Anthropic Says Small AI Models May Soon Be Able to Copy Themselves Across Devices

Anthropic has warned that small AI models could soon gain the ability to copy themselves across internet-connected devices without human help. These models could spread like computer worms by exploiting security flaws in connected devices. These devices could include smart TVs, ovens and other household appliances.

Anthropic published a research report on October 12, 2026. The company said small AI models could gain the ability to replicate themselves across Internet of Things (IoT) devices within months. Such systems could be harder to stop if their copies spread across multiple devices instead of relying on a single server.

However, current small AI models still struggle to complete every step reliably. Anthropic’s research points towards a possible cybersecurity risk. However, there is no proof that an AI worm capable of spreading widely is already active in the real world.

Anthropic Tests Whether AI Models Can Replicate Themselves

Anthropic’s Frontier Red Team tested whether AI models could find vulnerable devices, gain access to them, copy their files, and run on other devices without human help.

The researchers focused on internet-connected devices because many consumer Internet of Things (IoT) products have weak security. If an AI model gains access to these devices, it could use their computing power to run elsewhere.

The team tested AI models with 30 billion parameters or fewer to see whether they could perform the tasks needed for self-replication. Several models were able to complete many of these tasks. However, putting all the steps together into a reliable process remained a challenge.

In one simplified test, Anthropic found that the GLM-5.2 model could replicate itself across a test network. It reached seven or eight of the eight target devices in each of five trials.

However, the test used a simplified security setup. The researchers gave the model access to devices through an Android Debug Bridge (ADB) port that did not require authentication. This made it easier to access the test devices than it would be in a properly secured network.

This means the results do not prove that AI models could spread as easily across properly secured devices in the real world.

Small AI Models Could Run on Smart TVs and Other IoT Devices

Small AI Models Could Run on Smart TVs and Other IoT Devices

Anthropic said AI models that can copy themselves may use the computing power of hacked consumer devices instead of expensive data-centre GPUs. Potential targets include:

  • Smart TVs and Android TV boxes: These devices could be targeted if they have weak security.
  • Smart home appliances: Connected refrigerators, ovens and other appliances could be at risk if attackers find security flaws.
  • Security cameras and video-conferencing devices: Some devices have AI chips that can run models directly.
  • Small-business IoT devices: Poorly secured devices could provide extra computing power.

A large AI model could also be split into smaller parts and run across several devices that work together. Anthropic tested a 26-billion-parameter model across 15 Android TV boxes. The experiment showed that low-cost devices could work together to run a model that would normally need more powerful hardware.

However, this approach may be slow. Anthropic estimated that the replication process could take days, depending on the model and hardware.

AI Worms Could Spread Across Devices Without a Central Server

Traditional computer worms spread by exploiting security flaws in computers and networks. Anthropic warned that AI-based worms could use AI models to find vulnerable devices and spread with little human help.

An AI worm could find a weakly protected device, copy its files to another machine, and repeat the process. This could help it spread across multiple devices. Taking one infected device offline might not stop the spread, as other devices could still run copies of the worm.

Anthropic also warned that these worms could use different AI models to perform different tasks. However, researchers have not shown that AI worms can reliably bypass modern security systems or spread widely in real-world networks.

ALSO READ: AI Cyber Attacks Statistics [2025 and beyond]

Small AI Models Could Replicate Themselves by the End of 2026

Anthropic predicts that small AI models could gain the ability to replicate themselves by the end of 2026 if current progress continues. The company studied open-weight models with 30 billion parameters or fewer and tracked how their capabilities improved over time.

However, this is only an estimate. Anthropic has not confirmed when self-replicating AI will become possible. The smallest models tested could not yet complete all the steps needed for reliable self-replication. They would need to improve further to reach that level.

How Self-Replicating AI Differs From Traditional Malware

Traditional malware usually targets specific devices or security flaws. Self-replicating AI could adapt to different devices and systems.

Anthropic warned that AI models could learn how to run on unfamiliar hardware, making them harder to stop. They could also learn from failed attempts and share successful methods with other copies.

However, researchers have not shown that AI worms can bypass all security protections or reliably spread across different devices. The tests focused on individual steps, not the full process working in real-world conditions.

ALSO READ: ARTEX AI Agent Pulled From GitHub After Cyberattacks Target South Korean Banks

How Can Companies Prevent AI Worms From Spreading?

How Can Companies Prevent AI Worms From Spreading?

Anthropic said companies can reduce the risk by securing connected devices and monitoring their networks.

  • Secure devices: Change default passwords, close unused access points and install security updates.
  • Monitor networks: Watch for unusual connections, large file transfers and suspicious activity.
  • Limit access: Restrict what devices can access to reduce the damage from attacks.
  • Detect malware early: Look for signs of automated scanning and malware spreading across devices.
  • Build stronger security: Manufacturers should include better security protections in their products.

Anthropic also recommended limiting how many new devices an infected machine can reach. If each infected device spreads the threat to fewer than one other device on average, the spread should eventually stop.

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Uber Just Gave ChatGPT to 30,000 Workers After Cutting Thousands of Jobs

Uber is rolling out OpenAI’s ChatGPT Work to more than 30,000 employees worldwide, weeks after announcing plans to cut 3,300 corporate jobs. The tool will help staff across departments such as finance, marketing and operations with their daily tasks.

Announced on October 22, 2026, the rollout expands an earlier trial with 1,000 employees. Staff can use ChatGPT Work to analyze data, prepare presentations, research information and manage tasks across workplace apps.

Uber announced the job cuts in September to simplify its structure and reduce management layers. The company has not linked the layoffs to AI, but the timing raises questions about AI adoption as companies reduce their workforces.

Uber Expands ChatGPT Work to All Employees

Uber first tested ChatGPT Work with 1,000 employees in early 2026. The trial included staff from different departments, such as finance, operations and product.

Sumanth Sukumar, Uber’s vice president of engineering, said around 80% of the employees in the trial were not engineers. This shows that Uber is looking at ways to use AI for everyday business tasks, not just software development.

During the trial, employees used ChatGPT Work to analyze business data, spot trends and suggest improvements. They also used it to adapt training materials for different markets and languages and create interactive dashboards.

Uber has not shared how much it will cost to provide ChatGPT Work to its entire workforce. The company plans to track how employees use the tool, which tasks they use it for and whether it helps them work more efficiently.

ALSO READ: Enterprise Generative AI Statistics 2025-2026

How Uber Employees Can Use ChatGPT Work

How Uber Employees Can Use ChatGPT Work

ChatGPT Work is an AI agent available through ChatGPT Enterprise. It can handle tasks that involve several steps and help employees work across different apps.

The tool can connect with workplace apps such as Google Drive, Gmail, Google Calendar and Slack. This allows employees to gather information and complete tasks without having to move between apps as often. Uber employees can use ChatGPT Work to:

  • Research topics and summarize information.
  • Prepare meeting notes, briefs and presentations.
  • Analyze business data and spot trends.
  • Adapt training materials for different languages and markets.
  • Create interactive dashboards and organize daily tasks.
  • Complete work that requires information from multiple apps.

Uber will also provide employee training, approved app integrations and an internal library of AI skills to support the rollout. Engineers will continue to use OpenAI’s Codex for coding and software development.

The rollout is part of Uber’s plan to bring AI tools to employees across different departments. However, the company has not yet shared figures showing how much time or money ChatGPT Work could save.

Uber Expands ChatGPT Access After Announcing Job Cuts

Uber plans to give ChatGPT Work to more than 30,000 employees. The rollout comes after the company announced plans to cut around 3,300 corporate jobs, or about 10% of its corporate workforce.

In September, CEO Dara Khosrowshahi said the cuts would simplify the company’s structure and reduce management levels. Uber has not linked the layoffs to its latest AI rollout.

