Meta leaked scraping list reveals massive content harvesting operation

Meta has systematically scraped content from approximately 6 million unique websites to train its artificial intelligence models, according to internal documents leaked to Drop Site News on August 6, 2025. The leaked list reveals that Meta's data collection operations bypassed common website protection protocols, harvesting content from news organizations, educational platforms, adult content sites, and even revenge porn domains.

The comprehensive scraping operation encompassed roughly 100,000 of the internet's most-trafficked domains. According to the leaked documents, Meta's internal Web Crawler tool repeatedly visited target sites to extract updated content, storing this information on internal servers and databases where it remains permanently archived, even after sites potentially remove the original material.

Whistleblowers frustrated with Meta's political positions shared the data with Drop Site News, describing the company's practices as "unethical and potentially illegal." The leaked list exposes Meta's systematic disregard for standard web protocols designed to prevent automated content extraction.

Technical operations bypass website defenses

The scraped content includes copyrighted material, pirated content, adult videos, and news content from major outlets. Meta's operations specifically targeted mainstream businesses including Getty Images, Shopify, and Shutterstock, alongside extreme pornographic content and material that potentially exploits teenagers.

Meta's scraping bots ignored robots.txt files, which website owners use to communicate crawler restrictions. According to Drop Site News, this represents a deliberate circumvention of website owners' explicit instructions to prevent automated content harvesting.

Rather than directly accessing websites, many addresses on Meta's list belong to Content Delivery Networks (CDNs). These networks cache and store website information to improve performance, providing Meta with access to content that might otherwise be protected through direct site defenses.

Company employees told Drop Site that Meta's scraping bots visit identical sites repeatedly to capture updated information. The internal Web Crawler tool maintains records of all addresses scraped at least once, creating a comprehensive database of harvested content used for AI model training.

Legal challenges mount over training data

The scraping operations occur amid mounting legal challenges against AI companies over unauthorized content usage. Meta faces ongoing lawsuits from authors alleging copyright infringement through the use of their work in AI model training.

According to the leaked documents, AI models require tremendous amounts of data to function effectively. The controversy parallels previous cases involving Clearview AI, which scraped over 3 billion social media images for facial recognition tools before facing invasion of privacy lawsuits.

Stanford University research from 2023 found that the popular Stable Diffusion AI platform had been trained on hundreds of child exploitation images, raising significant ethical questions about data sources and model outputs. David Thiel, a Stanford data scientist who worked on the study, emphasized the recurring transparency problems in AI training data.

"With pretty much any generative AI model across many domains, the lack of transparency about the training data has been a recurring problem," according to Thiel. "In this case, with no other information available, you'd just have to hope that Meta followed decent safety procedures when training the model. I have no idea whether that's true or not."

Competitive pressures drive aggressive data collection

Meta's extensive scraping operations support the company's competition with OpenAI in artificial intelligence development. According to the leaked documents, Meta recently conducted a massive hiring campaign, offering hundreds of millions of dollars in individual bonuses to attract top AI researchers from OpenAI.

The data collection serves Meta's proprietary AI models, which the company announced would receive up to $72 billion in capital expenditures for 2025, focusing on AI development and data center infrastructure. Meta launched its standalone AI application in April 2025, marking a significant expansion of the company's artificial intelligence offerings.

Interestingly, the leaked list reveals that Meta scrapes content from OpenAI to train its own models, despite the competitive relationship between the companies.

The systematic data harvesting extends beyond traditional web content. Meta's operations captured material from educational platforms, niche forums, personal blogs, and various adult content sites, creating a comprehensive dataset spanning diverse content categories.

Industry resistance grows against unauthorized scraping

The revelations coincide with growing industry resistance to unauthorized AI training data collection. Recent analysis reported by PPC Land showed that over 35% of the world's top 1000 websites now block OpenAI's GPTBot web crawler, representing a seven-fold increase from August 2023.

High-profile publishers including The New York Times have engaged in litigation to prevent their content from being used in AI model training. Despite these legal challenges, the leaked list shows The New York Times is notably absent from Meta's scraping targets, suggesting the company avoided some sites that explicitly challenged unauthorized usage.

The legal landscape surrounding web scraping remains unsettled. While a 2019 case between LinkedIn and HiQ Labs generally upheld the legality of scraping publicly available websites, ongoing lawsuits against AI companies challenge specific aspects of this practice.

Ken Mickles from the digital rights advocacy group Fight for the Future characterized Meta's approach as problematic. "You do not want a company like Meta, which has proven to be an absolutely irresponsible force, to be in a position where it is effectively accruing power over the entire internet by scraping it to build its AI models," according to Mickles.

