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Gadget Hunter (GHunter) for the Node.js runtime

This artifact contains the part of tool and experiments from the paper "GHunter: Universal Prototype Pollution Gadgets in JavaScript Runtimes" for the Node.js analysis.

Citation

If you use the paper, tool, and/or experiment results for academic research we encourage you to cite it as:

@inproceedings{GHunter2024,
  title={GHunter: Universal Prototype Pollution Gadgets in JavaScript Runtimes},
  author={Cornelissen, Eric and Shcherbakov, Mikhail and Balliu, Musard},
  booktitle={33rd USENIX Security Symposium (USENIX Security 24)},
  pages={3693--3710},
  year={2024}
}

Artifact

The artifact implements dynamic analysis for detecting prototype pollution in the Node.js JavaScript runtime. The analysis modifies the Node.js runtime (the files in patches/ represent all modifications) and runs the Node.js test suite. You can run the analysis by following the instructions below.

Requirements

Hardware

We conducted the original experiments on an AMD EPYC 7742 64-Core 2.25 GHz server with 512 GB RAM, and 1 TB of disk space. We have also run the experiments partially (see below) on an AMD Ryzen 7 3700x 8-core CPU (3.60GHz), 32 GB RAM, and 50 GB of disk space. No specific hardware features are required.

Software

We originally ran the experiments on Ubuntu 22.04. We used Docker as an OCI container runtime.

Setup

We provide two modes for experiment evaluation:

  1. A container image with a prepared environment.
  2. Instructions on how to set up the environment on own machine.

Container

To run the experiments in a prepared environment you can use the container image defined for this project. You can either use a prebuild image or build the image from scratch.

Prebuild

To use the prebuild container image, pull the image ghcr.io/kth-langsec/ghunter4node, launch the container, and attach a bash shell to the container to get started.

docker pull ghcr.io/kth-langsec/ghunter4node:latest
docker run -dit --name ghunter4node ghcr.io/kth-langsec/ghunter4node:latest
docker exec -it ghunter4node /bin/bash

Build

To build the container image from scratch you can clone the repository and run the helper script container.sh. This will build the image, start a container, and attach to it automatically.

git clone [email protected]:KTH-LangSec/ghunter4node.git
cd ghunter4node
./container.sh

Local Installation

To run the experiments locally, clone the repository with submodules recursively:

git clone --recurse-submodules [email protected]:KTH-LangSec/ghunter4node.git

and set up the Node.js development prerequisites.

Basic Test

As a basic test we provide a way to run the source-to-sink analysis on a single test. To perform this basic test run ./run-basic_test.sh. This requires compiling Node.js, which can take up to hour. The analysis itself is expected to take about a minute.

This will produce a directory named node/fuzzing/X-YYYY-MM-DD-HH-MM-SS containing subdirectories for each detected sink with one SARIF file each. This analysis is expected to yield about 10 unique s2s pairs after filtering.

Experiments

We provide two experiment modes, partial and full. The motivation for this is that a full analysis of Node.js requires advanced hardware (see hardware requirements) and significant compute time. We recommend a partial experiment to assess the tool.

Partial

As a partial experiment we recommend analyzing Node.js' child_process API. This can be done by running two separate scripts, one for the source-to-sink analysis and one for the unexpected-termination analysis.

To run the source-to-sink analysis run ./run-child_process-s2s.sh, optionally with a number of workers (default 5) and test timeout (default 20s). This is expected to take at most 4 hours.

This script may show a shell prompt before finishing, make sure the script has outputted "All tests passed" before continuing. The script may get stuck after printing "All tests passed" a second time. If this happens, press Enter or Ctrl+C and rerun the script (this will continue where it left off).

This will output a summary about the analysis in the end and produce per-sink SARIF files for manual review in a node/fuzzing/X-YYYY-MM-DD-HH-MM-SS directory.

To run the unexpected-termination analysis run ./run-child_process-crashes.sh (after ./run-child_process-s2s.sh), optionally with a number of workers (default 5) and test timeout (default 20s). This is expected to take at most 2 hours.

This will output a summary about the analysis in the end and produce per-sink SARIF files for manual review in a node/fuzzing/X-YYYY-MM-DD-HH-MM-SS directory.

Full

Running the full experiment requires advanced hardware, notably a large amount of memory. Performing the full analysis requires running two separate scripts. One for the source-to-sink analysis and one for the unexpected-termination analysis.

To run the source-to-sink analysis run ./run-all-s2s.sh. On the original hardware this took approximately 72 hours with 64 workers. This will output numbers about the analysis in the end and produce per-sink SARIF files for manual review in a node/fuzzing/X-YYYY-MM-DD-HH-MM-SS directory.

To run the unexpected-termination analysis run ./run-all-crashes.sh (after ./run-all-s2s.sh). On the original hardware this took approximately 24 hours with 64 workers. This will output numbers about the analysis in the end and produce per-sink SARIF files for manual review in a node/fuzzing/X-YYYY-MM-DD-HH-MM-SS directory.

Comparison to Silent Spring

The experiments for a comparison to Silent Spring can be run as follows. No additional setup is required to run these experiments. These experiments can be run on the more modest hardware described above.

On Node.js v21.0.0

To run the experiments for the comparison on Node.js v21.0.0 run ./run-compare-ss-21.sh. This requires compiling Node.js, which can take up to hour. The analysis itself is expected to take about a minute.

This will produce 9 folders following the naming scheme X-YYYY-MM-DD-HH-MM-SS in node/fuzzing.ss21/. The X in the folder name maps to Table 3 of the paper according to the mapping below. Each folder will contain per-sink SARIF files as well as a file named compare.json and count.txt. The count.txt file contains the number presented as "GC" in Table 3. compare.json contains more detailed information about the detected sinks, including true and false positives. Verifying these is a manual process.

Folder index API from Table 3
1 cp.exec
2 cp.execFile
3 cp.execFileSync
4 cp.execSync
5 cp.fork
6 cp.spawn
7 cp.spawnSync
8 import
9 vm.compileFunction

On Node.js v16.13.1

To run the experiments for the comparison on Node.js v16.13.1 run ./run-compare-ss-16.sh. This requires compiling Node.js, which can take up to hour. The analysis itself is expected to take about a minute.

This will produce 11 folders following the naming scheme X-YYYY-MM-DD-HH-MM-SS in node/fuzzing.ss16/. The X in the folder name maps to Table 2 of the paper according to the mapping below. Each folder will contain per-sink SARIF files as well as a file named compare.json and count.txt. The count.txt file contains the number presented as "GC" in Table 2. compare.json contains more detailed information about the detected sinks, including true and false positives. Verifying these is a manual process.

Folder index API from Table 2
1 cp.exec
2 cp.execFile
3 cp.execFileSync
4 cp.execSync
5 cp.fork
6 cp.spawn
7 cp.spawnSync
8 import
9+10 require
11 vm.compileFunction

Additional content

This repository also contains:

  • results: A snapshot of results from an earlier run.
  • src: Various scripts and files to run GHunter on Node.js.
  • tests: A set of basic tests for GHunter.