However, AI has played a role in other job cuts at Uber. In July 2026, the company cut customer service jobs as it increased its use of AI. Former employees also told Business Insider that AI tools were already helping with tasks such as answering questions in Slack and writing replies to riders and drivers.

Uber has not said that AI caused the September job cuts. The company has also not linked those layoffs to the ChatGPT Work rollout.

The expansion shows that Uber is bringing AI into more areas of its business. However, the company has not shared data on how the tool will affect employee productivity, costs or jobs.

ALSO READ: AI Workforce Impact Statistics – Jobs at Risk as Global Economy Reshapes

How Uber Will Measure AI Productivity and Cost Savings

Uber has not shared how it will measure the success of ChatGPT Work across the company or how much money it expects to save. The company plans to track how employees use the tool, which tasks they complete with it and what results they achieve. This will help Uber decide how to use AI in other parts of its business.

The rollout also supports OpenAI’s efforts to bring its AI tools into more businesses and make them part of daily work.

For now, Uber is giving employees access to ChatGPT Work and allowing them to explore different ways to use it. The results will show whether the tool helps employees save time, reduce costs or change how certain tasks are done.

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UK launches world-first AI Biosecurity Lab with OpenAI and Anthropic backing

The UK government plans to set up a new AI Biosecurity Lab to detect biological threats and speed up the development of vaccines and treatments. The government will provide £100 million, while OpenAI and Anthropic have pledged up to £200 million in AI resources and technical support.

Announced on October 24, 2026, the AI Biosecurity Lab (AIBL) will bring together AI researchers, scientists and government agencies to help the UK detect and respond to biological threats.

The lab is part of the government’s National AI Biosecurity Initiative, which aims to use AI to protect public health and prepare for future biological risks.

UK Allocates £100 Million to AI Biosecurity Lab

The UK government will invest £100 million to set up the AI Biosecurity Lab. OpenAI and Anthropic have also pledged up to £200 million combined, bringing the total potential support to £300 million.

However, most of the companies’ contributions will come in the form of computing power, AI model access and technical support rather than cash. OpenAI will provide £100 million in computing resources and model credits. It will also offer trusted access to relevant advanced AI models through its Rosalind Biodefense programme.

Anthropic will contribute up to £100 million in model credits, access to its AI models and dedicated technical support.

The UK government has also announced partnerships with Google DeepMind and UK-based life sciences company Basecamp Research. Both organisations will support research by sharing AI tools, biological expertise and resources to help address biosecurity risks.

The lab will operate as an independent, non-profit organisation. It will work with government agencies, universities, research institutions and private companies to study how AI can help identify and reduce biological threats.

ALSO READ: U.S. Government and Google Join $1.8 Billion AI Biology Initiative

AI Biosecurity Lab to Focus on Early Threat Detection

The AI Biosecurity Lab will focus on two goals: detecting biological threats early and speeding up the development of vaccines and treatments.

AI can help scientists analyse biological data, identify risks and speed up research. The lab will use these tools to help authorities detect threats and respond faster.

It will work with UK agencies, including the UK Health Security Agency, the Animal and Plant Health Agency and the Defence Science and Technology Laboratory.

The lab will also work with the Medicines and Healthcare products Regulatory Agency to prepare for health emergencies and help regulate medicines developed using AI.

The UK government said the lab will help the country prevent, detect and respond to biological threats, including those linked to advances in AI.

UK Takes Steps to Reduce AI-Related Biosecurity Risks

UK Takes Steps to Reduce AI-Related Biosecurity Risks

Governments and AI companies are increasingly concerned that advanced AI systems could be misused for harmful biological research. AI can help scientists study diseases and develop treatments. However, these systems could also make it easier for people to carry out certain harmful biological activities.

The UK wants to use AI to reduce these risks while supporting medical research. AI Minister Kanishka Narayan said the new lab would bring together scientific experts to protect the public from biological threats and strengthen the UK’s work on AI safety and security.

The lab is part of a wider government plan to invest in AI safety, cybersecurity and UK technology. The government has also announced £65 million to improve biological surveillance and develop medical tools to fight priority pathogens.

ALSO READ: Anthropic Blocks Five Cases of AI Misuse Linked to Biological Weapons

How the UK’s AI Biosecurity Lab Could Improve AI Safety

The new lab aims to use AI to detect biological threats while reducing the risks linked to advanced AI systems. It will bring AI tools and biological research together to help scientists identify threats earlier and develop vaccines and treatments faster.

The lab will partner with OpenAI, Anthropic, Google DeepMind and Basecamp Research. These partnerships will provide access to AI models, computing resources and scientific expertise. However, the lab’s success will depend on how well it turns these resources into practical solutions. 

The government has outlined its main goals, but it has not yet provided a clear timeline or measurable targets for the results. The initiative aims to improve the UK’s response to biological threats and ensure that advances in AI help protect public health.

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Elvis Presley’s Music rights holders sue Anthropic for $41M over 274 songs

Elvis Presley’s music rights holders have sued AI company Anthropic for allegedly using 274 of his songs to train Claude without permission. They are seeking up to $41.1 million in damages.

The lawsuit was filed on October 23, 2026, in a federal court in San Francisco. Authentic Brands Group (ABG), which controls much of Presley’s intellectual property, filed the case with other rights holders.

The plaintiffs claim Anthropic used the songs without licenses or payment. They also allege that Claude can generate content similar to the original songs despite safeguards against copyright infringement.

Anthropic Accused of Training Claude on 274 Elvis Presley Songs

The lawsuit covers 274 songs associated with Elvis Presley, including popular tracks such as “Can’t Help Falling in Love,” “All Shook Up,” “Don’t Be Cruel,” “A Little Less Conversation,” and “Return to Sender.”

The plaintiffs claim that Anthropic copied these songs and used them to train its Claude AI models without permission from the copyright owners. They also allege that Anthropic stored copies of the songs in an internal library to train future versions of its AI models. 

The rights holders argue that the company used their copyrighted work without paying for it. The plaintiffs are seeking up to $150,000 in damages for each song allegedly used without permission. If awarded for all 274 songs, the total could reach $41.1 million.

ALSO READ: Microsoft Insider Warned AI Training Was ‘Largest Theft of Labor’ in History

Copyright Lawsuit Questions Claude’s Ability to Replicate Song Structures

The lawsuit also questions whether Claude can generate songs that closely resemble existing copyrighted music.

The plaintiffs claim that Anthropic continued to produce similar content even after adding safeguards in response to earlier copyright lawsuits. They allege that when users asked Claude to recreate songs for modern listeners, the chatbot generated versions that kept parts of the original songs.

According to the complaint, these versions included similar song structures, hooks, verses, choruses, and bridges, with only small changes to the lyrics.

The plaintiffs argue that this goes beyond taking inspiration from existing music and may amount to using copyrighted songs without permission.

These claims have not yet been proven in court. The case will determine whether Anthropic violated copyright law or whether its use of the material was legally allowed.

Anthropic Responds to Elvis Presley Copyright Lawsuit

Anthropic Responds to Elvis Presley Copyright Lawsuit

Anthropic has denied the allegations and plans to defend itself in court, according to the Los Angeles Times.

In other copyright cases, the company has argued that using copyrighted material to train AI models may qualify as fair use under US law. However, whether this defence applies depends on the details of each case.

The lawsuit is part of a growing number of legal challenges against AI companies over the use of copyrighted material. Musicians, publishers and other creators are questioning whether AI companies should be allowed to use their work without permission or payment.

Anthropic Faces Multiple Lawsuits Over Copyrighted Music

Anthropic is facing several legal challenges over the alleged use of copyrighted song lyrics. The lawsuits claim the company used these lyrics to train its Claude AI models without permission.

