# Application of BadNets in Spam Filters

arXiv: https://arxiv.org/abs/2307.09649 · HTML: https://mlsanity.com/paper/2307.09649/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 5 of 5)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks](https://mlsanity.com/paper/1805.12185/) (2018)
- [Defending Neural Backdoors via Generative Distribution Modeling](https://mlsanity.com/paper/1910.04749/) (2019)
- [NNoculation: Catching BadNets in the Wild](https://mlsanity.com/paper/2002.08313/) (2020)

## Abstract (arXiv)

Spam filters are a crucial component of modern email systems, as they help to protect users from unwanted and potentially harmful emails. However, the effectiveness of these filters is dependent on the quality of the machine learning models that power them. In this paper, we design backdoor attacks in the domain of spam filtering. By demonstrating the potential vulnerabilities in the machine learning model supply chain, we highlight the need for careful consideration and evaluation of the models used in spam filters. Our results show that the backdoor attacks can be effectively used to identify vulnerabilities in spam filters and suggest the need for ongoing monitoring and improvement in this area.