Suno's Copyright Filters Easily Bypassed — AI Clones Beyoncé and Black Sabbath Hits

Suno is one of the most popular AI services for music creation and officially claims that it does not allow reproduction of copyright-protected content. Users can upload their own tracks for remixing or overlay original lyrics on AI-generated music. Supposedly, the system should automatically recognize and block third-party songs.

In practice, these filters have proven to be almost useless: anyone with free software can obtain convincing clones of well-known tracks in minutes. Journalists from The Verge conducted an experiment: without special knowledge or paid tools, they generated imitations of hits from several major artists through Suno. The results were alarming — the tracks closely approximated "Freedom" by Beyoncé, "Paranoid" by Black Sabbath, and "Barbie Girl" by Aqua in melody, arrangement, and overall sound.

For most listeners, the difference from the original would hardly be obvious. The problem is not a single filter failure, but a systemic vulnerability. AI models trained on vast arrays of audio data absorb not just genre patterns, but specific melodic progressions, characteristic instrumental textures, and vocal intonations specific to particular performers.

The boundary between "inspired sound" and direct copying becomes vanishingly thin — and this is precisely what turns the situation into a slow-motion legal time bomb. The company itself is already at the center of legal proceedings. In 2024, major record labels filed a lawsuit against Suno through the RIAA (Recording Industry Association of America), accusing the platform of massive copyright infringement in model training.

Suno insists that it generates "new" music, rather than copying originals. However, such field tests cast serious doubt on this claim: if the result is practically indistinguishable from the original, the argument about "novelty" is difficult to defend in court. Suno is not an exception in the market.

Similar competitor Udio has also faced similar lawsuits. The industry as a whole has not yet developed a reliable technical mechanism that would allow a model to "forget" specific protected works without degrading generation quality. This is a fundamental problem of current neural network audio model architecture, not the particular negligence of a specific company.

Users find themselves in a difficult legal position. A person who asks to generate a track "in the style of Metallica" or "similar to Adele" will very likely get a result that violates copyright — without realizing it. According to the terms of service of most platforms, responsibility formally falls on the user, although the platform itself created a tool without adequate safeguards.

For the media industry, what is happening is a clear signal: one cannot rely on AI platform self-regulation in matters of copyright. The filters are either fundamentally flawed at the current level of technology, or intentionally weakened for the sake of generation quality. Real guarantees for copyright holders will have to be achieved through courts, licensing agreements, and legislative regulation.

Until then, AI music services are de facto operating in a legal gray zone — regardless of their stated policies.