In 2023, Universal Music Publishing Group, Concord Music Group and ABKCO Music sued the company over the alleged use of copyrighted lyrics. Other music companies, including BMG Rights Management, Sony Music Publishing and Warner Chappell Music, have also filed copyright claims against Anthropic.

These cases focus on whether AI companies can use copyrighted songs and lyrics to train their models without getting permission or licenses from the rights holders.

The Elvis Presley lawsuit adds another case to these ongoing legal disputes. It focuses on songs linked to a single artist and the companies that hold the rights to his music.

The outcome could affect future copyright cases involving AI training and creative works. However, the claims against Anthropic remain unresolved, and the court has yet to decide whether the company violated copyright law.

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Deepfake Nude Incidents in Schools: 2026 Tracker

Deepfake nude incidents in schools are becoming a growing concern worldwide. There have been reports of students using AI nudification tools to turn photographs of female classmates into fake sexual images and videos without their consent. When these images are shared on social media or messaging apps, students may face bullying, humiliation, and emotional distress.

A joint investigation by WIRED and Indicator documented 88 school-related deepfake sexual abuse cases across 28 countries, affecting more than 640 children since 2023. This 2026 tracker of deepfake nude incidents in schools examines reported cases, affected regions, examples from different countries, and how schools and authorities are responding. 

In this article, we are going to take a look at reported deepfake nude incidents in schools, the number of students affected, cases from different countries, and more. 

How Many Deepfake Nude Incidents Have Been Reported in Schools in 2026?

A joint investigation by WIRED and Indicator, published in April 2026, found 88 cases of deepfake sexual abuse linked to schools across 28 countries, affecting more than 640 children. However, these figures include cases reported since 2023 and do not show how many incidents happened in 2026 alone.

The investigation found that students, often teenage boys, were accused of using AI nudification tools to create fake nude images of their classmates. These images were then shared on social media and messaging apps, causing distress, embarrassment, and humiliation for the victims. 

However, the mentioned cases cover incidents documented since 2023, not just 2026. The investigation does not confirm how many cases occurred in 2026 specifically. The actual number could be higher because some incidents are handled privately by schools or law enforcement agencies and are never reported publicly.

ALSO READ: AI Nude Apps Are Creating a Deepfake Crisis in Schools

Reported Deepfake Nude Cases in Schools by Region

Reported school deepfake cases have been documented across several regions, highlighting the global spread of AI-generated nude images and sexual abuse targeting students. 

According to a joint investigation by WIRED and Indicator, 88 school-related deepfake sexual abuse incidents were documented across 28 countries, affecting more than 640 children since 2023. North America recorded nearly 30 reported cases, followed by more than 20 in Europe and over 10 in South America. Australia and East Asia together accounted for around 12 cases.

RegionReported Cases Since 2023
North AmericaNearly 30
Europe20+
South America10+
Australia and East Asia (Combined)Around 12
Global88 (documented incidents)

Source: WIRED

Deepfake Incidents in Schools Across Different Countries

The school deepfake incident tracker covers reported cases of AI-generated nude images involving students in different countries, including the United States, South Korea, and Australia. These cases show how AI tools can be used to create fake nude images of classmates without their consent. 

Country or RegionReported IncidentNumber of Victims
Pennsylvania, United StatesTwo students created AI-generated sexual images and videos of girlsAround 60 girls
Iowa, United StatesA student was targeted with fake explicit imagesNot specified
New Jersey, United StatesA teenager pursued legal action against a nudification service over fake sexual imagesOne identified teenage claimant, the total number of victims is not specified
Multiple schools in North AmericaStudents were reportedly targeted with deepfake sexual images across different schoolsNot specified
Oregon, United StatesTeachers were targeted with manipulated images shared through a social media accountMultiple teachers, exact number not specified

Pennsylvania Students Face Consequences for AI-Generated Nude Images

In Pennsylvania, two students admitted guilt in juvenile court after creating AI-generated sexual images and videos of girls. According to the WIRED and Indicator investigation, around 60 girls were identified as victims in the case. 

In March, the students were sentenced to 60 hours of community service on felony charges related to child sexual abuse material. The case shows how creating fake sexual images of classmates can lead to legal consequences, even when the images are generated using AI.

Iowa Student Speaks Out About Fake Explicit Images

In Iowa, a student was targeted with fake explicit images that raised concerns about how classmates might view her. The victim spoke about the fear that other students would associate her with the images. 

The supplied investigation does not identify the school or provide the exact date of the incident. It also does not specify whether anyone faced disciplinary action or criminal charges in this case.

New Jersey Teen Takes Legal Action Against a Nudification Service

In New Jersey, a teenage girl pursued legal action against a nudification service over the creation of fake sexual images. Her legal representatives described the emotional distress caused by the images and the fear that they could spread online and remain accessible for years. 

The investigation does not provide a confirmed total number of victims in this case. The legal action highlights how victims and their families are using the courts to challenge services that enable the creation of nonconsensual sexual images.

North America Reports Nearly 30 School Deepfake Abuse Cases

The WIRED and Indicator investigation documented cases in which students at multiple schools were allegedly targeted with AI-generated sexual images. Across North America, nearly 30 school-related deepfake sexual abuse cases had been publicly reported since 2023. 

Some cases involved dozens of alleged victims, while others affected students across more than one school. Responses varied, with some incidents leading to legal action and others raising concerns about delays in reporting or disciplinary measures. The investigation does not provide individual details for every case in this regional group.

South Korean Schools Change Yearbook Photo Policies Over Deepfake Risks

In South Korea, some schools changed their yearbook photo policies because of concerns that student photographs could be taken and used to create fake sexual images. According to the investigation, some schools gave students the option to leave their pictures out of yearbooks.

These changes were intended to reduce the risk of photographs being misused. The supplied report does not identify the schools involved or provide a confirmed number of students affected by these policy changes.

Australian School Limits Student Photos to Prevent Deepfake Misuse

An Australian school changed how it planned to display student photographs online after concerns about AI-generated deepfakes. The school said it would use alternatives such as silhouettes, side profiles, distant group photographs, and approved stock images. 

These measures aimed to reduce the risk of publicly available student photos being taken and altered using AI tools. The supplied investigation does not identify the school or link this specific policy change to a confirmed individual victim or legal case.

ALSO READ: Parents Warned That Ordinary Photos Can Be Turned Into AI Sexual Abuse Images

Oregon Teachers Protest Manipulated Images Shared Online

In Oregon, a school reportedly hired substitute teachers after regular staff called in sick to protest a social media account sharing manipulated images of teachers. The images placed staff members in false and humiliating situations. 

The report illustrates that deepfake-related abuse in schools can affect teachers as well as students. However, the supplied article does not establish that the images in this case were nude deepfakes, so the incident should be treated separately from cases involving AI-generated sexual images of students.

How Schools and Authorities Are Responding to Deepfake Incidents

Schools and authorities are taking different steps to deal with deepfake nude incidents involving students. Some schools have changed their photo policies to prevent the misuse of student pictures, while others have faced criticism for delays in reporting cases or taking action against those accused of creating fake images. Responses include school discipline, police investigations, legal action, and programmes to teach students about the risks of AI-generated sexual images.

1. Schools Are Changing Student Photo Policies

Some schools have restricted the use of student photographs on public websites and social media. Schools in South Korea and Australia have reportedly changed how they use yearbook pictures and online photos because of concerns that these images could be used to create fake nude pictures. Such steps aim to reduce the risk of photo misuse, although they cannot prevent every form of deepfake abuse.

2. Students May Face Disciplinary Action

Students accused of creating or sharing AI-generated nude images of classmates may face school disciplinary action. Depending on the circumstances, this can include suspension or other penalties. 

Schools also need to consider the safety and well-being of victims when deciding how to handle a case. The response can vary depending on the school’s policies, the evidence available, and the laws that apply.

