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The Glove Stealer malware employs a novel technique to circumvent Chrome’s App-Bound Encryption, enabling the theft of browser cookies and other sensitive data. This malware is written in .NET and targets browser extensions and locally installed software. The sophistication of this technique highlights the ongoing evolution of malware and the need for robust security measures.

The research community is exploring innovative ways to leverage large language models (LLMs) for cybersecurity purposes. A recent study has demonstrated the potential of LLMs to identify vulnerabilities in real-world code. The study’s findings suggest that LLMs can be trained to detect flaws in software by analyzing vast amounts of code data. This approach represents a promising advancement in automated vulnerability detection, potentially leading to improved software security and reduced exploitation risks. This research indicates the potential of LLMs to play a crucial role in proactive vulnerability identification and mitigation, enhancing the security of software systems.

Security researchers discovered a critical zero-click vulnerability within Synology’s Photos app, impacting millions of users. This vulnerability allows attackers to compromise the system without any user interaction. The flaw resides in a part of the app that doesn’t require authentication, enabling direct exploitation over the internet. Attackers can gain root access and install malicious code, potentially turning the infected device into a botnet for further nefarious activities. Synology has addressed the bug, but users need to manually update their devices. This incident highlights the importance of regularly updating software to mitigate security risks and the growing threat of zero-click vulnerabilities.