# ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning

arXiv: https://arxiv.org/abs/2308.04964 · HTML: https://mlsanity.com/paper/2308.04964/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Functionality-preserving Black-box Optimization of Adversarial Windows Malware](https://mlsanity.com/paper/2003.13526/) (2020)
- [Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection](https://mlsanity.com/paper/1704.08996/) (2017)
- [DeltaPhish: Detecting Phishing Webpages in Compromised Websites](https://mlsanity.com/paper/1707.00317/) (2017)
- [WAF-A-MoLE: Evading Web Application Firewalls through Adversarial Machine Learning](https://mlsanity.com/paper/2001.01952/) (2020)
- [Evolutionary Multi-Task Injection Testing on Web Application Firewalls](https://mlsanity.com/paper/2206.05743/) (2022)

## Built on by (most cited first, 1 of 1)

- [One Detector Fits All: Robust and Adaptive Detection of Malicious Packages from PyPI to Enterprises](https://mlsanity.com/paper/2512.04338/) (2025)

## Abstract (arXiv)

Many Web Application Firewalls (WAFs) leverage the OWASP CRS to block incoming malicious requests. The CRS consists of different sets of rules designed by domain experts to detect well-known web attack patterns. Both the set of rules and the weights used to combine them are manually defined, yielding four different default configurations of the CRS. In this work, we focus on the detection of SQLi attacks, and show that the manual configurations of the CRS typically yield a suboptimal trade-off between detection and false alarm rates. Furthermore, we show that these configurations are not robust to adversarial SQLi attacks, i.e., carefully-crafted attacks that iteratively refine the malicious SQLi payload by querying the target WAF to bypass detection. To overcome these limitations, we propose (i) using machine learning to automate the selection of the set of rules to be combined along with their weights, i.e., customizing the CRS configuration based on the monitored web services; and (ii) leveraging adversarial training to significantly improve its robustness to adversarial SQLi manipulations. Our experiments, conducted using the well-known open-source ModSecurity WAF equipped with the CRS rules, show that our approach, named ModSec-AdvLearn, can (i) increase the detection rate up to 30%, while retaining negligible false alarm rates and discarding up to 50% of the CRS rules; and (ii) improve robustness against adversarial SQLi attacks up to 85%, marking a significant stride toward designing more effective and robust WAFs. We release our open-source code at https://github.com/pralab/modsec-advlearn.