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Advanced RATs exploit architectural blind spots and native tools to evade detection

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πŸ“° 1 unique sources, 1 articles

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Advanced remote access Trojans (RATs) are increasingly using stealthy techniques to evade detection and exploit architectural blind spots in enterprise security environments. These RATs, such as StilachiRAT and SnowDog RAT, use corrupted DOS and PE headers to hide in plain sight and persist undetected on systems. Attackers are also leveraging simple Windows batch files and PowerShell-based loaders to launch sophisticated payloads, taking advantage of implicit trust in native system tools. This trend highlights a shift towards stealth and obfuscation, making traditional defenses less effective. The use of living-off-the-land (LotL) and in-memory execution techniques, particularly through PowerShell, allows attackers to execute malicious commands directly in memory without writing to disk. This fileless approach significantly reduces the attack footprint, making detection difficult for traditional antivirus solutions. Additionally, attackers are using artificial intelligence (AI) and large language models (LLMs) to generate convincing phishing lures, craft effective social engineering schemes, and automate attack campaigns. This adaptability lowers the barrier to entry for less skilled cybercriminals, accelerating the development of more evasive and resilient threats.

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  1. 18.08.2025 17:00 πŸ“° 1 articles

    Advanced RATs exploit architectural blind spots and native tools

    Recent strains of RATs, such as StilachiRAT and SnowDog RAT, are using corrupted DOS and PE headers to hide and persist undetected on enterprise systems. Attackers are leveraging simple Windows batch files and PowerShell-based loaders to launch sophisticated payloads, taking advantage of implicit trust in native system tools. The use of living-off-the-land (LotL) and in-memory execution techniques, particularly through PowerShell, allows attackers to execute malicious commands directly in memory without writing to disk. This fileless approach significantly reduces the attack footprint, making detection difficult for traditional antivirus solutions. Additionally, attackers are using AI and large language models (LLMs) to generate convincing phishing lures, craft effective social engineering schemes, and automate attack campaigns. This adaptability lowers the barrier to entry for less skilled cybercriminals, accelerating the development of more evasive and resilient threats.

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