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This study investigates the effectiveness of patch-level authorship verification as a defense against identity exploitation in software repositories, utilizing stylometric analysis on both code diffs and commit messages. By fine-tuning a cross-modal transformer on over 20 years of Linux kernel commit history, the authors achieve a remarkable ROC AUC of 0.93 for open-world authorship verification. The proposed streaming anomaly detector successfully identifies forged commits in real-world scenarios, demonstrating its potential to enhance security in continuous integration and deployment environments.
Cross-modal embeddings can detect author impersonation in software repositories with over 90% accuracy, revolutionizing how we secure code contributions.
Software supply-chain attacks increasingly exploit an identity gap where compromised maintainer accounts authorize malicious changes. This work evaluates patch-level authorship verification as a behavioral defense layer, showing that stylometric analysis can operate not only on full source files but also on patch-level commits. We fine-tune a cross-modal transformer on more than 20 years of Linux kernel commit history to embed code diffs and commit messages into a unified stylometric space, achieving ROC AUC of 0.93 for open-world authorship verification. We then use these representations in a streaming anomaly detector suited to continuous integration and deployment (CI/CD) settings. We validate the pipeline on two retrospective supply-chain incidents involving different patch characteristics: the 2021 PHP backdoor and the 2026 ForceMemo/GlassWorm campaign. Without retraining, the proposed detector surfaces both PHP forged commits within approximately 1% of the maintainer audit queue and ranks the 28 scoreable ForceMemo spoofs with a median per-repository review burden of 0.8%. These results indicate that cross-modal patch-level embeddings can support behavioral triage against author impersonation in real-world repositories.
