Using Machine Learning to Detect Keystrokes

Researchers have trained a ML model to detect keystrokes by sound with 95% accuracy.

“A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards”

Abstract: With recent developments in deep learning, the ubiquity of microphones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever. This paper presents a practical implementation of a state-of-the-art deep learning model in order to classify laptop keystrokes, using a smartphone integrated microphone. When trained on keystrokes recorded by a nearby phone, the classifier achieved an accuracy of 95%, the highest accuracy seen without the use of a language model. When trained on keystrokes recorded using the video-conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium. Our results prove the practicality of these side channel attacks via off-the-shelf equipment and algorithms. We discuss a series of mitigation methods to protect users against these series of attacks…

August 9, 2023
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Keystroke sounds can betray passwords

Researchers from several UK universities have proven that the recorded sounds of laptop keystrokes can be used to obtain sensitive user data such as passwords with a high accuracy. Sounds of keystrokes can reveal passwords, other sensitive data Side-ch…

August 7, 2023
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How AI will affect cybersecurity: What we told the CFTC

Dan Guido, CEO The second meeting of the Commodity Futures Trading Commission’s Technology Advisory Committee (TAC) on July 18 focused on the effects of AI on the financial sector. During the meeting, I explained that AI has the potential to fundamentally change the balance between cyber offense and defense, and that we need security-focused benchmarks […]

July 31, 2023
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Indirect Instruction Injection in Multi-Modal LLMs

Interesting research: “(Ab)using Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs“:

Abstract: We demonstrate how images and sounds can be used for indirect prompt and instruction injection in multi-modal LLMs. An attacker generates an adversarial perturbation corresponding to the prompt and blends it into an image or audio recording. When the user asks the (unmodified, benign) model about the perturbed image or audio, the perturbation steers the model to output the attacker-chosen text and/or make the subsequent dialog follow the attacker’s instruction. We illustrate this attack with several proof-of-concept examples targeting LLaVa and PandaGPT…

July 28, 2023
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