematically defends against five categories of adversarial evasion strategies and demonstrates high detection rates with low false positives across hundreds of real-world benign and backdoored samples. Notably, Lily successfully reproduces and blocks multiple known real-world backdoor incidents.

Abstract

Code-level backdoors-stealthy code changes that grant hidden privileges via secret triggers-pose a persistent threat to opensource software. Known attempts to inject such backdoors into widely used projects through malicious commits, tampered release packages, or compromised third-party dependencies, were stopped only by luck and manual review. Existing Continuous Integration (CI) pipelines cannot detect these attacks, and downstream binary analysis tools require substantial manual effort. In this work, we present Lily, an automated approach that strengthens open-source development and release processes against backdoor injection. Lily integrates a backdoor detection mechanism into (1) CI pipelines to block malicious commits, and (2) release vetting workflows to prevent tampered releases or compromised dependencies from entering large ecosystems, such as Linux distributions. Lily offers two key contributions. First, it enhances CI-compatible fuzzing with the capability to detect triggers of suspicious behavior based on historical and current software executions. This enables fast, precise backdoor detection suitable for both CI and update validation workflows. Second, it combines code change analysis with fuzzing data to precisely point maintainers to backdoor-revealing code regions, even when release updates modify millions of lines of code. We also outline five strategies attackers could use to evade Lily, and evaluate corresponding defenses. Our experiments across hundreds of benign and backdoored commits and releases show that Lily achieves high detection accuracy with low false alarm rates, reliably identifies malicious code, resists adversarial attempts, and would have prevented real-world backdoor incidents.

Problem

• code-level backdoors
• malicious commits
• tampered releases
• open-source software
• backdoor detection

Innovation

• backdoor detection
• continuous integration
• fuzzing
• code change analysis
• release vetting