  Being on a mushrooming spree since at least 2013, malware can take a large
toll on any system. In a perpetual cat-and-mouse chase with defenders, malware
writers constantly conjure new methods to hide their code so as to evade
detection by security products. In this context, focusing on the MS Windows
platform, this work contributes a comprehensive empirical evaluation regarding
the detection capacity of popular, off-the-shelf antivirus and endpoint
detection and response engines when facing legacy malware obfuscated via more
or less uncommon but publicly known methods. Our experiments exploit a blend of
seven traditional AV evasion techniques in 16 executables built in C++, Go, and
Rust. Furthermore, we conduct an incipient study regarding the ability of the
ChatGPT chatbot in assisting threat actors to produce ready-to-use malware. The
derived results in terms of detection rate are highly unexpected: approximately
half of the 12 tested AV engines were able to detect less than half of the
malware variants, four AVs exactly half of the variants, while only two of the
rest detected all but one of the variants.
