# AgenTag: Attribution of AI Coding Agents from Behavioral Fingerprints

arXiv: https://arxiv.org/abs/2608.00966 · HTML: https://mlsanity.com/paper/2608.00966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 25 of 25)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Is ChatGPT the Ultimate Programming Assistant -- How far is it?](https://mlsanity.com/paper/2304.11938/) (2023)
- [Agentic Much? Adoption of Coding Agents on GitHub](https://mlsanity.com/paper/2601.18341/) (2026)
- [Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub](https://mlsanity.com/paper/2601.15195/) (2026)
- [Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions](https://mlsanity.com/paper/2510.07176/) (2025)
- [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)
- [How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests](https://mlsanity.com/paper/2601.17581/) (2026)
- [Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub](https://mlsanity.com/paper/2601.00477/) (2026)
- [Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests](https://mlsanity.com/paper/2601.04886/) (2026)
- [When AI Agents Touch CI/CD Configurations: Frequency and Success](https://mlsanity.com/paper/2601.17413/) (2026)
- [Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests](https://mlsanity.com/paper/2601.17627/) (2026)
- [Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance](https://mlsanity.com/paper/2602.08915/) (2026)
- [How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses](https://mlsanity.com/paper/2602.17084/) (2026)
- [Reassessing Code Authorship Attribution in the Era of Language Models](https://mlsanity.com/paper/2506.17120/) (2025)
- [Fingerprinting AI Coding Agents on GitHub](https://mlsanity.com/paper/2601.17406/) (2026)
- [Bridging Behavioral Biometrics and Source Code Stylometry: A Survey of Programmer Attribution](https://mlsanity.com/paper/2603.11150/) (2026)

## Abstract (arXiv)

AI coding agents increasingly author pull requests (PRs), often under developers' own accounts, obscuring who actually produced a change. Reliable attribution is important for repository governance, empirical studies of AI-assisted software development, and measuring the impact of AI coding agents. Existing work focuses on closed-set identification of known agents, leaving the practical limits of open-world AI coding agent attribution largely unexplored. In this paper, we present AgenTag, a multimodal framework for open-world AI coding agent attribution, evaluated on AIDev, comprising 33,580 PRs from five AI coding agents and 6,618 human-authored PRs. We represent each PR using textual, behavioral, and code-based modalities, and compare conventional classification with supervised contrastive learning for open-set recognition and few-shot enrollment of previously unseen agents. AgenTag identifies authoring agents with a weighted F1 of 0.96 (macro F1 of 0.84), distinguishes AI- from human-authored PRs with a balanced F1 of 0.89, and detects previously unseen agents with an AUC of 0.84. We further show that PR descriptions and commit messages provide nearly all of the attribution signal, whereas code diffs contribute little across multiple representations, indicating that coding agents are distinguished primarily by how they communicate changes rather than by the code they generate. Moreover, these behavioral fingerprints persist after removing explicit self-disclosed markers, demonstrating that attribution relies largely on latent stylistic characteristics. These findings show that reliable attribution of AI coding agents is feasible and clarify the practical trade-offs between attribution accuracy and the information required to achieve it.