3. Police Are Investigating Deepfake Abuse Cases

Some school deepfake incidents have led to police investigations and criminal charges. Cases involving sexual images of minors may fall under laws related to child sexual abuse material, depending on the jurisdiction and the circumstances. 

In the Pennsylvania case described by WIRED and Indicator, two students admitted guilt in juvenile court after creating AI-generated sexual images and videos of girls. The case shows that using AI to create fake images does not necessarily protect those responsible from legal consequences.

4. Victims and Families Are Taking Legal Action

Some victims and their families have turned to the courts to challenge services that allow users to create fake nude images of other people. These legal actions aim to address the harm caused by nonconsensual images and the role of nudification tools in their creation. 

However, legal options and possible outcomes differ across countries, and taking action against a service does not automatically remove every copy of an image already shared online.

5. Schools Are Increasing Student Awareness

Schools can also help prevent deepfake abuse by teaching students about consent, online safety, and the consequences of creating or sharing sexual images without permission. 

Students should know how to report an incident and where to seek help if they are targeted. Training for teachers and school administrators can also help them respond quickly, protect victims, preserve relevant evidence, and avoid actions that could cause further distress.

The US Take It Down Act Sets Rules for Removing Deepfake Images

The US Take It Down Act addresses the sharing of intimate images without a person’s consent, including certain AI-generated or digitally altered images. It requires covered online platforms to offer a way to report this content and remove qualifying images within 48 hours of a valid request. The law also addresses related criminal conduct. However, its requirements apply within the law’s scope and do not cover every website worldwide.

Removing harmful images is only one part of tackling deepfake abuse in schools. Schools also need clear ways for students to report incidents, prompt investigations, and support for those affected.

Teaching students about the harm caused by creating or sharing fake sexual images can help prevent further incidents. Schools, families, law enforcement agencies, and online platforms must also work together to limit the spread of these images and protect victims.

ALSO READ: AI Porn Statistics (2025-2030): Key Data, Trends & Facts

How Many School Deepfake Incidents Go Unreported?

How Many School Deepfake Incidents Go Unreported?

The exact number of school deepfake incidents that go unreported is unknown. A joint investigation by WIRED and Indicator documented 88 school-related deepfake sexual abuse cases across 28 countries, affecting more than 640 children. However, the investigation covered publicly reported cases dating back to 2023 and does not represent the full scale of the problem.

Some incidents may never appear in news reports because schools and families choose to handle them privately or report them directly to law enforcement. In other cases, reports may lack details about the school, the number of students affected, or the actions taken by authorities. Differences in language and reporting practices also make it harder to track cases across countries.

As a result, the number of publicly documented incidents may be lower than the actual number of cases. There is not enough evidence to estimate how many school deepfake incidents go unreported or how many occurred specifically in 2026. The documented cases should therefore be treated as a record of reported incidents since 2023, rather than a complete count of school deepfake abuse worldwide.

How Students Can Report Deepfake Nude Images

Students who discover that someone has created or shared fake nude images of them should seek help as soon as possible. These images can lead to bullying, embarrassment, and emotional distress, but the person targeted is not responsible for someone else’s decision to create or share them. Students, parents, and teachers can take several steps to report the abuse and help limit the spread of the images.

  • Report the content to the platform: Use the website or app’s reporting option to report fake nude images, sexual harassment, or intimate images shared without consent. Report the content on every platform where it appears.
  • Save details of the post: Keep a record of the post’s URL, username, account name, date, and any threatening messages. Do not download or forward sexual images, especially those involving minors.
  • Tell a trusted adult: Students should speak to a parent, teacher, school counselor, or another trusted adult who can help them report the incident and get support.
  • Inform the school: Contact the school’s administration or safeguarding lead. They can investigate the incident, protect the affected student, and take steps to stop bullying or harassment.
  • Contact the police if necessary: Report incidents involving threats, blackmail, repeated harassment, or sexual images of minors to the appropriate local authorities.
  • Use image removal tools: The Take It Down tool can help prevent eligible intimate images taken when someone was under 18 from spreading on participating platforms. However, it cannot guarantee that images will be removed from every website.
  • Do not share the images further: Avoid reposting, forwarding, or circulating the images, even to show evidence of what happened. Instead, share relevant links and account details with trusted adults, platforms, or investigators.

ALSO READ: More Children Are Finding AI-Generated Nude Images of Themselves Online

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Which Platforms Send the Most Traffic to Nudify Sites?

Nudify websites use search engines, social media platforms and online communities to attract visitors. According to the Institute for Strategic Dialogue (ISD), YouTube and X were the largest social media sources of traffic to the top 10 nudify websites between December 2025 and February 2026, followed by Reddit and Facebook. 

However, social media is only one part of the picture. Direct visits, search results, referral links, advertisements and Telegram bots also help users discover these services.

In this article, we are going to examine which platforms send the most traffic to nudify sites, how these websites attract visitors and why relying on multiple traffic sources can make them harder to disrupt.

ALSO READ: AI Porn Statistics (2025-2030): Key Data, Trends & Facts

How Nudify Websites Attract Users Online

Nudify websites attract users through search engines, social media platforms, online communities, advertisements and referral links. According to the Institute for Strategic Dialogue (ISD), these services appear in search results and are promoted through videos, social media posts and paid advertisements. 

Some websites also offer free trials, low-cost image generation and affiliate programmes to attract new users. Telegram bots and online forums provide additional ways to discover nudification tools and share links to related services. These methods help nudify websites reach wider audiences and maintain traffic even when individual websites, accounts or promotional posts are removed.

Which Platforms Send the Most Traffic to Nudify Sites?

Which Platforms Send the Most Traffic to Nudify Sites?

YouTube and X were the largest social media sources of traffic to the top 10 Nudify websites between December 2025 and February 2026, according to the Institute for Strategic Dialogue (ISD). YouTube generated 1.82 million visits, accounting for 31.48% of the 5.789 million social media visits recorded during the period. X followed with 1.3 million visits, or 22.51%.

Reddit ranked third with 691,000 visits, representing 11.94% of the total. Facebook contributed 464,000 visits (8.02%), while Instagram generated 391,000 visits (6.76%).

Together, YouTube and X accounted for nearly 54% of the social media traffic measured in the study. The figures show how mainstream platforms can serve as routes to websites that offer tools for creating sexualized images of real people without their consent.

Social Media Traffic to the Top 10 Nudify Websites

The following table ranks the social media platforms by the number of visits attributed to them in the reported traffic data.

RankSocial Media PlatformVisitsTotal Share
1YouTube1.82 million31.48%
2X1.3 million22.51%
3Reddit691,00011.94%
4Facebook464,0008.02%
5Instagram391,0006.76%
6Telegram247,8004.28%
7WhatsApp108,6001.88%
8Discord47,3350.82%
9VK14,5830.25%
10TikTok3,8120.06%

Source: Institute for Strategic Dialogue (ISD)

YouTube Was the Largest Source of Traffic to Nudify Sites

YouTube generated 1.82 million visits to nudify websites, accounting for 31.48% of the reported social media traffic. ISD also identified videos and tutorials that could help users discover nudification tools.

This has raise questions about how platforms moderate content that promotes services linked to nonconsensual image manipulation. However, the traffic data does not show how many visits came specifically from tutorials or promotional videos.

X Sent 1.3 Million Visits to Nudify Websites

X generated approximately 1.3 million visits to nudify websites, making it the second-largest referral source. ISD separately identified more than 5,745 direct links to commercial nudify platforms in its monitoring sample, along with 179 Telegram links in expanded URLs.

The report also identified coordinated promotional activity, including five accounts linked to one website that published more than 1,131 promotional posts between December 2025 and March 2026. These link and post counts are separate from the website traffic figures.

Reddit Generated 691,000 Visits

Reddit accounted for approximately 691,000 visits to nudify websites, ranking third in the traffic analysis. ISD’s wider research examined discussions about nonconsensual intimate imagery, AI models and image-manipulation tools.

