Cracking With Cloud Amazon

, Amazon EC2 is providing what they call “Cluster GPU Instances”:  An instance in the Amazon cloud that provides you with the power of two NVIDIA Tesla “Fermi” M2050 GPUs. The exact specifications look like this:

22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge

GPUs are known to be the best hardware accelerator for cracking passwords, so I decided to give it a try: How fast can this instance type be used to crack SHA1 hashes?

Using the CUDA-Multiforce, I was able to crack all hashes from this file with a password length from 1-6 in only 49 Minutes (1 hour costs 2.10$ by the way.):

Compute done: Reference time 2950.1 seconds
Stepping rate: 249.2M MD4/s
Search rate: 3488.4M NTLM/s

This just shows one more time that SHA1 for password hashing is deprecated – You really don’t want to use it anymore! Instead, use something like scrypt or PBKDF2! Just imagine a whole cluster of these machines (which is now easily available to anybody thanks to Amazon) cracking passwords for you. Pretty comfortable, large-scale password cracking for everybody!

Some more details:

• Multiforcer Output
• Hashes
• Charset
• Makefile
• cpuinfo
• meminfo
• nvsmi

If I find the time, I’ll write a tool which uses the AWS-API to launch on-demand password-cracking instances with a preconfigured AMI. Stay tuned either via RSS or via Twitter.

Installation Instructions:

I used the “Cluster Instances HVM CentOS 5.5 (AMI Id: ami-aa30c7c3)” machine image as provided by Amazon — I chose this because it was the only image with CUDA support built in — and selected “Cluster GPU (cg1.4xlarge, 22GB)” as the instance type. After launching the instance and SSHing into it, you can continue by installing the cracker:

I decided to install the “CUDA-Multiforcer” in version 0.7, as it’s the latest version of which the source is available. To compile it, you first need to download the “GPU Computing SDK code samples“:

# wget  http://developer.download.nvidia.com/compute/cuda/3_2/sdk/gpucomputingsdk_3.2.12_linux.run
# chmod +x gpucomputingsdk_3.2.12_linux.run
# ./gpucomputingsdk_3.2.12_linux.run
(Just press enter when asked for the installation directory and the CUDA directory.)

Now we need to install the g++ compiler:

# yum install automake autoconf gcc-c++

The next step is compiling the libraries of the SDK samples:

# cd ~/NVIDIA_GPU_Computing_SDK/C/
# make lib/libcutil.so
# make shared/libshrutil.so

Now it’s time to download and compile the CUDA-Multiforcer:

# cd ~/NVIDIA_GPU_Computing_SDK/C/
# wget http://www.cryptohaze.com/releases/CUDA-Multiforcer-src-0.7.tar.bz2 -O src/CUDA-Multiforcer.tar.bz2
# cd src/
# tar xjf CUDA-Multiforcer.tar.bz2
# cd CUDA-Multiforcer-Release/argtable2-9/
# ./configure && make && make install
# cd ../

Since the Makefile of the CUDA-Multiforcer doesn’t work out of the box, we need to open it up and find the line

CCFILES := -largtable2 -lcuda

Replace CCFILES with LINKFLAGS so that the line looks like this:

LINKFLAGS := -largtable2 -lcuda

And type make. If everything worked out, you should have a file ~/NVIDIA_GPU_Computing_SDK/C/bin/linux/release/CUDA-Multiforcer. You can try the Multiforcer by doing something like this:

# export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
# export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
# cd ~/NVIDIA_GPU_Computing_SDK/C/src/CUDA-Multiforcer-Release/
# ../../bin/linux/release/CUDA-Multiforcer -h SHA1 -f test_hashes/Hashes-SHA1-Full.txt --min=1 --max=6 -c charsets/charset-upper-lower-numeric-symbol-95.chr

Congratulations, you now have a fully working, CUDA-based hash-cracker running on an Amazon EC2 instance.

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