European regulatory pressure intensifies

The leaked scraping list emerges as Meta faces regulatory challenges in Europe over AI training data usage. German courts recently allowed Meta's AI training with public data, but the company has refused to sign the European Union's code of practice for artificial intelligence, citing "legal uncertainty" for AI developers.

In July 2025, Meta announced it would not participate in the EU's voluntary AI guidelines, contrasting with Microsoft's commitment to likely sign the framework. Joel Kaplan, Meta's chief global affairs officer, criticized the code for creating "legal uncertainties for model developers."

The European regulatory environment presents ongoing challenges for Meta's data collection practices. The company announced in April 2025 its intention to begin AI training using EU user data starting May 27, after temporarily halting similar plans in June 2024 following initial legal challenges.

Publishers have increasingly rallied against AI scraping, with over 80 media executives meeting in New York during July 2025 to address what many consider an existential threat to digital publishing. The meeting included representatives from Google and Meta alongside numerous industry leaders confronting AI companies that scrape publisher content without consent or compensation.

Technical details reveal sophisticated operations

The leaked documents provide insight into Meta's technical approach to data collection. The company's scraping operations utilize sophisticated systems that repeatedly visit target websites to capture updated content and maintain comprehensive records of all harvested material.

Meta's internal Web Crawler tool creates detailed logs of all addresses accessed for AI training purposes. According to company employees cited in the leak, scraped data continues to exist on Meta's internal servers and databases indefinitely, regardless of whether source websites subsequently remove or modify the original content.

The content delivery network approach allows Meta to access cached versions of website content, potentially bypassing direct site protections. CDNs typically store multiple copies of website content across distributed servers to improve loading times and performance, creating additional access points for automated harvesting systems.

The systematic nature of Meta's operations suggests significant investment in data collection infrastructure. The company's ability to harvest content from 6 million unique websites indicates substantial technical resources dedicated to AI training data acquisition.

Industry context and competitive implications

Meta's aggressive data collection occurs within a broader industry context where AI companies compete for access to high-quality training data. The artificial intelligence industry has experienced rapid growth, with companies investing billions in model development and infrastructure.

Recent developments show major technology companies pursuing various approaches to content access. Cloudflare launched a pay-per-crawl service in July 2025, allowing content creators to charge AI companies for access rather than blocking crawlers entirely.

The emergence of paid content access models reflects industry recognition that unauthorized scraping creates unsustainable dynamics for content creators. Publishers increasingly seek compensation for their contributions to AI training datasets while maintaining some control over content usage.

Meta's broader AI strategy includes massive infrastructure investments, with plans to spend hundreds of billions of dollars on data centers and AI development. The company's Prometheus and Hyperion data center clusters will deliver over 6 gigawatts of combined capacity, fundamentally reshaping AI infrastructure capabilities.

Corporate governance and ethical concerns

The leaked documents highlight concerns about Meta's corporate governance and ethical practices in AI development. Whistleblowers cited the company's political positions, particularly regarding international conflicts, as motivation for sharing the scraping data.

According to the leaked information, some senior policy officials at Meta who have been involved in content moderation previously worked for foreign government entities, raising questions about potential conflicts of interest in content governance decisions.

The revelation of systematic protocol violations represents a potential corporate governance failure. Meta's decision to bypass robots.txt files and other website protection measures suggests a deliberate policy of ignoring website owners' preferences regarding automated content access.

The ethical implications extend beyond copyright concerns to questions about consent and user privacy. Meta's scraping operations potentially captured personal information shared on public websites without individual knowledge or consent, creating privacy concerns for millions of users.

Timeline

• June 2024: Meta temporarily halts EU AI training data plans following legal challenges
• August 2023: OpenAI introduces GPTBot crawler, initially blocked by 5% of top websites
• 2023: Stanford University study reveals Stable Diffusion trained on child exploitation images
• April 7, 2025: Meta announces policy allowing public user content for AI training
• April 2025: Meta launches standalone AI application
• May 23, 2025: German court rejects injunction against Meta's AI training plans
• May 27, 2025: Meta begins processing EU user data for AI training
• July 2, 2025: Cloudflare launches pay-per-crawl service for AI content access
• July 11, 2025: Meta unveils Creative breakdown for AI-generated ad content
• July 18, 2025: Meta refuses to sign EU AI code of practice
• July 30, 2025: Publishers rally against AI scraping at IAB Tech Lab summit
• August 6, 2025: Drop Site News publishes leaked Meta scraping list
• August 2024: Over 35% of top websites block OpenAI's GPTBot crawler

Key terms Explained

Meta: The technology company formerly known as Facebook that operates social media platforms including Facebook, Instagram, and WhatsApp. Meta has emerged as a major player in artificial intelligence development, investing up to $72 billion in AI infrastructure for 2025. The company's systematic web scraping operations revealed in the leaked documents position it as a significant competitor to OpenAI in the AI market, utilizing harvested content from millions of websites to train proprietary AI models for its standalone AI application launched in April 2025.

