Wisecracker A high performance distributed cryptanalysis framework

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1 Wisecracker A high performance distributed cryptanalysis framework A Technical White Paper October Written by Vikas N Kumar Introduction Cryptanalysis can be performed in various ways such as by using number theory to discover flaws in existing cryptographic algorithms or by using brute force to break the cryptography by using numerous computers. Discovering number theory related flaws might be near impossible to find or might need a Ph.D. in mathematics, but using brute force might make it easy to break such algorithms. Wisecracker aims to tackle cryptanalysis by using the brute force method. However, brute force cryptanalysis, depending on the algorithm, might still need large scale supercomputers or massively parallel clusters of computers to be able to work in a reasonable amount of time that is countably finite. Some algorithms might take months or years to crack while some might take minutes or seconds. Cracking algorithms that might take weeks or months is possible by those who have access to supercomputers and

4 compute units and work group size). Amazon's VMs have 2 GPUs each. When a single system is being used the tasks are distributed in the range [1, 690billion] between 2 GPUs by dividing the range into smaller blocks of size based on the product of the compute units and workgroup size. So if a GPU's compute units are 32 in number and have a work group size of 2048, it gets task blocks to work on. So one by one each GPU will keep computing on it successive task blocks that it is given until it finds the solution. When you use 2 systems, the tasks are distributed between each system as the ranges [1, 345billion] and (345billion, 690billion]. Once each system gets its task range, it distributes work between its GPUs in a similar fashion as task blocks based on compute units and work group size. Let's say you want to recover the string and it might be in the index range [345billion, 690billion]. If you were to run this on 1 Amazon GPU VM the program will have to compute for [1, 345billion] range first and then get to the (345billion, 690billion] range. However, if you distribute this on 2 VMs you will hit upon the solution faster because the second system is starting from 345 billion and you might not need to compute all the 345 billion possible values on each VM. You are saved from the needless computation of the [1, 345billion] range in full as done in the single system operation. Hence the 2 VMs give a bigger decline in time rather than 1 VM because of the way the work is distributed for the MD5 example, wisecrackmd5. On an average with different sets of strings the runtime drastically goes down because of the fact that the search buckets are smaller and start at different points. NOTE: We define average as the average time taken to extract the source string from the MD5, and that the code running on the CPUs is the same OpenCL that will run on the GPUs. Comparisons with highly optimized CPU code using SSE2 intrinsics has not been done at this time. As we can see, with the power of GPUs across multiple systems and the intelligent distribution of tasks by Wisecracker we can exponentially reduce the time taken to try out various combinations for MD5 reversing. This is true for any other algorithm as well although the time taken might vary based on the number of combinations to be tried out and the computational requirements of the algorithm itself per combination. Domain Decomposition The distribution of billions or trillions of combinations or tasks gets done by Wisecracker which uses a master-slave model to distribute the workload across multiple GPUs and CPUs. Figures 1 and 2 denote the distribution across GPUs and CPUs of a single system

6 Single System Run Let's say the user wants to reverse the MD5 sum 7e7cf8ad e63d8bfc12d0a0747 which is the MD5 sum for the string (without the NULL terminating character). If the user wants to run wisecrackmd5 on a single system, all they need to do is run the following command at the shell prompt: \$./wisecrackmd5 -N 6 -C alnumspl -M 7e7cf8ad e63d8bfc12d0a0747 Depending on the system properties, this might take a few hours on a system with 8 CPUs to maybe 20 minutes on the Amazon EC2 GPU VM to return the string To expedite the run for testing, the user can use the -p option to provide a hint to the program, like below: \$./wisecrackmd5 -N 6 -C alnumspl -M 7e7cf8ad e63d8bfc12d0a0747 -p a6c This might take a few seconds to complete as the number of combinations to try exponentially reduces to 830,584 from 689,869,781,056. To view all the options provided by the wisecrackmd5 application you can use the -h option: \$./wisecrackmd5 -h Multiple System Run For a multiple system run, the user has to setup MPI to work correctly across the systems. Each system will need to have OpenCL and MPI installed and also the wisecrackmd5 application present in the same path on both systems. For Linux and Mac OSX systems, we recommend using MPI over ssh on systems. It is necessary for ssh to work using public key authentication rather than passwords. For Windows, we recommend using Microsoft's recommended MPI or using OpenMPI's instructions for setting up MPI on Windows. Details of setting up MPI are out of the scope of this document. Once MPI has been setup, the user should create a host file with the names or IP addresses of the systems that will be running wisecrackmd5. An example host file that runs with 1 master and 2 slaves will look like this: slots=2

7 The reason the first system IP address has 2 slots is because the master executor uses 1 extra slot and does not perform computations, and hence we need to run 1 slave on that machine. The user might choose not to run a slave on the master machine. To run the wisecrackmd5 application using MPI, the user has to run the following command for 1 master and 2 slaves (3 processes) with the host file being as above will look like this: \$ mpiexec -np 3 -hostfile./path/to/hostfile./wisecrackmd5 -N 6 -C alnumspl -M 7e7cf8ad e63d8bfc12d0a0747 If we run the above command with 2 Amazon EC2 GPU VMs and number of processes as 3, it takes about seconds to retrieve the string If the user runs the MPI compiled application with 1 process, the master executor automatically switches to single system mode and will perform the computation itself. As a debugging method, the application can be run with 2 processes and it will run as 1 master and 1 slave which can help debug the communications between processes. Conclusion Wisecracker is an easy to use framework for security researchers to perform their own cryptanalysis by leveraging multiple systems with multi-core processors and GPUs. They can either use the provided sample applications or can write their own or enhance the samples themselves. The open source nature of Wisecracker allows the user to have the freedom to perform research as desired. The presence of cloud computing VMs such as those provided by Amazon can be leveraged to perform cryptanalysis on demand and Wisecracker helps the users by making distribution easier across systems. The source code of wisecracker can be downloaded from https://github.com/ vikasnkumar/wisecracker. Professional level technical support can be obtained by contacting the developers at Selective Intellect LLC by ing them at

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