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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 373 |
| 2026-08-04 | 373 |
| 2026-08-06 | 373 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Membership Inference Attacks and Defenses on Machine Learning Models Literature
A curated list of membership inference attacks and defenses papers on machine learning models.
Papers are sorted by their released dates in descending order.
This repository serves as a complement to the survey below.
[**Membership Inference Attacks on Machine Learning: A Survey**](https://arxiv.org/abs/2103.07853) **(More than 100 papers reviewed).**
````bibtex
@article{hu2022membership,
title={Membership inference attacks on machine learning: A survey},
author={Hu, Hongsheng and Salcic, Zoran and Sun, Lichao and Dobbie, Gillian and Yu, Philip S and Zhang, Xuyun},
journal={ACM Computing Surveys (CSUR)},
volume={54},
number={11s},
pages={1--37},
year={2022},
publisher={ACM New York, NY}
}
````
If you feel this repository is helpful, please cite the survey above.
## How to Search?
Search keywords like conference name (e.g., ```CCS```), adversarial knowledge (e.g., ```Black-box```), or target model (e.g., ```Classification Model```) over the webpage to quickly locate related papers. Because we are in the age of generative AI, we highlight the target model of ```Large Language Model (LLM)```.
## Quick Links
**Attack papers sorted by year:** | [2026](#attack-papers-2026) | [2025](#attack-papers-2025) | [2024](#attack-papers-2024) |[2023](#attack-papers-2023) |[2022](#attack-papers-2022) |[2021](#attack-papers-2021) | [2020](#attack-papers-2020-back-to-top) | [2019](#attack-papers-2019-back-to-top) | [2018](#attack-papers-2018-back-to-top) | [2017](#attack-papers-2017-back-to-top) |
**Defense papers sorted by year:** | [2025](#defense-papers-2025-back-to-top) | [2024](#defense-papers-2024-back-to-top) | [2023](#defense-papers-2023-back-to-top) |[2022](#defense-papers-2022-back-to-top) | [2021](#defense-papers-2021-back-to-top) | [2020](#defense-papers-2020-back-to-top) | [2019](#defense-papers-2019-back-to-top) | [2018](#defense-papers-2018-back-to-top) |
## Membership Inference Attack
### Attack Papers 2026
| Year | Title | Adversarial Knowledge | Target Model | Venue | Paper Link | Code Link |
|-------|--------|--------|--------|-----------|------------|---------------|
| 2026 | **Automated Membership Inference Attacks: Discovering MIA Signal Computations using LLM Agents** | Black-/White-box | LLM/VLM | ArXiv | [Link](https://arxiv.org/abs/2603.19375) | [Link](https://github.com/toan-vt/automia) |
### Attack Papers 2025
| Year | Title | Adversarial Knowledge | Target Model | Venue | Paper Link | Code Link |
|-------|--------|--------|--------|-----------|------------|---------------|
| 2025 | **MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector** | Black-box | :sparkles: ```LLM``` :sparkles: | AAAI | [Link](https://ojs.aaai.org/index.php/AAAI/article/view/34939) | [Link](https://github.com/tsinghua-fib-lab/AAAI2025_MIA-Tuner) |
| 2025 | **Min-K%++: Improved Baseline for Pre-Training Data Detection from Large Language Models** | White-box | :sparkles: ```LLM``` :sparkles: | ICLR | [Link](https://openreview.net/forum?id=ZGkfoufDaU) | [Link](https://github.com/zjysteven/mink-plus-plus) |
| 2025 | **RecPS: Privacy Risk Scoring for Recommender Systems** | White-box | Recommender System | RecSys | [Link](https://dl.acm.org/doi/10.1145/3705328.3748052) | [Link](https://github.com/RhincodonE/RsLiRA) |
### Attack Papers 2024
| Year | Title | Adversarial Knowledge | Target Model | Venue | Paper Link | Code Link |
|-------|--------|--------|--------|-----------|------------|---------------|
| 2024 | **Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration** | Black-box | :sparkles: ```LLM``` :sparkles: | NeurIPS | [Link](https://openreview.net/forum?id=PAWQvrForJ) | [Link](https://github.com/tsinghua-fib-lab/NeurIPS2024_SPV-MIA) |
| 2024 | **Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models** | Black-box Excerpt of 59,088 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e57f927bd4ba4f9b, llm:Repository name: 'membership-inference-machine-learning-literature' — indicates collection/list of literature on membership inference attacks in machine learning (privacy/security). No README provided.
matched fp:e57f927bd4ba4f9b, llm:Repository name: 'membership-inference-machine-learning-literature' — indicates collection/list of literature on membership inference attacks in machine learning (privacy/security). No README provided.