# A Survey on Poisoning Attacks Against Supervised Machine Learning

arXiv: https://arxiv.org/abs/2202.02510 · HTML: https://mlsanity.com/paper/2202.02510/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [A backdoor attack against LSTM-based text classification systems](https://mlsanity.com/paper/1905.12457/) (2019)
- [Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning](https://mlsanity.com/paper/1804.00308/) (2018)
- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [Sponge Examples: Energy-Latency Attacks on Neural Networks](https://mlsanity.com/paper/2006.03463/) (2020)
- [Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks](https://mlsanity.com/paper/1803.06975/) (2018)
- [Manipulating SGD with Data Ordering Attacks](https://mlsanity.com/paper/2104.09667/) (2021)
- [Label Sanitization against Label Flipping Poisoning Attacks](https://mlsanity.com/paper/1803.00992/) (2018)
- [Generative Poisoning Attack Method Against Neural Networks](https://mlsanity.com/paper/1703.01340/) (2017)
- [Adversary Resistant Deep Neural Networks with an Application to Malware Detection](https://mlsanity.com/paper/1610.01239/) (2016)
- [A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters](https://mlsanity.com/paper/1811.00121/) (2018)

## Built on by (most cited first, 5 of 5)

- [Deep Learning Model Security: Threats and Defenses](https://mlsanity.com/paper/2412.08969/) (2024)
- [The last Dance : Robust backdoor attack via diffusion models and bayesian approach](https://mlsanity.com/paper/2402.05967/) (2024)
- [Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach](https://mlsanity.com/paper/2406.10719/) (2024)
- [Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras](https://mlsanity.com/paper/2411.03022/) (2024)
- [Let the Neurons Die: Exploiting ReLU-Induced Model Degradation](https://mlsanity.com/paper/2609.35528/) (2026)

## Abstract (arXiv)

With the rise of artificial intelligence and machine learning in modern computing, one of the major concerns regarding such techniques is to provide privacy and security against adversaries. We present this survey paper to cover the most representative papers in poisoning attacks against supervised machine learning models. We first provide a taxonomy to categorize existing studies and then present detailed summaries for selected papers. We summarize and compare the methodology and limitations of existing literature. We conclude this paper with potential improvements and future directions to further exploit and prevent poisoning attacks on supervised models. We propose several unanswered research questions to encourage and inspire researchers for future work.