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https://github.com/AvalZ/WAF-A-MoLE
A guided mutation-based fuzzer for ML-based Web Application Firewalls
https://github.com/AvalZ/WAF-A-MoLE
adversarial-machine-learning machine-learning web web-application-firewall web-security
Last synced: 21 days ago
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A guided mutation-based fuzzer for ML-based Web Application Firewalls
- Host: GitHub
- URL: https://github.com/AvalZ/WAF-A-MoLE
- Owner: AvalZ
- License: mit
- Created: 2019-09-23T10:35:18.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-03-06T13:38:21.000Z (9 months ago)
- Last Synced: 2024-08-05T17:45:02.977Z (4 months ago)
- Topics: adversarial-machine-learning, machine-learning, web, web-application-firewall, web-security
- Language: Python
- Homepage:
- Size: 4.44 MB
- Stars: 163
- Watchers: 9
- Forks: 31
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-hacking-lists - AvalZ/WAF-A-MoLE - A guided mutation-based fuzzer for ML-based Web Application Firewalls (Python)
README
# WAF-A-MoLE
A *guided mutation-based fuzzer* for ML-based Web Application Firewalls, inspired by AFL and based on the [FuzzingBook](https://www.fuzzingbook.org) by Andreas Zeller et al.
Given an input SQL injection query, it tries to produce a *semantic invariant* query that is able to bypass the target WAF.
You can use this tool for assessing the robustness of your product by letting WAF-A-MoLE explore the solution space to find dangerous "blind spots" left uncovered by the target classifier.
[](https://www.python.org/downloads/release/python-374/)
[](https://github.com/AvalZ/WAF-A-MoLE/blob/master/LICENSE)
[](https://waf-a-mole.readthedocs.io/en/latest/?badge=latest)
# Architecture
WAF-A-MoLE takes an initial payload and inserts it in the payload **Pool**, which manages a priority queue ordered by the WAF confidence score over each payload.
During each iteration, the head of the payload Pool is passed to the **Fuzzer**, where it gets randomly mutated, by applying one of the available mutation operators.
## Mutation operators
Mutations operators are all *semantics-preserving* and they leverage the high expressive power of the SQL language (in this version, MySQL).
Below are the mutation operators available in the current version of WAF-A-MoLE.
| Mutation | Example |
| --- | --- |
| Case Swapping | `admin' OR 1=1#` ⇒ `admin' oR 1=1#` |
| Whitespace Substitution | `admin' OR 1=1#` ⇒ `admin'\t\rOR\n1=1#`|
| Comment Injection | `admin' OR 1=1#` ⇒ `admin'/**/OR 1=1#`|
| Comment Rewriting | `admin'/**/OR 1=1#` ⇒ `admin'/*xyz*/OR 1=1#abc`|
| Integer Encoding | `admin' OR 1=1#` ⇒ `admin' OR 0x1=(SELECT 1)#`|
| Operator Swapping | `admin' OR 1=1#` ⇒ `admin' OR 1 LIKE 1#`|
| Logical Invariant | `admin' OR 1=1#` ⇒ `admin' OR 1=1 AND 0<1#`|
| Number Shuffling | `admin' OR 1=1#` ⇒ `admin' OR 2=2#`|
# How to cite us
WAF-A-MoLE implements the methodology presented in ["WAF-A-MoLE: Evading Web Application Firewalls through Adversarial Machine Learning"](https://dl.acm.org/doi/abs/10.1145/3341105.3373962).
A pre-print of our article can also be found on [arXiv](https://arxiv.org/abs/2001.01952).
If you want to cite us, please use the following (BibTeX) reference:
```
@inproceedings{demetrio20wafamole,
title={WAF-A-MoLE: evading web application firewalls through adversarial machine learning},
author={Demetrio, Luca and Valenza, Andrea and Costa, Gabriele and Lagorio, Giovanni},
booktitle={Proceedings of the 35th Annual ACM Symposium on Applied Computing},
pages={1745--1752},
year={2020}
}
```
# Running WAF-A-MoLE
## Prerequisites
* [numpy](https://numpy.org/)
* [keras](https://keras.io/)
* [scikit-learn](https://scikit-learn.org/stable/)
* [joblib](https://github.com/joblib/joblib)
* [sqlparse](https://github.com/andialbrecht/sqlparse)
* [networkx](https://networkx.github.io/)
* [Click](https://click.palletsprojects.com/en/7.x/)
## Setup
`pip install -r requirements.txt`
## Sample Usage
You can evaluate the robustness of your own WAF, or try WAF-A-MoLE against some example classifiers.
In the first case, have a look at the [Model](https://github.com/AvalZ/waf-a-mole/blob/master/wafamole/models/model.py) class. Your custom model needs to implement this class in order to be evaluated by WAF-A-MoLE.
We already provide wrappers for *sci-kit learn* and *keras* classifiers that can be extend to fit your feature extraction phase (if any).
### Help
`wafamole --help`
```
Usage: wafamole [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
evade Launch WAF-A-MoLE against a target classifier.
