{"id":4266,"date":"2026-07-15T18:06:05","date_gmt":"2026-07-15T18:06:05","guid":{"rendered":"https:\/\/relocationobserver.com\/?p=4266"},"modified":"2026-07-15T18:06:05","modified_gmt":"2026-07-15T18:06:05","slug":"suno-hack-shows-how-youtube-music-deezer-and-genius-data-trained-ai-music-generators-models","status":"publish","type":"post","link":"https:\/\/relocationobserver.com\/?p=4266","title":{"rendered":"Suno Hack Shows How YouTube Music, Deezer and Genius Data Trained AI Music Generator\u2019s Models"},"content":{"rendered":"
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\nSuno has long acknowledged that its AI music generator relied on the scraping of millions of songs available across the internet, but a new hack reveals just how the company pulled from streaming services and websites such as YouTube Music, Deezer and Genius to power its product \u2014\u00a0all while user information remained vulnerable.<\/p>\n

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\n\tA report in 404 Media on Wednesday, relying on data a hacker provided to the outlet, showed the instructions in the company\u2019s source code had it scrape files from \u201cgenius_hq, youtube_music, freesound, jamendo, imp, deezer,\u201d with the stock music libraries Freesound, Jamendo and the International Music Score Library Project among the other sources scraped. The instructions demanded that \u201cnon-music\u201d\u00a0be filtered out. The hacker also had access to Suno\u2019s customer list, which included emails, phone numbers and Stripe payment details, according to the report.<\/p>\n

\n\tRepresentatives for Deezer, YouTube Music, Genius, and the stock libraries scraped did not respond to immediate requests for comment.<\/p>\n

\n\tA Suno spokesperson said the hack was \u201cquickly contained\u201d\u00a0after the company learned about it in November 2025 and that it primarily exposed \u201coutdated source code that is no longer in use at Suno.\u201d The spokesperson said no sensitive user information was compromised, as the company does not retain full credit card numbers, and that due to the limited breach, it didn\u2019t feel obligated to notify its user base.<\/p>\n

\n\t\u201cAs we have stated in public filings and disclosures, Suno\u2019s AI models have been trained on publicly available music files and related metadata accessible on third-party websites on the open Internet,\u201d\u00a0the spokesperson said.<\/p>\n

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\n\tOne file focused on YouTube Music noted it took in \u201c2,013,545 music clips\u201d at the time it was last updated, while another file\u2019s comments about Suno\u2019s datasets included \u201c113,879 hours of youtube_music,\u201d \u201c17,615 hours of genius_hq,\u201d \u201c410 hours of free sound,\u201d \u201c19,514 hours of imslp,\u201d \u201c3,726 hours of jamendo,\u201d \u201c62,117 hours of pond5_music,\u201d \u201c12,287 hours of deezer,\u201d \u201c152,162 hours of ytm_tagged\u201d and \u201c103 hours of musescore_lyrics,\u201d\u00a0according to the 404 Media report.<\/p>\n

\n\tThe hack provides a rare glimpse into just how AI music generators train their products, a process that has fueled several copyright infringement lawsuits against the company by record labels, including Universal Music Group and Sony Music Entertainment, and the Recording Industry Association of America, which represents the recording industry, all while earning it derision from several artists. (Warner Music Group settled its lawsuit with Suno last year, and the two are currently developing a new model of the music generator.)<\/p>\n

\n\tSuno has acknowledged in both the filings and its website that its product was trained on \u201cessentially all music files of reasonable quality that are accessible on the open Internet, abiding by paywalls, password protections, and the like, combined with similarly available text descriptions,\u201d\u00a0though it has claimed such use is protected under fair use law and that it has introduced to prevent its users from producing songs similar to those scraped through the training.<\/p>\n

\n\t\u201cOur goal has always been to help people create original new music, not replicate someone else\u2019s. That\u2019s why we build our models around what we call \u2018Original Creation, By Design,\u2019\u201d the spokesperson said in a statement. \u201cFor example, we intentionally do not use artist names as a category of training metadata because we want our models to help people create brand new songs, not music that replicates other artists\u2019 existing work. It\u2019s also why we built Suno with detection filters that block or prevent a user from using specific artist, song, or album names as prompts, and prevent users from uploading lyrics or sound recordings that match existing works.\u201d<\/p>\n

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A new hack reveals just how Suno pulled from streaming services and websites such as YouTube Music, Deezer and Genius to power its product.<\/p>\n","protected":false},"author":1,"featured_media":1269,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33],"tags":[3457,2234,3458],"class_list":["post-4266","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-music","tag-deezer","tag-suno-2","tag-youtube-music"],"yoast_head":"\nSuno Hack Shows How YouTube Music, Deezer and Genius Data Trained AI Music Generator\u2019s Models - Relocation Observer<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/relocationobserver.com\/?p=4266\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Suno Hack Shows How YouTube Music, Deezer and Genius Data Trained AI Music Generator\u2019s Models - 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