These discussions can help users discover technologies and external services. However, the findings concern specific discussions and links, not Reddit as a whole.

Facebook Sent 464,000 Visits to Nudify Sites

Facebook generated approximately 464,000 visits to nudify websites. Separately, ISD found hundreds of advertisements promoting nudify tools across Facebook and Instagram in its analysis of Meta’s Ad Library. More than 600 identical ads were posted between April and July 2025.

This has raise questions about how effectively Meta identifies and removes advertisements promoting services associated with nonconsensual image manipulation. The advertising research covers a different period from the traffic analysis.

Instagram Generated 391,000 Visits

Instagram accounted for approximately 391,000 visits to nudify websites, placing it fifth among the listed platforms. ISD also identified advertisements promoting nudification tools on Instagram.

This shows that Instagram’s role extends beyond referral traffic. Advertisements and promotional accounts can expose users to these services, although the data does not establish how many visits resulted from advertising.

ALSO READ: Meta’s Ad System Helped Promote an AI App That Could Nudify Female Politicians

Telegram Connected Users With Nudification Tools

Telegram generated approximately 247,800 visits to nudify websites, ranking sixth in the traffic comparison. ISD’s wider research identified at least 36 dedicated nudification bots and public channels used to distribute generated content.

Two channels identified in the report had around 74,000 members each. The research also documented links between bots, open-source AI tools and other resources.

Telegram therefore serves as more than a referral source in this ecosystem. Bots can provide access to image-generation tools, while channels distribute content and links connect users with external services. These activities should be distinguished from website traffic alone.

Top Traffic Sources for Nudify Websites

Nudify websites attract visitors through direct visits, search engines, referral links, display ads and social media. According to the Institute for Strategic Dialogue (ISD), users can discover these services through Google searches, online posts, advertisements and links shared across websites and messaging platforms.

The report examined traffic to the most-visited nudify websites and found that direct visits and organic search were major sources of traffic. Other routes included referrals from external websites and display advertising.

SourceVisitsTotal Share
Direct84.5 million52.5%
Organic Search40.3 million25.03%
Paid1,4060.00087%
Referrals25.97 million16.13%
Display4.39 million3.59%
Social10,396 0.0067%

Source: Institute for Strategic Dialogue (ISD)

Search Engines Help Users Find Nudify Websites

Search engines are one way users discover nudify websites. ISD found that organic search accounted for around 25% of traffic to the 10 most-visited websites in its analysis.

The report also found links to nudification services on the first page of Google, Bing and Yahoo results for some related searches. Articles listing nudify apps and Telegram bots provided additional ways for users to find these tools.

This means users can discover nudify websites through regular web searches without joining private groups or specialist forums. Search results can also help these services remain visible when individual websites or promotional accounts are removed.

Social Media Drives Traffic to Nudify Sites

Social media posts, videos, comments and advertisements can direct users to nudify websites. ISD’s separate analysis of social media referrals found that YouTube generated around 1.82 million visits, followed by X with 1.3 million, Reddit with 691,000 and Facebook with 464,000.

Some posts link directly to external services, while others promote image manipulation tools or encourage users to try them. ISD also identified more than 600 identical advertisements promoting nudify tools to UK users across Facebook and Instagram between April and July 2025.

Free Trials and Low Prices Attract Users

Some nudify websites offer free features or trial credits before charging for additional image generations and subscriptions. ISD reported that certain services offered image generation for as little as $1 per image.

Low prices can make these tools easier to try. Users may start with free features and pay for more generations or other options later. Some operators also use affiliate and referral programmes to attract visitors. ISD found that at least 12 of the 181 websites it examined had dedicated affiliate or referral pages in their sitemaps.

One website claimed to have paid more than $2.6 million in affiliate commissions and offered revenue shares of up to 40%. This was a claim made by the website, not an independently verified payment total.

These programmes give third parties a financial reason to promote nudify services and send new visitors to their websites.

Telegram Bots and Online Communities Spread Access

Telegram bots and online communities provide additional ways to find nudification tools. ISD identified at least 36 dedicated nudification bots, along with public channels used to share generated content and links to other services.

Users can also find these bots through Telegram searches, search engines and articles listing available tools. Reddit and other online communities host discussions about AI models, image manipulation and related services.

This means access does not depend on a single website. If a bot or service is removed, users may still find similar tools or replacement links elsewhere.

Direct Visits and Referrals Bring Users Back

Direct visits accounted for the largest share of traffic to the 10 most-visited nudify websites in ISD’s analysis. Organic search ranked second, followed by referrals from external websites.

Direct traffic can include visitors who enter a website address themselves or return to a site they have used before. Referral traffic comes from links on other websites or online services.

These sources suggest that Nudify websites can continue attracting visitors after their initial discovery. Repeat visits and links shared across different platforms can help maintain traffic even when individual promotional posts or websites are removed.

Why Nudify Websites Rely on Multiple Traffic Sources?

Why Nudify Websites Rely on Multiple Traffic Sources?

Nudify websites attract visitors through direct visits, search engines, referral links, social media and messaging platforms. Using several traffic sources allows these websites to reach users across different parts of the internet rather than depend on a single platform.

  • Direct Visits Bring Back Existing Users: People who already know a nudify website may visit it by typing its web address or opening a saved link. This helps websites get repeat visitors without relying on search engines or social media. Regular direct visits can also show that users are familiar with the website.
  • Search Engines Help Users Find Nudify Websites: Search engines help people find nudify websites when they search for AI undressing tools or image editing services. Websites that appear in relevant search results can attract new visitors. This allows them to reach people who are already looking for these tools.
  • Referral Links Bring Visitors From Other Websites: Referral links direct users to Nudify websites from other sites, blogs, forums and online communities. People may find these links while reading discussions about AI image generators or related services. These referrals help websites attract visitors beyond search engine results.
  • Social Media Helps Nudify Websites Reach More People: Social media posts and shared links can introduce nudify websites to new users. People may discover these sites while browsing discussions about AI tools and image editing. However, the reach of such content depends on each platform’s rules and moderation policies.
  • Messaging Platforms Help Users Share Website Links: Users may share nudify website links through private messages, group chats and online communities. This can help a website reach people who might not find it through search engines. However, traffic from private messages can be difficult to track because analytics tools may not identify its original source.
  • Multiple Traffic Sources Help Maintain Website Traffic: Nudify websites that rely on only one traffic source may lose visitors if that channel becomes less effective. For example, a drop in search rankings or tighter social media restrictions can reduce their reach. Traffic from other sources can help these websites continue attracting visitors despite such changes.
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The Fight Over AI Nude Images Could Define Who Owns Your Digital Likeness

The growing use of AI nudification tools has made it easier to create fake nude images of real people without their consent. A photo posted on social media can be turned into a sexual image, even if the person has never posed for one. These images can spread quickly online, making it difficult for victims to protect their privacy and prevent further sharing.

The rise of AI deepfake tools has brought a difficult issue into focus. People need greater control over their digital likenesses and legal protection when someone uses them without permission.

Governments are beginning to take action against AI-generated nude images, but passing laws is only part of the challenge. Some of these laws are facing legal challenges over free speech and the limits of government authority. The dispute between Elon Musk’s xAI and Minnesota is one such case.

The outcome could influence how future laws protect people from deepfake pornography while respecting freedom of expression. It also raises questions about whether current privacy and image rights laws are strong enough to protect people whose faces and likenesses are used without their permission.

The xAI Minnesota Case Puts AI Identity Rights to the Test

Minnesota passed House File 1606 in May 2026. The law restricts websites, apps and other services from allowing users to create AI-generated nude images of real people without their consent. The law took effect on August 1, 2026.

xAI challenged the law in federal court, arguing that it violates the First Amendment, which protects freedom of speech in the United States. The company also raised concerns that the law could restrict a broad range of AI-generated images, including content created without any intention to cause harm.

