mlsanityData to 7 Oct 2026

Stylometric Defenses Against Author Impersonation in Software Repositories

arXiv 2608.02695 ↗First posted 3 Aug 2026cs.CR cs.LG cs.SEcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 72 references that are in the map. First posted on arXiv 3 Aug 2026.

Builds on

24 of 72 references.

References · 24

  1. 2020Supervised Contrastive Learningcited by 2,803
  2. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  3. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  4. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 890
  5. 2020GraphCodeBERT: Pre-training Code Representations with Data Flowcited by 600
  6. 2022UniXcoder: Unified Cross-Modal Pre-training for Code Representationcited by 355
  7. 2022Taxonomy of Attacks on Open-Source Software Supply Chainscited by 57
  8. 2020CC2Vec: Distributed Representations of Code Changescited by 38
  9. 2019Misleading Authorship Attribution of Source Code using Adversarial Learningcited by 33
  10. 2024Code Representation Learning At Scalecited by 26
  11. 2015A Meta-Analysis of the Anomaly Detection Problemcited by 23
  12. 2021The Topic Confusion Task: A Novel Scenario for Authorship Attributioncited by 17
12 more
  1. 2021CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Modelcited by 16
  2. 2022RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformationcited by 13
  3. 2020Authorship Attribution of Source Code: A Language-Agnostic Approach and Applicability in Software Engineeringcited by 9
  4. 2023Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequencecited by 9
  5. 2017Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragmentscited by 7
  6. 2022CommitBART: A Large Pre-trained Model for GitHub Commitscited by 6
  7. 2019PatchNet: A Tool for Deep Patch Classificationcited by 5
  8. 2024DevPhish: Exploring Social Engineering in Software Supply Chain Attacks on Developerscited by 3
  9. 2019Scalable Source Code Similarity Detection in Large Code Repositoriescited by 2
  10. 2023SHIELD: Thwarting Code Authorship Attributioncited by 2
  11. 2024SoK: A Defense-Oriented Evaluation of Software Supply Chain Securitycited by 2
  12. 2020Robust and Accurate Authorship Attribution via Program Normalizationcited by 1

Built on by

Not cited yet.