```
`wafamole evade --help`
```
Usage: wafamole evade [OPTIONS] MODEL_PATH PAYLOAD
Launch WAF-A-MoLE against a target classifier.
Options:
-T, --model-type TEXT Type of classifier to load
-t, --timeout INTEGER Timeout when evading the model
-r, --max-rounds INTEGER Maximum number of fuzzing rounds
-s, --round-size INTEGER Fuzzing step size for each round (parallel fuzzing
steps)
--threshold FLOAT Classification threshold of the target WAF [0.5]
--random-engine TEXT Use random transformations instead of evolution
engine. Set the number of trials
--output-path TEXT Location were to save the results of the random
engine. NOT USED WITH REGULAR EVOLUTION ENGINE
--help Show this message and exit.
```
### Evading example models
We provide some pre-trained models you can have fun with, located in [wafamole/models/custom/example_models](https://github.com/AvalZ/waf-a-mole/tree/master/wafamole/models/custom/example_models).
The classifiers we used are listed in the table below.
| Classifier name| Algorithm
| --- | --- |
| [WafBrain](https://github.com/BBVA/waf-brain) | Recurrent Neural Network |
| [ML-Based-WAF](https://github.com/vladan-stojnic/ML-based-WAF) | Non-Linear SVM |
| [ML-Based-WAF](https://github.com/vladan-stojnic/ML-based-WAF) | Stochastic Gradient Descent |
| [ML-Based-WAF](https://github.com/vladan-stojnic/ML-based-WAF) | AdaBoost |
| Token-based | Naive Bayes |
| Token-based | Random Forest |
| Token-based | Linear SVM |
| Token-based | Gaussian SVM |
| [SQLiGoT](https://www.sciencedirect.com/science/article/pii/S0167404816300451) - Directed Proportional | Gaussian SVM |
| [SQLiGoT](https://www.sciencedirect.com/science/article/pii/S0167404816300451) - Directed Unproportional | Gaussian SVM |
| [SQLiGoT](https://www.sciencedirect.com/science/article/pii/S0167404816300451) - Undirected Proportional | Gaussian SVM |
| [SQLiGoT](https://www.sciencedirect.com/science/article/pii/S0167404816300451) - Undirected Unproportional | Gaussian SVM |
In addition to ML-based WAF, WAF-a-MoLE supports also rule-based WAFs.
Specifically, it provides a wrapper for the ModSecurity WAF equipped with the [OWASP Core Rule Set (CRS)](https://coreruleset.org), based on the [pymodsecurity](https://github.com/pymodsecurity/pymodsecurity) project.
#### WAF-BRAIN - Recurrent Neural Newtork
Bypass the pre-trained WAF-Brain classifier using a `admin' OR 1=1#` equivalent.
```bash
wafamole evade --model-type waf-brain wafamole/models/custom/example_models/waf-brain.h5 "admin' OR 1=1#"
```
#### ML-Based-WAF - Non-Linear SVM (with original WAF-A-MoLE dataset)
Bypass the pre-trained ML-Based-WAF SVM classifier using a `admin' OR 1=1#` equivalent.
```bash
wafamole evade --model-type mlbasedwaf wafamole/models/custom/example_models/mlbasedwaf_svc.dump "admin' OR 1=1#"
```
#### ML-Based-WAF - Non-Linear SVM (with SQLiV5/SQLiV3 datasets)
Bypass the pre-trained ML-Based-WAF SVM classifier using a `admin' OR 1=1#` equivalent. Note that SQLiV5 is a dataset sourced from [Kaggle](https://www.kaggle.com/datasets/syedsaqlainhussain/sql-injection-dataset) expanded with a series of queries generated by WAF-A-MoLE itself, as a proof of concept that WAF-A-MoLE queries can enhance the robustness of a WAF with retraining. Use `mlbasedwaf_svc_sqliv3.dump` to bypass the WAF trained with the original Kaggle dataset (SQLiV3).
```bash
wafamole evade --model-type mlbasedwaf wafamole/models/custom/example_models/mlbasedwaf_svc_sqliv5.dump "admin' OR 1=1#"
```
#### ML-Based-WAF - Stochastic Gradient Descent (SGD)
Bypass the pre-trained ML-Based-WAF SGD classifier using a `admin' OR 1=1#` equivalent.
```bash
wafamole evade --model-type mlbasedwaf wafamole/models/custom/example_models/mlbasedwaf_sgd.dump "admin' OR 1=1#"
```
#### ML-Based-WAF - AdaBoost
Bypass the pre-trained ML-Based-WAF AdaBoost classifier using a `admin' OR 1=1#` equivalent (takes longer than other models, at around 2 to 5 minutes of runtime).
```bash
wafamole evade --model-type mlbasedwaf wafamole/models/custom/example_models/mlbasedwaf_ada.dump "admin' OR 1=1#"
```
#### Token-based - Naive Bayes
Bypass the pre-trained token-based Naive Bayes classifier using a `admin' OR 1=1#` equivalent.
```bash
wafamole evade --model-type token wafamole/models/custom/example_models/naive_bayes_trained.dump "admin' OR 1=1#"
```
#### Token-based - Random Forest
Bypass the pre-trained token-based Random Forest classifier using a `admin' OR 1=1#` equivalent.