In September, a federal district court refused to block the law. But on October 2, the US Court of Appeals for the Eighth Circuit temporarily stopped its enforcement while the legal challenge continues. The case remains unresolved, leaving the law’s future uncertain.

The case highlights the challenge of protecting people from AI-generated sexual images made without their consent while also respecting freedom of speech.

People should have legal protection against the unauthorised use of their faces and bodies in sexual images. At the same time, laws restricting AI-generated content need clear boundaries and must comply with constitutional protections.

Both issues deserve serious consideration. However, the growing ability to create realistic nonconsensual intimate images raises questions about whether existing laws offer enough protection to victims. The outcome of the xAI Minnesota lawsuit could help shape how courts handle these cases and how far governments can go to protect digital likeness rights.

ALSO READ: xAI Appeals Minnesota’s First-of-Its-Kind AI Nudification Ban

Consent Should Matter More Than the Technology Used

One of the biggest concerns about AI-generated nude images is that they can be created without the person’s knowledge or consent. Someone can take an ordinary photo from social media and use AI to turn it into a fake sexual image.

Unlike a real intimate photograph, deepfake pornography can show a person in a situation they were never part of. The image may look realistic enough for others to believe it is genuine, even when it is completely fabricated.

The damage can still be real. These images can harm someone’s reputation, affect personal relationships, lead to online harassment or be used to threaten and control them. Whether an image is real or AI-generated does not change the fact that it can cause serious distress.

Sharing a normal photograph should never be treated as permission to turn it into sexual content. Having a public social media account does not mean someone has agreed to let others use their face or body to create nude images.

This is why AI identity rights need to focus on consent, not just the technology used to create an image. People should have legal protection against the nonconsensual use of their likeness in sexual content, even when the images are entirely artificial.

At the same time, deepfake pornography laws need to account for other uses of AI-generated images. Satire, art, fictional characters and reporting on matters of public interest may raise different legal questions. Laws that ignore these differences could restrict legitimate expression or leave creators unsure about what is allowed.

The goal should be to protect people from the misuse of their likeness without treating every AI-generated image as illegal. Stronger digital likeness rights should give victims a way to seek help while allowing legitimate creative and public-interest uses of synthetic media.

Free Speech and Consent Are at the Centre of the AI Deepfake Debate

Free Speech and Consent Are at the Centre of the AI Deepfake Debate

Freedom of speech is a key issue in the Minnesota case. AI-generated images can be used for many purposes, including art, satire and other forms of expression. A law that limits how people use AI image-generation tools could affect these activities as well as the creation of harmful content.

In its lawsuit, xAI argued that Minnesota’s law is too broad and could hold companies responsible for content protected by the US Constitution. Minnesota, on the other hand, has argued that the law is needed to address the harm caused by AI nudification tools.

The question is not simply whether deepfake pornography can hurt people. Courts must also decide whether the law protects victims without placing unnecessary limits on free speech.

This balance matters for future AI identity rights laws. If a law covers too much, courts could restrict its reach or strike it down. But if it does not go far enough, victims may have little protection against tools that make it easy to create nonconsensual intimate images.

On October 2, the US Court of Appeals for the Eighth Circuit temporarily blocked enforcement of Minnesota’s law while xAI’s legal challenge continues. This does not mean the court has made a final decision on whether the law is constitutional. That question remains unresolved.

The case could influence how courts assess future deepfake pornography laws and decide how far governments can go to prevent the misuse of people’s faces and bodies. The Reuters report on the appeals court decision provides more details on the ruling and the ongoing legal dispute.

AI Companies Should Take Responsibility for How Their Tools Are Used

The debate over AI identity rights is also about the responsibility of companies that build image-generation tools. These companies decide how their products work and what limits they place on users. They can take steps to stop people from creating sexual images of others without their consent.

For example, companies can block prompts that request explicit images of real people, improve systems that identify abusive requests and give users a simple way to report harmful content. They can also help victims request the removal of AI-generated nude images shared without their permission.

However, having safety rules is not enough. Companies need to make sure those rules work in practice. A policy against nonconsensual intimate images means little if users can easily get around it.

This does not mean AI companies should be held responsible for every image a user creates. Their legal responsibility should depend on factors such as the risks they could reasonably prevent, the safeguards they have in place and how they respond when abuse is reported.

Still, companies should not be able to ignore the harmful uses of their products. If an AI tool makes it easy to create deepfake pornography, its developers should take reasonable steps to reduce that risk.

Governments should examine whether AI platforms have effective safety measures, clear reporting systems and a proper process for handling complaints. They should also consider what legal options victims have when companies fail to meet their obligations.

These steps could help protect people from the misuse of their likenesses without relying only on broad bans that may raise concerns about freedom of speech. Effective deepfake pornography laws should consider both the responsibility of AI companies and the rights of the people affected by their tools.

The Growing Need for Stronger Digital Likeness Laws

The Minnesota case shows a growing problem with existing laws. Many rules covering privacy, identity and sexual abuse were written before AI tools could create realistic images of people in situations that never happened.

Legal protection also varies from one place to another. Some laws deal with nonconsensual intimate images, while others cover impersonation, privacy violations or the unauthorised commercial use of someone’s face. These laws do not cover every situation in the same way. The protection a victim receives may depend on where they live and where the images are created or shared.

AI-generated nude images create another challenge. Unlike stolen or leaked intimate photographs, these images may be made entirely by AI. There may be no original nude photograph to prove that someone took or shared it without permission. Yet the person shown in the image can still face harassment, threats and damage to their reputation.

Clearer digital likeness rights could help address this problem. People need practical ways to report abuse, request the removal of harmful images and take legal action when their likeness is used without consent, particularly in sexual content.

However, protecting someone’s likeness should not mean giving them control over every image or reference to their face. Such broad restrictions could affect journalism, political criticism, satire and other forms of lawful expression.

Instead, lawmakers should focus on specific forms of harm, such as deepfake pornography, deliberate impersonation and the use of someone’s identity to threaten, exploit or harass them.

The goal of stronger AI identity rights should be to protect people from abuse while respecting freedom of speech. As AI image-generation tools become more capable, laws need to address the harm these tools can cause without placing unnecessary limits on legitimate expression.

ALSO READ: Your Face and Voice May Become the Next Form of Digital Property

The Minnesota Case Could Set the Direction for AI Regulation

The Minnesota Case Could Set the Direction for AI Regulation

The outcome of the xAI Minnesota lawsuit could influence how other governments regulate AI-generated nude images and deepfake pornography. If the court upholds Minnesota’s law, it could encourage other states to introduce similar rules. However, each government would still need to consider its own laws and constitutional protections.

If the law is struck down, lawmakers may need to write narrower rules that focus on specific forms of abuse while protecting freedom of speech. Either outcome could shape future debates about AI identity rights and the legal use of people’s likenesses.

The case also shows why governments need clear laws that keep up with changes in AI technology. Legislation should explain what conduct is prohibited, which companies and services must follow the rules, and what penalties apply when those rules are broken. Victims should also be able to report abuse, request the removal of harmful content and seek legal help without facing unnecessary costs or delays.

AI companies should not wait for courts to settle every legal question before improving their safety measures. They can take steps now to prevent the creation of nonconsensual intimate images, respond to complaints and make it easier for victims to seek help.

The issue also goes beyond deepfake pornography laws. AI tools can copy a person’s voice, create fake videos and make someone appear to say or do something that never happened. These uses raise wider questions about consent, privacy and control over personal identity.

As these tools become more capable, digital likeness rights will become an increasingly important part of AI regulation. The challenge for lawmakers is to protect people from misuse without placing unnecessary limits on legitimate expression.

