J A W
Website | Docs | Setup | Crawler | Quick Start Docker (Example)
An open-source, prototype implementation of property graphs for JavaScript based on the esprima parser, and the EsTree SpiderMonkey Spec. JAW can be used for analyzing the client-side of web applications and JavaScript-based programs.
This project is licensed under GNU AFFERO GENERAL PUBLIC LICENSE V3.0. See here for more information.
JAW has a Github pages website available at https://soheilkhodayari.github.io/JAW/.
Release Notes:
- Dec 2024, JAW-v4: JAW updated with open redirect detection queries.
- Oct 2023, JAW-v3 (Sheriff): JAW updated to detect client-side request hijacking vulnerabilities.
- July 2022, JAW-v2 (TheThing): JAW updated to its next major release with the ability to detect DOM Clobbering vulnerabilities. See
JAW-V2branch. - Dec 2020, JAW-v1 : first prototype version. See
JAW-V1branch.
- Running the Pipeline
- Quick Example
- Crawling and Data Collection
- Graph Construction
- Security Analysis
- Test Web Application
The architecture of the JAW is shown below.
JAW can be used in two distinct ways:
-
Arbitrary JavaScript Analysis: Utilize JAW for modeling and analyzing any JavaScript program by specifying the program's file system
path. -
Web Application Analysis: Analyze a web application by providing a single seed URL.
- JAW features several JavaScript-enabled web crawlers for collecting web resources at scale.
-
Use the collected web resources to create a Hybrid Program Graph (HPG), which will be imported into a Neo4j database.
-
Optionally, supply the HPG construction module with a mapping of semantic types to custom JavaScript language tokens, facilitating the categorization of JavaScript functions based on their purpose (e.g., HTTP request functions).
-
Query the constructed
Neo4jgraph database for various analyses. JAW offers utility traversals for data flow analysis, control flow analysis, reachability analysis, and pattern matching. These traversals can be used to develop custom security analyses. -
JAW also includes built-in traversals for detecting client-side CSRF, DOM Clobbering and request hijacking vulnerabilities.
-
The outputs will be stored in the same folder as that of input.
The installation script relies on the following prerequisites:
- Latest version of
npm package manager(node js) - Any stable version of
python 3.x - Python
pippackage manager
Afterwards, install the necessary dependencies via:
$ ./install.shFor detailed installation instructions, please see here.
You can run an instance of the pipeline in a background screen via:
$ python3 -m run_pipeline --conf=config.yamlThe CLI provides the following options:
$ python3 -m run_pipeline -h
usage: run_pipeline.py [-h] [--conf FILE] [--site SITE] [--list LIST] [--from FROM] [--to TO]
This script runs the tool pipeline.
optional arguments:
-h, --help show this help message and exit
--conf FILE, -C FILE pipeline configuration file. (default: config.yaml)
--site SITE, -S SITE website to test; overrides config file (default: None)
--list LIST, -L LIST site list to test; overrides config file (default: None)
--from FROM, -F FROM the first entry to consider when a site list is provided; overrides config file (default: -1)
--to TO, -T TO the last entry to consider when a site list is provided; overrides config file (default: -1)
Input Config: JAW expects a .yaml config file as input. See config.yaml for an example.
Hint. The config file specifies different passes (e.g., crawling, static analysis, etc) which can be enabled or disabled for each vulnerability class. This allows running the tool building blocks individually, or in a different order (e.g., crawl all webapps first, then conduct security analysis).
For running a quick example demonstrating how to build a property graph and run Cypher queries over it, do:
$ python3 -m analyses.example.example_analysis --input=$(pwd)/data/test_program/test.jsThis module collects the data (i.e., JavaScript code and state values of web pages) needed for testing. If you want to test a specific JavaScipt file that you already have on your file system, you can skip this step.
JAW has crawlers based on Selenium (JAW-v1), Puppeteer (JAW-v2, v3) and Playwright (JAW-v3). For most up-to-date features, it is recommended to use the Puppeteer- or Playwright-based versions.
This web crawler employs foxhound, an instrumented version of Firefox, to perform dynamic taint tracking as it navigates through webpages. To start the crawler, do:
$ cd crawler
$ node crawler-taint.js --seedurl=https://google.com --maxurls=100 --headless=true --foxhoundpath=<optional-foxhound-executable-path>The foxhoundpath is by default set to the following directory: crawler/foxhound/firefox which contains a binary named firefox.
Note: you need a build of foxhound to use this version. An ubuntu build is included in the JAW-v3 release.
To start the crawler, do:
$ cd crawler
$ node crawler.js --seedurl=https://google.com --maxurls=100 --browser=chrome --headless=trueSee here for more information.
To start the crawler, do:
$ cd crawler/hpg_crawler
$ vim docker-compose.yaml # set the websites you want to crawl here and save
$ docker-compose build
$ docker-compose up -dPlease refer to the documentation of the hpg_crawler here for more information.
