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The use of deep neural networks for speech recognition and recognizing speech commands continues to grow. This necessitates an understanding of the security risks that goes along with this technology. This paper analyzes the ability to interfere with the performance of neural networks for speech pattern recognition. With the methods proposed herein, it is a simple matter to create adversarial data by overlaying audio of a command at a fairly unnoticeable amplitude. This causes the neural network to lose around 20% accuracy and misidentify commands for other commands with an average to high confidence value. Such an attack is virtually undetectable to the human ear. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 

Fuzzing is the art of creating data and using that generated data as input for a target program. The goal behind this is to crash the program in a manner that can be analyzed and exploited. Software developers are able to benefit from fuzzers, as they can patch the discovered vulnerabilities before an attacker exploits them. Programs are becoming larger and require improved fuzzers to keep up with the increased attack surface. Most innovations in fuzzer development are software related and provide better path coverage or data generation. This paper proposes creating a fuzzer that is designed to utilize a dedicated graphics card's graphics processing unit (GPU) instead of the standard processor. Much of the code within the fuzzer is parallelizable, meaning the graphics card could potentially process it in a much more efficient manner. The effectiveness of GPU fuzzing is assessed herein. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.