```bash
wafamole evade --model-type token wafamole/models/custom/example_models/random_forest_trained.dump "admin' OR 1=1#"
```
#### Token-based - Linear SVM
Bypass the pre-trained token-based Linear SVM classifier using a `admin' OR 1=1#` equivalent.
```bash
wafamole evade --model-type token wafamole/models/custom/example_models/lin_svm_trained.dump "admin' OR 1=1#"
```
#### Token-based - Gaussian SVM
Bypass the pre-trained token-based Gaussian SVM classifier using a `admin' OR 1=1#` equivalent.
```bash
wafamole evade --model-type token wafamole/models/custom/example_models/gauss_svm_trained.dump "admin' OR 1=1#"
```
#### SQLiGoT
Bypass the pre-trained SQLiGOT classifier using a `admin' OR 1=1#` equivalent.
Use **DP**, **UP**, **DU**, or **UU** for (respectivly) Directed Proportional, Undirected Proportional, Directed Unproportional and Undirected Unproportional.
```bash
wafamole evade --model-type DP wafamole/models/custom/example_models/graph_directed_proportional_sqligot "admin' OR 1=1#"
```
#### OWASP ModSecurity CRS - Rule-based WAF
Bypass the OWASP ModSecurity CRS using a `admin' OR 1=1#` equivalent.
The user also need to specify the [Paranoia Level](https://coreruleset.org/docs/concepts/paranoia_levels/) as well as the path to locate the CRS rules (e.g., `/etc/coreruleset`).
```bash
wafamole evade --model-type modsecurity_pl[1-4] /etc/coreruleset "admin' OR 1=1#"
```
**BEFORE LAUNCHING EVALUATION ON SQLiGoT**
These classifiers are more robust than the others, as the feature extraction phase produces vectors with a more complex structure, and all pre-trained classifiers have been strongly regularized.
It may take hours for some variants to produce a payload that achieves evasion (see Benchmark section).
**Note on newer ML-Based-WAF models**
Some models based on a slightly modified version of vladan-stojnic's [ML-Based-WAF](https://github.com/vladan-stojnic/ML-based-WAF) have been recently added, from an extension of WAF-A-MoLE entitled [wafamole++](https://github.com/nidnogg/wafamole-plusplus) by [nidnogg](https://github.com/nidnogg). Testing the AdaBoost model might take a longer time than usual (usually 2 to 5 minutes).
There are variants trained with the [SQLiV5.json](https://github.com/nidnogg/sqliv5-dataset) dataset, while most use the original SQL injection from WAF-A-MoLE dataset by default.
A Google Colaboratory [notebook](https://colab.research.google.com/drive/1YPHb8lrbxN6RjJWjwvM1upvsVoV7de8r?usp=sharing) is provided with the training routines for some of these models, using the original WAF-A-MoLE dataset (modified to the SQLiV5 format). Any dataset can be used as long as they're in the same format as SQLiV5.json.
### Custom adapters
First, create a custom Model class that implements the `extract_features` and `classify` methods.
```python
class YourCustomModel(Model):
def extract_features(self, value: str):
# TODO: extract features
feature_vector = your_custom_feature_function(value)
return feature_vector
def classify(self, value):
# TODO: compute confidence
confidence = your_confidence_eval(value)
return confidence
```
Then, create an object from the model and instantiate an `engine` object that uses your model class.
```python
model = YourCustomModel() #your init
engine = EvasionEngine(model)
result = engine.evaluate(payload, max_rounds, round_size, timeout, threshold)
```
# Benchmark
We evaluated WAF-A-MoLE against all our example models.
The plot below shows the time it took for WAF-A-MoLE to mutate the `admin' OR 1=1#` payload until it was accepted by each classifier as benign.
On the *x* axis we have time (in seconds, logarithmic scale).
On the *y* axis we have the *confidence* value, i.e., how sure a classifier is that a given payload is a SQL injection (in percentage).
Notice that being "50% sure" that a payload is a SQL injection is equivalent to flipping a coin.
This is the usual classification threshold: if the confidence is lower, the payload is classified as benign.
Experiments were performed on [DigitalOcean *Standard* Droplets](https://www.digitalocean.com/products/droplets/).
# Contribute
Questions, bug reports and pull requests are welcome.
In particular, if you are interested in expanding this project, we look for the following contributions:
1. New WAF adapters
1. New mutation operators
1. New search algorithms
# Team
* [Luca Demetrio](http://csec.it/people/luca_demetrio/) - [CSecLab](https://csec.it/), DIBRIS, University of Genova
* [Andrea Valenza](https://avalz.it/) - [CSecLab](https://csec.it/), DIBRIS, University of Genova
* [Gabriele Costa](https://www.imtlucca.it/it/gabriele.costa) - [SysMA](http://sysma.imtlucca.it/), IMT Lucca
* [Giovanni Lagorio](https://csec.it/people/giovanni_lagorio/) - [CSecLab](https://csec.it/), DIBRIS, University of Genova