riefre / edefuzz

EDEFuzz - Hunting excessive data exposure in web APIs

A tool to flag excessive data exposure vulnerabilities in web APIs. Tested on Ubuntu 20.04 and Windows 10 22H2.

The design and implementation of EDEFuzz are described in our paper.

Citing EDEFuzz

EDEFuzz was accepted for publication at the 46th International Conference on Software Engineering (ICSE 2024).

If you use EDEFuzz in your research, please cite our ICSE'2024 paper.

@inproceedings{pan2024edefuzz,
  title={EDEFuzz: A Web API Fuzzer for Excessive Data Exposures},
  author={Pan, Lianglu and Cohney, Shaanan and Murray, Toby and Pham, Van-Thuan},
  booktitle={Proceedings of the 46th IEEE/ACM International Conference on Software Engineering},
  pages={1--12},
  year={2024}
}

Folder structure

EDEFuzz
├── config: this folder stores configuration files, prepared manually (discussed in section 4.1)
│   ├── *.config: the configuration file, containing a sequence of instructions for EDEFuzz to interact with the web page under test
├── tests: contents in this folder are generated by the tool
│   ├── *.json: the API response produced by the server, used as a baseline in our experiments
│   └── *.data: the cached request-response pairs captured, allowing us to run a simulated server (discussed in section 4.2)
├── cache.py: to generate the cache file for a target API, used by `fuzzer.py`
├── constants.py: defines some constant values used in EDEFuzz, nothing special
├── database.py: database utility functions
├── engine.py: the fuzzer engine, used by `fuzzer.py` to execute test cases
├── fuzzer.py: main entry of EDEFuzz
├── mutate.py: generates mutations of an API response
├── README.md
└── report.py: the result analyser, used by `fuzzer.py` to analyse results
└── report_html.py: to generate the HTML file highlights flagged excessive fields, used by `report.py`

Initial setup

Docker setup

Run sudo docker-compose up. It will build two Docker images and run them. Once complete, and you should be able to see something begin with edefuzz-mysql | ... indicating the database is running.

• edefuzz-mysql: the database used by EDEFuzz to store data
• edefuzz: EDEFuzz will run within this container

If you encounter http: invalid Host header error, try sudo snap refresh docker --channel=latest/edge. You may find more information about this error in this link.

Usage

(In a new terminal window, )Get into the Docker container edefuzz's shell by: docker exec -it edefuzz bash. cd into EDEFuzz's folder by cd edefuzz.

Preparation

• Create a configuration file under config/ folder (discussed in section 4.1). You can find a few examples given in config/ folder.
• Run python3 fuzzer.py c [target] (e.g. python3 fuzzer.py c wikipedia) to generate request-response cache (used by simulated server in fuzzing stage, discussed in section 4.2). Note that configuration files provided by us may or may not work as those websites could have changed.

The request-response cache is stored at tests/[target].data. This process should typically take no more than a few seconds.

Test execution

• Run python3 fuzzer.py f [target] to execute test cases (e.g. python3 fuzzer.py f wikipedia). The below screenshot indicates the test execution is in progress.

The duration of this stage is mostly dependent on the number of mutants, ranging from a couple minutes to a few days. Outputs are stored in the database (the edefuzz-mysql Docker container). As a reference, the wikipedia example took around ten minutes on our end.

Analysing results

• Run python3 fuzzer.py r [target] to generate the results. The full process is discussed in section 4.4.

Flagged excessive data fields are reported in tests/[target].csv. Each line in tests/[target].csv indicates an identified excessive data field. A more user-friendly output is generated in tests/[target]_flagged.html (an example is shown below), containing the original JSON object, with flagged excessive fields highlighted in red.

A statistical summary is generated in report_stat.csv. Each line in report_stat.csv is in the form [target name], [test duration (min)], [number of data fields in API], [number of executed test cases], [number of excessive fields flagged], [extra flags indicating why EDEFuzz failed (explained in section 5.2 RQ2)]. This statistical summary is useful after testing multiple targets.

Dataset Used in Our Paper

The Australian Dataset

Appear in paper	Entity Name	[target] name used in EDEFuzz
Company-A	Australia Post	auspost
Company-B	Canvas	canvas
Company-C	Chemist Warehouse	chemistwarehouse
Company-D	Ikea Australia	ikea
Company-E	JB-HIFI	jbhifi
Company-F	JetStar	jetstar
Company-G	Myer	myer
Company-H	Volkswagen Australia	volkswagen

• Some businesses included in our Australian dataset have updated their websites (e.g. new API endpoints, the page we tested no longer accessible, etc.). The config files for those targets are revised in January 2024.
• Company-A's website is now protected by CAPTCHA (discussed in section 5.2 RQ2). While we still provide a config file for Company-A, it may fail the preparation phase.
• The config file for Company-B is not provided as it contains author's log-in credential.

The Alexa Top 200 Dataset

The Alexa top 200 dataset, less 131 that we excluded for numerous reasons (discussed in section 5.2 RQ2).

360	3dmgame	adobe	ali213	alibaba	aliexpress
alipay	aliyun	amazon	apple	archive	baidu
bankofamerica	bilibili	binance	bing	cctv	chess
cnn	coinmarketcap	csdn	deepl	deviantart	douban
douyu	dropbox	etsy	freepik	hupu	ilive
imdb	imgur	iqiyi	ixigua	lenovo	linkedin
microsoft	msn	nytimes	paypal	pinterest	primevideo
qq	salesforce	shutterstock	sina	sogou	sohu
spotify	stackexchange	steamcommunity	telegram	theguardian	tiktok
tradingview	tumblr	twitter	weather	wetransfer	wikipedia
wordpress	yahoo	youdao	youku	youtube	zhihu
zhihuishu	zoho	zoom

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.