Futurism86%

Hackers Expose How AI Music App Suno Stole Decades Worth of Copyrighted Music 57%

By Maggie Harrison Dupré83%

7/17/2026, 6:46:32 PM

BS Summary: This article contains 21 faulty reasoning types, including Appeal to Authority, Unattributed Quote, and Framing Effect, with Negativity Bias as the most egregious example at 61.1% saturation with 206 hits. Analysis detected 1,184 faulty-reasoning hits from 337 analyzed words, generating a BS Score of 54.7% and a BS Rank of 57% (8,205 of 18,898 articles). This article is worse (more manipulative) than 56.60% of the article peer group.

A hack revealed in detail how AI music generating app Suno scraped millions of songs, likely including copyrighted ones, from across the web to feed into its AI model, 404 Media reports . 
Suno , which is currently embroiled in multiple ongoing copyright lawsuits, has already admitted in response to legal action that it used “essentially all music files of reasonable quality that are accessible on the open internet” to train its music-generating AI. 
According to 404 , the hack shed light on how that copyrighted music was obtained and funneled into datasets, with Suno pulling music and lyrics from sites including YouTube Music, Genius, and Deezer, in addition to stock music libraries like Jamendo, Freesound, Pond5, and the International Music Score Library Project (IMSLP). 
Source code obtained by the hacker also pointed to the staggering scale at which Suno has swallowed up music from across the web. 
For instance, one file, titled “youtube_music,” contained “2,013,545 music clips,” while another file noted that Suno’s AI datasets held “113,879 hours of youtube_music.” 
Altogether, the file appears to host hundreds of thousands of hours of music. 
The hacker, who goes by the pseudonym ellie.191, also claimed to 404 that they’d also been able to view sensitive Suno user data, including Stripe payment information. 
In a statement, Suno told 404 that “as we have stated in public filings and disclosures, Suno’s AI models have been trained on publicly available music files and related metadata accessible on third-party websites on the open Internet.” 
The company also insisted that its “goal has always been to help people create original new music, not replicate someone else’s.” 
That’s despite tests extensively showing how Suno can easily replicate published music, including the work of well-known artists. 
More on Suno: CEO of Song-Generating AI App Says People “Don’t Enjoy” Making Music With Instruments 
The post Hackers Expose How AI Music App Suno Stole Decades Worth of Copyrighted Music appeared first on Futurism . 
Confirmation Bias
21.4%
Anchoring Bias
12.2%
Availability Heuristic
20.5%
Representativeness Heuristic
0%
Hindsight Bias
0%
Overconfidence Bias
0%
Framing Effect
23.7%
Loss Aversion
0%
Status Quo Bias
0%
Sunk Cost Effect
0%
Optimism Bias
3.9%
Pessimism Bias
0%
Negativity Bias
61.1%
Self-Serving Bias
17.5%
Fundamental Attribution Error
0%
Actor-Observer Bias
0%
In-Group Bias
0%
Out-Group Homogeneity Bias
0%
Halo Effect
0%
Horn Effect
0%
Dunning-Kruger Effect
0%
Recency Bias
4.7%
Primacy Effect
6.8%
Blind-Spot Bias
0%
Ad Hominem
0%
Straw Man
0%
Appeal to Authority
38.6%
False Dilemma
0%
Slippery Slope
0%
Circular Reasoning
0%
Hasty Generalization
19%
Red Herring
4.7%
Bandwagon
0%
Appeal to Emotion
12.2%
Begging the Question
11.3%
Post Hoc (False Cause)
0%
Tu Quoque
6.2%
Burden of Proof
0%
Appeal to Nature
0%
Composition/Division
0%
Anecdotal
8%
No True Scotsman
0%
Ambiguity (Equivocation)
10.7%
Gambler’s Fallacy
0%
Middle Ground
0%
Personal Incredulity
0%
Special Pleading
6.2%
Genetic Fallacy
0%
Unattributed Quote
36.8%
Quote-first Misdirection
0%
Biased Writer Voice
16%
Indoctrination
0%
Politically Left Leaning Bias
0%
Politically Right Leaning Bias
0%
Attempt to Sell a Product or Service
9.8%

337 words analyzed.

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.