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Category: cybercrime/malcode – Page 116

NSO Group, an Israeli tech firm, developed malware to hack iPhones by creating a “computer within a computer” capable of stealing sensitive data and sitting undetected for months or even years, researchers at Google have revealed.

The malware is part of NSO Group’s Pegasus software tool, which it is thought to have sold to countries including Azerbaijan, Bahrain, Saudi Arabia, India and the United Arab Emirates. US law-makers have called for sanctions against the firm.

An incredibly sophisticated piece of malware developed by the Israeli tech firm NSO Group works by creating an entirely separate computer inside the memory of an iPhone, allowing attackers to snoop and steal data.

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2021 will be remembered as a significant year for the cyber security industry. With the pandemic accelerating digital transformation, the threat landscape was in constant flux. Major ransomware attacks demonstrated not just their impact on businesses, but wider society too. As we look ahead to 2022, the only constant in our industry is uncertainty in the cyber realm, but here are a few of our predictions for next year, based on trends we’re already seeing emerge.

Ransomware.

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Jamie Metzl is an author specializing in topics of genetic engineering, biotechnology, and geopolitics. Please support this podcast by checking out our sponsors:
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Continue reading “Jamie Metzl: Lab Leak Theory | Lex Fridman Podcast #247” | >

Cybersecurity researchers have demonstrated a new attack technique that makes it possible to leverage a device’s Bluetooth component to directly extract network passwords and manipulate traffic on a Wi-Fi chip, putting billions of electronic devices at risk of stealthy attacks.

The novel attacks work against the so-called “combo chips,” which are specialized chips that are equipped to handle different types of radio wave-based wireless communications, such as Wi-Fi, Bluetooth, and LTE.

“We provide empirical evidence that coexistence, i.e., the coordination of cross-technology wireless transmissions, is an unexplored attack surface,” a group of researchers from the Technical University of Darmstadt’s Secure Mobile Networking Lab and the University of Brescia said in a new paper.

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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.

With their millions and billions of numerical parameters, deep learning models can do many things: detect objects in photos, recognize speech, generate text—and hide malware. Neural networks can embed malicious payloads without triggering anti-malware software, researchers at the University of California, San Diego, and the University of Illinois have found.

Their malware-hiding technique, EvilModel, sheds light on the security concerns of deep learning, which has become a hot topic of discussion in machine learning and cybersecurity conferences. As deep learning becomes ingrained in applications we use every day, the security community needs to think about new ways to protect users against their emerging threats.

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