The Right to Control Your Digital Likeness Should Not Depend on AI Progress

The rise of AI-generated deepfake pornography has forced courts to consider how to protect people’s rights while respecting freedom of speech. Minnesota’s law and the xAI lawsuit show how difficult it can be to find the right balance when governments try to regulate new AI tools.

The answer is not to ban all AI-generated images. But freedom of speech should not be used to dismiss the harm caused by sexual images created without someone’s consent.

A better approach would give people legal protection against the misuse of their faces and bodies, require AI companies to take reasonable steps to prevent abuse and allow legitimate uses of AI for art, satire and public-interest reporting.

Courts must decide how these protections fit within existing constitutional rules. Lawmakers also need to address gaps in laws covering privacy, sexual images and digital identity.

As AI tools make it easier to create realistic images of real people, AI identity rights will become harder to ignore. The Minnesota case could help clarify where the law draws the line, but more work will be needed to protect people’s consent and dignity as the technology develops.

The principle is simple: being able to create an image of someone does not give you permission to turn their likeness into sexual content without their consent. Stronger digital likeness rights should help ensure that people have legal protection, even when the images are entirely artificial.

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Deepfake Detection May Be the Wrong Battle – Provenance Is the Better Bet

Deepfake detection tools help identify AI-generated images, videos and audio. But as AI tools improve, fake content is becoming harder to spot. By the time a deepfake is detected, it may have already reached thousands of people. One way to address this problem is to check where digital content comes from before it spreads.

This is where content provenance can help. Instead of only looking for signs of manipulation, it records information about how a file was created. Along with this, it also looks for which tools were used and whether it was edited later.

As AI-generated content becomes more difficult to detect, checking its origin is becoming increasingly important. However, provenance is not a complete solution. It will work only if more companies adopt it, platforms preserve the records, and users can easily check the information.

Why Deepfake Detection Is Becoming More Difficult

Advances in artificial intelligence are making deepfake detection more difficult. AI tools can now create realistic images, videos, and audio recordings that are harder to distinguish from real content. Deepfake detection tools look for signs of manipulation, such as unusual facial movements, strange lighting, or unnatural voices.

However, these signs are becoming harder to spot as AI improves. A tool that can detect one type of deepfake may fail to identify another. Even simple changes, such as editing or compressing a video, can make it harder for these tools to detect fake content.

The U.S. Government Accountability Office (GAO) has also highlighted the challenges of detecting deepfakes in real-world situations. People who create fake content can change their methods to avoid detection. This makes it harder for detection tools to keep up with new techniques.

Social media platforms face another challenge. They handle large amounts of images, videos, and audio every day. As a result, deepfakes can spread widely before someone identifies or reports them.

Deepfake detection tools are still useful for checking suspicious content and identifying harmful media. However, they cannot catch every fake. Platforms also need ways to verify where digital content came from and whether it has been changed. Content provenance can help by providing information about a file’s source and editing history.

ALSO READ: OpenAI and Meta Warn U.S. Courts That AI Deepfakes Are Harder to Detect

How Content Provenance Can Help Identify Deepfakes

How Content Provenance Can Help Identify Deepfakes

Content provenance helps people check where an image, video, or audio file came from and how it was edited. Unlike deepfake detection tools, which look for signs of manipulation, provenance records information about a file’s origin and editing history.

One example is C2PA Content Credentials, an open standard that records how digital content was created and changed. Depending on the tools used, these records may include the software used, the edits made, and the file’s source.

C2PA uses cryptographic methods to check whether the recorded information has been altered. However, verified credentials do not guarantee that the content is true or authentic.

For example, a photo taken with a supported camera may include details about its origin. If someone edits the photo using compatible software, the changes may appear in its Content Credentials. People can review this information to see how the image has changed.

Content provenance can also identify AI-generated media. Compatible tools can add credentials showing that an image was created using AI. This helps people understand how the image was made, even when it looks like a real photograph.

Unlike deepfake detection, provenance does not rely on finding flaws in an image, video, or audio recording. It allows people to check a file’s recorded history instead. However, credentials may be missing or removed when content is shared or edited using unsupported tools. Provenance works best alongside deepfake detection and other verification methods.

Why Provenance Could Work Better at Scale Than Detection

Deepfake detection tools look for signs that content may be fake. But as AI tools improve, those signs can become harder to spot. Provenance takes a different approach by recording information about how a file was created and edited.

This could help social media platforms, news organisations, election officials and other groups that deal with large amounts of digital content every day. There are three main benefits.

  • Verifies content origin: Provenance credentials may show which tool created a file and record its editing history. This provides more information than a detection tool’s estimate of whether content is fake.
  • Tracks file changes: When compatible tools preserve provenance data during editing and sharing, journalists and investigators can review a file’s history and see how an image or video has changed over time.
  • Works across media formats: The same approach can be used for photos, videos and audio recordings. However, the available information depends on the file format and the tools involved.

The US National Institute of Standards and Technology (NIST) recognises several ways to address the risks of AI-generated content. These include checking authenticity, tracking a file’s origin, using watermarks and detecting synthetic media. Each method serves a different purpose, which shows why relying on a single solution may not be enough.

Provenance could make it easier to check the origin of digital content at scale. However, it cannot solve every deepfake problem on its own. It needs to work alongside detection tools and other methods of verifying content.

Why Content Provenance Can Fail When Digital Files Are Shared

One of the main challenges with content provenance is that the information attached to a file can disappear when it is edited or shared. C2PA Content Credentials can disappear when someone takes a screenshot or changes a file format. They may also be lost when a file is edited using unsupported software or shared through a platform that removes this information.

When Content Credentials disappear, the original history of an image or video may no longer be available. For example, a journalist may receive a video through a messaging app without the credentials attached to the original file. This does not mean the video is fake, but the journalist may no longer be able to verify its origin and editing history through those credentials.

Content provenance cannot guarantee that an image or video is authentic or accurate. It can show where a file came from and how it was edited. However, it cannot confirm whether the scene is real or the claims made about it are true.

For example, someone could share a genuine photograph with a misleading caption. Similarly, an AI-generated image could have valid Content Credentials identifying it as synthetic while depicting an event that never happened.

Content provenance can help people verify the origin and editing history of digital content, but it cannot prove that everything shown is true. Combining content provenance with fact-checking, source verification and deepfake detection tools can help people assess the authenticity and accuracy of images and videos more effectively.

ALSO READ: AI Watermarks Won’t Stop Deepfakes Unless Platforms Stop Breaking Them

Why AI Companies and Social Media Platforms Must Work Together

Content provenance will only be useful if more companies adopt it. If AI developers, software makers and social media platforms use different systems or fail to preserve the information, checking a file’s history will remain difficult.

AI companies need to add reliable origin information to the content their tools create. Camera makers and editing software providers should keep this information intact and update it when changes are made. Social media and messaging platforms also need to find ways to preserve these records or let users check them.

This information must also be easy for people to understand. Most users will not look through a file’s technical details to check where it came from. Platforms should make this information visible and explain what it means in simple language.

A file without provenance information should not automatically be treated as fake. Many genuine photos and videos were created before these systems existed. Their credentials can also disappear when files are edited or shared.

Social media platforms must keep this distinction clear. Missing information does not prove that someone has tried to deceive people. Provenance can help users make better decisions, but it should not become the only basis for judging whether content is genuine.

Provenance Cannot Replace Deepfake Detection

Content provenance can help people check where a file came from, but it cannot replace deepfake detection entirely. Not everyone uses tools that record a file’s origin. Some people may also remove this information on purpose, change the file or try to make false records look genuine. 

Older photos and videos may not have any records that can verify how they were created. This is where deepfake detection still has a role. These tools examine the content itself for signs of manipulation. 

Watermarks can provide another way to identify AI-generated content when they are present and can be verified. Human reviewers are also important when a decision could have serious consequences. The best approach is to use these methods together rather than depend on just one.