AI training data: The vast collections of text, images, and other digital content used to teach artificial intelligence models how to generate responses and perform tasks. According to the leaked documents, AI models require tremendous amounts of data to function effectively, with Meta collecting content from approximately 6 million unique websites. Training data quality and quantity directly impact AI model performance, driving companies to engage in extensive web scraping operations despite legal and ethical concerns about unauthorized content usage.

Web scraping: The automated process of extracting content from websites using specialized software tools, often performed without explicit permission from website owners. Meta's internal Web Crawler tool systematically harvested content from target websites, repeatedly visiting sites to capture updated information and storing this data permanently on internal servers. The practice becomes controversial when it bypasses website protection protocols and ignores robots.txt files that communicate website owners' preferences regarding automated access.

Content Delivery Networks (CDNs): Distributed server systems that cache and store website content across multiple locations to improve loading speeds and performance. According to the leaked documents, many addresses on Meta's scraping list belong to CDNs rather than original websites, allowing Meta to access cached versions of content that might otherwise be protected. CDNs create additional access points for automated harvesting systems, enabling companies to circumvent direct website protections.

Robots.txt: A standard web protocol file that website owners use to communicate instructions to automated crawlers about which parts of their site should or should not be accessed. The leaked documents reveal that Meta's scraping operations ignored robots.txt files, which website owners use to indicate their preferences regarding automated content extraction. While robots.txt serves as a widely-adopted industry standard for communicating crawling preferences, its legal enforceability remains a subject of ongoing debate in courts addressing AI training data collection.

Copyright infringement: The unauthorized use of copyrighted material without permission from the rights holder, a central legal issue in AI training data collection. Meta faces ongoing lawsuits from authors alleging copyright violations through the use of their work in AI model training. The leaked list reveals Meta scraped content from major outlets and platforms including Getty Images, Shutterstock, and news organizations, potentially exposing the company to significant legal liability for unauthorized use of protected content.

OpenAI: Meta's primary competitor in artificial intelligence development, creator of ChatGPT and other popular AI models. The competitive relationship between the companies has intensified as both invest billions in AI development, with Meta conducting massive hiring campaigns to attract OpenAI researchers. Interestingly, the leaked documents show Meta scrapes content from OpenAI to train its own models, highlighting the complex competitive dynamics in the AI industry.

European Union regulations: The comprehensive regulatory framework governing AI development in Europe, including the AI Act and voluntary codes of practice that establish guidelines for responsible AI development. Meta has refused to sign the EU's code of practice for artificial intelligence, citing "legal uncertainty" for developers, while facing ongoing challenges over its data collection practices in European markets where privacy protections are more stringent than other jurisdictions.

Digital publishing industry: The sector encompassing online news organizations, content creators, and media companies that produce the written and visual content often targeted by AI scraping operations. Publishers increasingly view unauthorized AI training data collection as an existential threat, with over 80 media executives meeting in July 2025 to address scraping concerns and seek compensation mechanisms for their content contributions to AI development.

Data collection infrastructure: The technical systems and processes that companies like Meta use to systematically harvest content from across the internet for AI training purposes. Meta's ability to scrape 6 million unique websites indicates substantial investment in specialized crawling systems, data storage capabilities, and processing infrastructure required to manage the massive scale of content acquisition necessary for competitive AI model development.

Summary

Who: Meta, the social media and technology company, conducted systematic web scraping operations. Internal whistleblowers leaked documents to Drop Site News, revealing the extent of unauthorized content collection.

What: Meta scraped content from approximately 6 million unique websites, including 100,000 top-ranked domains, to train artificial intelligence models. The operations bypassed website protection protocols and harvested copyrighted content, news articles, educational materials, and adult content.

When: The leaked documents were published by Drop Site News on August 6, 2025. Meta's scraping operations appear to be ongoing, with the company's Web Crawler tool repeatedly accessing target websites to collect updated content.

Where: The scraping operations targeted websites globally, with particular focus on high-traffic domains and content delivery networks. Meta stores the harvested data on internal servers and databases for AI model training purposes.

Why: Meta conducts extensive data scraping to compete in the artificial intelligence market, particularly against OpenAI. The company requires massive amounts of training data to develop and improve its AI models, supporting its $72 billion capital expenditure commitment for AI development and infrastructure.