AI incident #997 ·
Meta and OpenAI Accused of Using LibGen’s Pirated Books to Train AI Models
What happened
Court records reveal that Meta employees allegedly discussed pirating books to train LLaMA 3, citing cost and speed concerns with licensing. Internal messages suggest Meta accessed LibGen, a repository of over 7.5 million pirated books, with apparent approval from Mark Zuckerberg. Employees allegedly took steps to obscure the dataset’s origins. OpenAI has also been implicated in using LibGen.
Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.
News reports (4)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
- Alleged deployer
- Openai, Meta
- Alleged developer
- Openai, Meta
- Alleged harmed party
- Writers, Publishers, Journalists, Authors, Academic Researchers
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Privacy & Security
- Risk subdomain
- 2.1 Compromise of privacy by obtaining, leaking or correctly inferring sensitive information
- Causal entity
- Human
- Intent
- Intentional
- Timing
- Pre-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 2.1.
- Risks to privacy
"General- purpose AI models or systems can ‘leak’ information about individuals whose data was used in training. For future models trained on sensitive personal data like health or financial data, this may lead to partic...
- Risks to privacy
"General- purpose AI systems can cause or contribute to violations of user privacy. Violations can occur inadvertently during the training or usage of AI systems, for example through unauthorised processing of personal d...
- Privacy Leakage
"Privacy Leakage means the generated content includes sensitive personal information"
- Private Training Data
"As recent LLMs continue to incorporate licensed, created, and publicly available data sources in their corpora, the potential to mix private data in the training corpora is significantly increased. The misused private d...
- Memorization in LLMs
"Memorization in LLMs refers to the capability to recover the training data with contextual prefixes. According to [88]–[90], given a PII entity x, which is memorized by a model F. Using a prompt p could force the model...
- Association in LLMs
"Association in LLMs refers to the capability to associate various pieces of information related to a person. According to [68], [86], given a pair of PII entities (xi , xj ), which is associated by a model F. Using a pr...
- Privacy Leakage
"The model is trained with personal data in the corpus and unintentionally exposing them during the conversation."
- Privacy and regulation violations
"Some of the broken systems discussed above are also very invasive of people’s privacy, controlling, for instance, the length of someone’s last romantic relationship [51]. More recently, ChatGPT was banned in Italy over...
Incidents in the same risk subdomain
- Meta AI Smart Glasses Reportedly Routed Intimate Imagery to Reviewers at Kenyan Contractor Sama Before Meta Ended Contract
- Grok Reportedly Disclosed Adult Performer Siri Dahl's Legal Name and Birthdate, Allegedly Contributing to Doxxing and Harassment
- NPR Host David Greene Alleged Google's NotebookLM Replicated His Voice Without Consent, Prompting Lawsuit
- Border Patrol Agent Allegedly Claimed Facial Recognition Identified Minneapolis ICE Observer and Global Entry Was Reportedly Revoked Three Days Later
- Perplexity AI Reportedly Accused in Federal Lawsuit of Purported Copyright Infringement and False Attribution of Chicago Tribune Content
- Secret Desires AI Platform Reportedly Exposed Nearly Two Million Sensitive Images in Cloud Storage Leak