argon2-kraken

Based on the project argon2-gpu by Ondrej Mosnáček.

The argon2-kraken program is a password cracking tool that uses the Argon2 hashing algorithm. The program can use either the OpenCL or CUDA library to run the Argon2 hashing algorithm on the GPU. The program takes in a leftlist file, a wordlist file, and a potfile, and outputs the successfully cracked passwords to the potfile.

This program implements an association attack technique inspired by hashcat. Instead of comparing each line from the leftlist with each line from the wordlist, it associates each line from the leftlist with the line in the same position in the wordlist. This approach is intended to improve performance and reduce the amount of time required to crack passwords.

Usage

argon2-kraken [mode: opencl or cuda] [leftlist] [wordlist] [potfile]

Notes

In Argon2, the memory size is defined in kilobytes, and the amount of memory used is calculated as m times the block size r times the parallelism p.

The block size r is a fixed parameter in Argon2 and is equal to 1024 bytes. Therefore, with m=65536 and a parallelism factor of p=1, the amount of memory used would be m*r*p = 65536*1024*1 = 67,108,864 bytes or 64 MB. TODO: Detect amount of workers based on available GPU memory. 1080TI I use for testing has 11264 MB of on board memory, example of the hash used for testing:

$argon2id$v=19$m=65536,t=1,p=4$qK32Vuin0v8USlgec6lDFw$w5yjWJqZxCfM4EO3S9jBONpfCx0EBlyxd3MfRFhdn6U

65536*1024*4 == 268Mb
11264 / 268 == 42 (lol)

BUT! At least in case of 1080TI we are not getting anywhere close to this memory utilization (floats around 20%), as GPU chip itself is a bottleneck that is being used up to 99%.

TODO

1. Bring the rest of CUDA kernel variants
2. Add precompute argument

Building

This project uses the CMake build system.

First, if you haven't cloned the repository using git clone --recursive, you need to run:

git submodule update --init

Then, to prepare build:

cmake -DCMAKE_BUILD_TYPE=Release .

Finally, just run make to build the code. Note that to use the OpenCL backend, you need to have the data subdirectory in the working directory (if you have the binaries in a different directory, just create a symlink using ln -s <path_to_repo>/data data).