# The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution

arXiv: https://arxiv.org/abs/2510.10493 · HTML: https://mlsanity.com/paper/2510.10493/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 12 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [The Stack: 3 TB of permissively licensed source code](https://mlsanity.com/paper/2211.15533/) (2022)
- [Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges](https://mlsanity.com/paper/2408.08946/) (2024)
- [Misleading Authorship Attribution of Source Code using Adversarial Learning](https://mlsanity.com/paper/1905.12386/) (2019)
- [How secure is AI-generated Code: A Large-Scale Comparison of Large Language Models](https://mlsanity.com/paper/2404.18353/) (2024)
- [Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education](https://mlsanity.com/paper/2401.03676/) (2024)
- [Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problems](https://mlsanity.com/paper/2403.04013/) (2024)
- [When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries](https://mlsanity.com/paper/1512.08546/) (2015)
- [Authorship Attribution of Source Code: A Language-Agnostic Approach and Applicability in Software Engineering](https://mlsanity.com/paper/2001.11593/) (2020)
- [I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution](https://mlsanity.com/paper/2506.17323/) (2025)
- [BinMLM: Binary Authorship Verification with Flow-aware Mixture-of-Shared Language Model](https://mlsanity.com/paper/2203.04472/) (2022)
- [I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution](https://mlsanity.com/paper/2501.08165/) (2025)

## Built on by (most cited first, 2 of 2)

- [AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software](https://mlsanity.com/paper/2512.18567/) (2025)
- [Can Multi-Agent LLMs Identify Their Peers? Stylometric Fingerprinting in Role-Constrained Political Analysis](https://mlsanity.com/paper/2606.09854/) (2026)

## Abstract (arXiv)

In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling reliable authorship attribution and model fingerprinting. With the rapid rise of AI-generated code, attribution is playing a critical role in detecting vulnerabilities, flagging malicious content, and ensuring accountability. While AI-vs-human detection usually treats AI as a single category we show that individual LLMs leave unique stylistic signatures, even among models belonging to the same family or parameter size. To this end, we introduce LLM-NodeJS, a dataset of 50,000 Node.js back-end programs from 20 large language models. Each has four transformed variants, yielding 250,000 unique JavaScript samples and two additional representations (JSIR and AST) for diverse research applications. Using this dataset, we benchmark traditional machine learning classifiers against fine-tuned Transformer encoders and introduce CodeT5-JSA, a custom architecture derived from the 770M-parameter CodeT5 model with its decoder removed and a modified classification head. It achieves 95.8% accuracy on five-class attribution, 94.6% on ten-class, and 88.5% on twenty-class tasks, surpassing other tested models such as BERT, CodeBERT, and Longformer. We demonstrate that classifiers capture deeper stylistic regularities in program dataflow and structure, rather than relying on surface-level features. As a result, attribution remains effective even after mangling, comment removal, and heavy code transformations. To support open science and reproducibility, we release the LLM-NodeJS dataset, Google Colab training scripts, and all related materials on GitHub: https://github.com/LLM-NodeJS-dataset.