To generate an HPG for a given (set of) JavaScript file(s), do:
$ node engine/cli.js --lang=js --graphid=graph1 --input=/in/file1.js --input=/in/file2.js --output=$(pwd)/data/out/ --mode=csv
optional arguments:
--lang: language of the input program
--graphid: an identifier for the generated HPG
--input: path of the input program(s)
--output: path of the output HPG, must be i
--mode: determines the output format (csv or graphML)
To import an HPG inside a neo4j graph database (docker instance), do:
$ python3 -m hpg_neo4j.hpg_import --rpath=<path-to-the-folder-of-the-csv-files> --id=<xyz> --nodes=<nodes.csv> --edges=<rels.csv>
$ python3 -m hpg_neo4j.hpg_import -h
usage: hpg_import.py [-h] [--rpath P] [--id I] [--nodes N] [--edges E]
This script imports a CSV of a property graph into a neo4j docker database.
optional arguments:
-h, --help show this help message and exit
--rpath P relative path to the folder containing the graph CSV files inside the `data` directory
--id I an identifier for the graph or docker container
--nodes N the name of the nodes csv file (default: nodes.csv)
--edges E the name of the relations csv file (default: rels.csv)
In order to create a hybrid property graph for the output of the hpg_crawler and import it inside a local neo4j instance, you can also do:
$ python3 -m engine.api <path> --js=<program.js> --import=<bool> --hybrid=<bool> --reqs=<requests.out> --evts=<events.out> --cookies=<cookies.pkl> --html=<html_snapshot.html>Specification of Parameters:
<path>: absolute path to the folder containing the program files for analysis (must be under theengine/outputsfolder).--js=<program.js>: name of the JavaScript program for analysis (default:js_program.js).--import=<bool>: whether the constructed property graph should be imported to an active neo4j database (default: true).--hybrid=bool: whether the hybrid mode is enabled (default:false). This implies that the tester wants to enrich the property graph by inputing files for any of the HTML snapshot, fired events, HTTP requests and cookies, as collected by the JAW crawler.--reqs=<requests.out>: for hybrid mode only, name of the file containing the sequence of obsevered network requests, pass the stringfalseto exclude (default:request_logs_short.out).--evts=<events.out>: for hybrid mode only, name of the file containing the sequence of fired events, pass the stringfalseto exclude (default:events.out).--cookies=<cookies.pkl>: for hybrid mode only, name of the file containing the cookies, pass the stringfalseto exclude (default:cookies.pkl).--html=<html_snapshot.html>: for hybrid mode only, name of the file containing the DOM tree snapshot, pass the stringfalseto exclude (default:html_rendered.html).
For more information, you can use the help CLI provided with the graph construction API:
$ python3 -m engine.api -hThe constructed HPG can then be queried using Cypher or the NeoModel ORM.
You should place and run your queries in analyses/<ANALYSIS_NAME>.
You can use the NeoModel ORM to query the HPG. To write a query:
- (1) Check out the HPG data model and syntax tree.
- (2) Check out the ORM model for HPGs
- (3) See the example query file provided;
example_query_orm.pyin theanalyses/examplefolder.
$ python3 -m analyses.example.example_query_orm For more information, please see here.
You can use Cypher to write custom queries. For this:
- (1) Check out the HPG data model and syntax tree.
- (2) See the example query file provided;
example_query_cypher.pyin theanalyses/examplefolder.
$ python3 -m analyses.example.example_query_cypherFor more information, please see here.
This section describes how to configure and use JAW for vulnerability detection, and how to interpret the output. JAW contains, among others, self-contained queries for detecting client-side CSRF and DOM Clobbering
Step 1. enable the analysis component for the vulnerability class in the input config.yaml file:
request_hijacking:
enabled: true
# [...]
open_redirect:
enabled: false
# [...]
domclobbering:
enabled: false
# [...]
cs_csrf:
enabled: false
# [...]Step 2. Run an instance of the pipeline with:
$ python3 -m run_pipeline --conf=config.yamlHint. You can run multiple instances of the pipeline under different screens:
$ screen -dmS s1 bash -c 'python3 -m run_pipeline --conf=conf1.yaml; exec sh'
$ screen -dmS s2 bash -c 'python3 -m run_pipeline --conf=conf2.yaml; exec sh'
$ # [...]To generate parallel configuration files automatically, you may use the generate_config.py script.
The outputs will be stored in a file called sink.flows.out in the same folder as that of the input. For Client-side CSRF, for example, for each HTTP request detected, JAW outputs an entry marking the set of semantic types (a.k.a, semantic tags or labels) associated with the elements constructing the request (i.e., the program slices). For example, an HTTP request marked with the semantic type ['WIN.LOC'] is forgeable through the window.location injection point. However, a request marked with ['NON-REACH'] is not forgeable.
An example output entry is shown below:
[*] Tags: ['WIN.LOC']
[*] NodeId: {'TopExpression': '86', 'CallExpression': '87', 'Argument': '94'}
[*] Location: 29
[*] Function: ajax
[*] Template: ajaxloc + "/bearer1234/"
[*] Top Expression: $.ajax({ xhrFields: { withCredentials: "true" }, url: ajaxloc + "/bearer1234/" })
1:['WIN.LOC'] variable=ajaxloc
0 (loc:6)- var ajaxloc = window.location.href
This entry shows that on line 29, there is a $.ajax call expression, and this call expression triggers an ajax request with the url template value of ajaxloc + "/bearer1234/, where the parameter ajaxloc is a program slice reading its value at line 6 from window.location.href, thus forgeable through ['WIN.LOC'].
In order to streamline the testing process for JAW and ensure that your setup is accurate, we provide a simple node.js web application which you can test JAW with.
First, install the dependencies via:
$ cd tests/test-webapp
$ npm installThen, run the application in a new screen:
$ screen -dmS jawwebapp bash -c 'PORT=6789 npm run devstart; exec sh'For more information, visit our wiki page here. Below is a table of contents for quick access.
Pull requests are always welcomed. This project is intended to be a safe, welcoming space, and contributors are expected to adhere to the contributor code of conduct.
If you use the JAW for academic research, we encourage you to cite the following paper:
@inproceedings{JAW,
title = {JAW: Studying Client-side CSRF with Hybrid Property Graphs and Declarative Traversals},
author= {Soheil Khodayari and Giancarlo Pellegrino},
booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)},
year = {2021},
address = {Vancouver, B.C.},
publisher = {{USENIX} Association},
}
JAW has come a long way and we want to give our contributors a well-deserved shoutout here!