Smashing Security podcast #334: Acoustic attacks, and the tears of a crypto rapper

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Razzlekhan, the self-proclaimed Crocodile of Wall Street, pleads guilty to the biggest crypto laundering scheme in history, and just how safe are you typing while on a Zoom call?

Meanwhile, Graham rants about public EV chargers.

All this and more is discussed in the latest edition of the award-winning “Smashing Security” podcast by cybersecurity veterans Graham Cluley and Carole Theriault.

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USN-6280-1: PyPDF2 vulnerability

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It was discovered that PyPDF2 incorrectly handled PDF files with certain
markers. If a user or automated system were tricked into processing a
specially crafted file, an attacker could possibly use this issue to
consume system resources, resulting in a denial of service.

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USN-6243-2: Graphite-Web regression

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USN-6243-1 fixed vulnerabilities in Graphite-Web. It was discovered that the
applied fix was incomplete. This update fixes the problem.

Original advisory details:

It was discovered that Graphite-Web incorrectly handled certain inputs. If a
user or an automated system were tricked into opening a specially crafted
input file, a remote attacker could possibly use this issue to perform
server-side request forgery and obtain sensitive information. This issue
only affected Ubuntu 16.04 LTS and Ubuntu 18.04 LTS. (CVE-2017-18638)

It was discovered that Graphite-Web incorrectly handled certain inputs. If a
user or an automated system were tricked into opening a specially crafted
input file, a remote attacker could possibly use this issue to perform
cross site scripting and obtain sensitive information. (CVE-2022-4728,
CVE-2022-4729, CVE-2022-4730)

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USN-4336-3: GNU binutils vulnerabilities

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USN-4336-1 fixed several vulnerabilities in GNU. This update provides
the corresponding update for Ubuntu 14.04 LTS.

Original advisory details:

It was discovered that GNU binutils contained a large number of security
issues. If a user or automated system were tricked into processing a
specially-crafted file, a remote attacker could cause GNU binutils to
crash, resulting in a denial of service, or possibly execute arbitrary
code.

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Using Machine Learning to Detect Keystrokes

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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.

News article.

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