USN-6243-2: Graphite-Web regression

Read Time:41 Second

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)

Read More

USN-4336-3: GNU binutils vulnerabilities

Read Time:21 Second

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.

Read More

Using Machine Learning to Detect Keystrokes

Read Time:50 Second

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.

Read More

Multiple Vulnerabilities in Adobe Products Could Allow for Arbitrary Code Execution

Read Time:43 Second

Multiple vulnerabilities have been discovered in Adobe products, the most severe of which could allow for arbitrary code execution.

Adobe Acrobat is used to view, create, print, and manage PDF files.
Adobe Reader is used to view, create, print, and manage PDF files
Adobe Commerce is an offering that provides companies with a flexible and scalable end-to-end plate form to manage commerce experiences of their customers.
Successful exploitation of the most severe of these vulnerabilities could allow for arbitrary code execution in the context of the logged on user. Depending on the privileges associated with the user, an attacker could then install programs; view, change, or delete data; or create new accounts with full user rights. Users whose accounts are configured to have fewer user rights on the system could be less impacted than those who operate with administrative user rights.

Read More