Polyglot files, which conform to multiple file format specifications, present a significant challenge to endpoint detection and response (EDR) systems. These files can respond differently depending on the calling program, making them difficult to classify correctly. As a result, they can bypass traditional malware detection methods that rely on format identification, feature extraction, and signature comparisons.
Research conducted by Oak Ridge National Laboratory and Assured Information Security highlights the effectiveness of polyglots in evading commercial EDR tools. Tests revealed that some vendors had 0% detection rates for malicious polyglots, demonstrating a critical gap in current malware detection strategies. Polyglot files’ ability to blend into various formats makes them particularly effective at avoiding detection.
To address this issue, researchers developed tools like Fazah to simulate real-life polyglot creation and PolyConv, a deep learning model that achieved over 99% accuracy in detecting polyglots. Despite these advancements, existing tools remain less effective compared to methods such as the custom CDR tool ImSan, which showed 100% efficacy in sanitizing image-based polyglots.
The study underscores the need for enhanced detection techniques to combat the advanced threats posed by polyglot files. With threat actors increasingly using polyglots in malware campaigns, improving detection capabilities and developing format-agnostic approaches are crucial for strengthening cybersecurity defenses.
