New Cybersecurity Research Reveals SSD Tracking Vulnerability
Websites Are Spying on Your Solid State Drive

Image: Futurism
Researchers in Austria have discovered a new cyberattack method called FROST, which allows malicious websites to track users' online activities by exploiting solid state drives (SSDs). This method can predict user behavior with high accuracy, raising significant privacy concerns.
- 01FROST (fingerprinting remotely using OPFS-based SSD timing) enables websites to track user activity without interaction.
- 02The technique can predict which websites a user will visit with an accuracy of 88.95% and applications with 95.83% accuracy.
- 03The attack works on Mac and Linux devices, and potentially affects Windows as well.
- 04FROST exploits SSD activity by creating large temporary files that block data movement, allowing tracking.
- 05Users can mitigate risks by closing website tabs promptly after use.
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Recent research from Austria has unveiled a concerning new cyberattack method known as FROST, which stands for 'fingerprinting remotely using OPFS-based SSD timing.' This technique allows malicious websites to monitor users' online activities without requiring any interaction or software installation. By exploiting the behavior of solid state drives (SSDs), FROST creates large temporary files that hinder the normal processing of web data, enabling attackers to analyze timing data and predict user behavior with alarming accuracy. The researchers found that their machine learning model could forecast which websites users would visit with an accuracy of 88.95% and applications with 95.83% accuracy. While the tests were conducted on Mac and Linux systems, the potential for similar attacks on Windows devices exists. To protect against such vulnerabilities, users are advised to close website tabs immediately after use, although this is only a partial solution to the broader issue of online privacy.
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The discovery of FROST poses significant privacy risks for users globally, as it allows for detailed tracking of online behavior.
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