In a landmark cybersecurity breakthrough this February, researchers uncovered a new and highly sophisticated malware strain—BypassERWDirectSyscallShellcodeLoader—marking the first documented instance of generative AI being used to both create and analyze malicious code.

This advanced malware, generated using large language models like ChatGPT and DeepSeek, showcases a turning point in cyber warfare. No longer confined to manually written code, cybercriminals are now leveraging AI to produce complex, stealthy threats at scale, posing a fresh challenge for traditional defense systems.

The malicious code came to light through Deep Instinct’s proprietary DIANNA (Deep Instinct Artificial Neural Network Assistant)—an AI-powered detection tool that successfully explained and categorized this AI-born threat. The analysis revealed the malware’s capacity to evade detection while deploying multiple payloads through direct system calls, bypassing standard API monitoring tools.

What sets this malware apart is its modular framework, which allows attackers to tailor payloads for specific objectives. It also employs advanced evasion techniques, including anti-debugging, anti-sandboxing, and Bypass-ETW (Event Tracing for Windows). These features enable it to operate silently, deceiving security tools while maintaining its functionality in infected systems.

Remarkably, DIANNA identified and blocked the malware hours before it surfaced on VirusTotal, where only six security vendors initially flagged it as malicious. This detection gap underscores the limitations of signature-based methods and emphasizes the growing necessity for next-generation AI-driven cybersecurity solutions.

The emergence of BypassERWDirectSyscallShellcodeLoader is a wake-up call: as cybercriminals adopt AI to innovate attacks, defenders must evolve equally fast. AI-assisted tools like DIANNA are no longer just an option—they’re a critical frontline in the escalating battle against intelligent cyber threats.

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News Source: CyberSecurityNews.com