# Fast Adversarial Label-Flipping Attack on Tabular Data

arXiv: https://arxiv.org/abs/2310.10744 · HTML: https://mlsanity.com/paper/2310.10744/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization](https://mlsanity.com/paper/1708.08689/) (2017)
- [Stronger Data Poisoning Attacks Break Data Sanitization Defenses](https://mlsanity.com/paper/1811.00741/) (2018)
- [Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning](https://mlsanity.com/paper/2205.01992/) (2022)
- [Label Sanitization against Label Flipping Poisoning Attacks](https://mlsanity.com/paper/1803.00992/) (2018)
- [On Defending Against Label Flipping Attacks on Malware Detection Systems](https://mlsanity.com/paper/1908.04473/) (2019)
- [Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detector](https://mlsanity.com/paper/2301.01044/) (2023)

## Built on by (most cited first, 3 of 3)

- [Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data](https://mlsanity.com/paper/2507.10998/) (2025)
- [Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks](https://mlsanity.com/paper/2310.16224/) (2023)
- [Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraints](https://mlsanity.com/paper/2607.07089/) (2026)

## Abstract (arXiv)

Machine learning models are increasingly used in fields that require high reliability such as cybersecurity. However, these models remain vulnerable to various attacks, among which the adversarial label-flipping attack poses significant threats. In label-flipping attacks, the adversary maliciously flips a portion of training labels to compromise the machine learning model. This paper raises significant concerns as these attacks can camouflage a highly skewed dataset as an easily solvable classification problem, often misleading machine learning practitioners into lower defenses and miscalculations of potential risks. This concern amplifies in tabular data settings, where identifying true labels requires expertise, allowing malicious label-flipping attacks to easily slip under the radar. To demonstrate this risk is inherited in the adversary's objective, we propose FALFA (Fast Adversarial Label-Flipping Attack), a novel efficient attack for crafting adversarial labels. FALFA is based on transforming the adversary's objective and employs linear programming to reduce computational complexity. Using ten real-world tabular datasets, we demonstrate FALFA's superior attack potential, highlighting the need for robust defenses against such threats.