Lost in Translation: AI-based Generator of Cross-Language Sound-squatting

Sound-squatting is a phishing attack that tricks users into accessing malicious resources by exploiting similarities in the pronunciation of words. It is an understudied threat that gains traction with the popularity of smartspeakers and the resurgence of content consumption exclusively via audio, such as podcasts. Defending against sound-squatting is complex, and existing solutions rely on manually curated lists of homophones, which limits the search to a few (and mostly existing) words only. We introduce Sound-squatter, a multi-language AI-based system that generates sound-squatting candidates for proactive defense that covers over 80% of exact homophones and further generating thousands of high-quality approximated homophones. Sound-squatter relies on a state-of-art Transformer Network to learn transliteration. We search for Sound-squatter generated cross-language sound-squatting domains over hundreds of millions of emitted TLS certificates comparing with other types of squatting candidates. Our finding reveals that around 6% of generated sound-squatting candidates have emitted TLS certificates, compared to 8% of other types of squatting candidates. We believe Sound-squatter uncovers the usage of multilingual sound-squatting phenomenon on the Internet and it is a crucial asset for proactive protection against sound-squatting.