Provenance can help verify a file’s source and editing history. Watermarks can provide additional clues about how the content was created. Deepfake detectors can flag suspicious files, including those without reliable origin records. Investigators can then review the available evidence before deciding whether the content has been manipulated.

The US National Institute of Standards and Technology (NIST) also covers several methods for checking AI-generated content, including authentication, watermarking and detection. Each method has a different role in identifying and managing the risks.

The aim should not be to find one tool that catches every deepfake. Instead, companies need to combine different methods so that the weaknesses of one can be addressed by the others.

Why Deepfake Prevention Must Protect People, Not Just Content

Why Deepfake Prevention Must Protect People, Not Just Content

News organisations could use content provenance to help check photos and videos before publishing them. Editors could review a file’s credentials, compare it with the original version and look for gaps in its recorded history.

However, a file should not be considered genuine just because it has valid credentials. Journalists still need to check when and where the event took place, what the image shows and whether the claims surrounding it are accurate. Provenance can support this work, but it cannot replace proper reporting.

The same applies to social media users. Verified credentials can offer useful information about a file’s origin. If those details are missing, users should check the content further rather than assume it is fake.

For people targeted by deepfakes, the problem goes beyond verifying a file’s source. Fake intimate images and manipulated videos can spread quickly and cause serious harm. Provenance alone cannot stop people from creating or sharing this material. Platforms still need simple ways to report abusive content, remove it quickly and take action against accounts that distribute it.

The real challenge is not just identifying fake content. It is also protecting people from harm. Even when a platform cannot confirm whether a file is genuine, it needs clear procedures for handling content that violates someone’s privacy, rights or safety.

The Future of Deepfake Defense Depends on Verification

Deepfake detection is still important, but it cannot be the only way to deal with fake images, videos and audio. As AI tools improve, spotting signs of manipulation is likely to become more difficult.

Content provenance offers another way to check digital content. By recording where a file came from and how it was edited, it can give journalists, social media platforms and ordinary users information they cannot get from looking at the content alone.

For this approach to work, more companies need to adopt common standards and keep provenance records intact when files are shared. Platforms must also explain verification results in simple language. They need to make clear what the records confirm and what they cannot prove.

The aim is not to replace deepfake detection tools. It is to make checking content easier by giving people reliable information about its origin and history. The future of deepfake prevention will depend on both detection and verification.

Detection tools look for signs that content may be fake, while provenance helps people check where it came from and how it was changed. Neither method can solve the problem alone, but using them together can give people a better way to judge what they see online.

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Meta Says AI Is Catching Abuse Faster After Nudify Ads Slipped Through

Meta says its AI systems detected more than 97% of the child sexual exploitation content. The company removed it or took action in the first half of 2026, before users reported it. It took action against 33.2 million pieces of such content on Facebook and Instagram between January and June. Despite Meta’s progress in detecting harmful content, dangerous AI tools and their ads are still appearing on its platforms.

In an October 7 announcement, Meta introduced new AI systems to detect ads that lead users to child sexual abuse material hosted on external websites. The company is also using AI to flag suspicious ads, test its safety systems, and identify people who return after their accounts have been removed.

This shows that Meta is taking steps to improve its response to online exploitation. However, when people’s safety is at stake, platforms must do more than detect harmful content. They must prevent it from reaching users in the first place.

The issue extends beyond child safety. Meta has also faced scrutiny over ads for AI nudify apps that can create sexual images of real people without their consent. These cases raise concerns about whether Meta’s ad review systems can keep up with the growing misuse of AI tools.

Meta’s 97% Detection Rate Does Not Prove Its Platforms Are Safe

Meta says it detected more than 97% of the child sexual exploitation content it acted on between January and June 2026 before users reported it. In India, the company reported a rate of over 98%, covering 5.3 million pieces of content across Facebook and Instagram.

This shows that automated content moderation helps Meta identify harmful material before users flag it. However, they do not reveal how much illegal content its systems failed to detect, how long it took to remove violating posts, or whether harmful ads reached users before being taken down.

This covers child sexual exploitation content across Meta’s platforms, not just ads. This means they cannot be used to claim that Meta’s ad review system detects 97% of harmful advertisements before they reach users.

Meta has shared figures on the content it removed and the material it detected without user reports. However, these numbers alone do not show how much harmful content escaped detection or how well its moderation systems work overall.

This is an important gap in Meta’s safety claims. Effective content moderation should do more than remove reported violations or detect harmful material after it appears. It should also prevent users from seeing such content in the first place. That includes stopping ads for AI nudify apps and other services that can be used to create sexual images of people without their consent.

Meta Uses AI to Find Suspicious Ads and Expose Hidden Abuse

Meta Uses AI to Find Suspicious Ads and Expose Hidden Abuse

On October 7, Meta announced new AI tools to detect child sexual exploitation content and identify suspicious advertisements. The company said some offenders were using ads that looked harmless but directed users to external websites hosting illegal material.

Meta calls this tactic “signposting.” Instead of showing prohibited content in an ad, offenders use the ad to direct people to another website where the material is available.

To address this problem, Meta is using a large language model to identify suspicious ads based on their content and purpose. It is also checking the websites linked to ads, rather than relying only on the images and text displayed in them.

Other updates include extra AI checks to catch content missed by existing systems, an AI agent to test its safety measures for weaknesses, and better ways to identify people who return with new accounts after being banned.

These changes target a gap in ad moderation. An ad may look harmless on its own, but the website it links to can reveal its real purpose.

However, the need for these new tools raises questions about Meta’s earlier safeguards. If offenders could use harmless-looking ads to direct users to illegal content, why did the existing systems fail to catch this pattern sooner?

Meta says it expanded its investigation after discovering the tactic. While these updates may help detect such ads, platforms should keep improving their moderation systems before new abuse methods spread, rather than waiting for a problem to emerge.

ALSO READ: Meta Told to Toughen Deepfake Rules as AI Nude Abuse Spreads

Meta Still Struggles to Block Ads for AI Nudify Tools

Meta’s content moderation challenges are not limited to child safety. The company has also faced criticism for allowing ads that promote AI nudify tools, which can create fake sexual images of real people without their consent.

An August 18, 2026, investigation by WIRED found that Meta ran ads for Kromix, an AI image and video app. Some of its promotional material appeared to show sexual deepfakes resembling female US politicians.

One ad showed a woman resembling a prominent US politician before switching to a pornographic video featuring a similar-looking face. Meta removed the ads after WIRED contacted the company about them.

The investigation also found that some ads used innocent-looking images, such as landscapes, to hide sexually explicit content elsewhere in the video. This shows how advertisers can use misleading visuals to get around content review systems.

Meta said it removed more than 340,000 ads promoting nudify apps between November 2025 and January 2026. The company also said it was developing new technology to identify and block ads that try to bypass its rules.

However, the Kromix case shows that ads promoting AI nudify tools could still get through Meta’s review process.

AI moderation remains important because automated systems can scan large amounts of content quickly. But high detection rates alone do not prove that a platform is protecting users effectively.

Moderation systems may catch known violations but miss new tactics, misleading ads, or content that requires a closer look at its context. Meta’s experience with nudify ads highlights why its safeguards need regular testing and independent scrutiny.

The same concern applies to child safety. Platforms must do more than detect known abusive content. They also need to identify new methods used to promote harmful material and stop it from reaching users.

ALSO READ: AI Has Made Fake Nude Abuse Easier to Scale, New Berkeley Report Warns

Meta Should Be Held Accountable for the Ads It Approves

Meta earns money by allowing advertisers to promote products and services across its platforms. Its ad review systems decide which promotions can reach users. This makes the company responsible for checking whether advertisements violate its rules.

The risks go beyond a misleading ad. Promotions for AI nudify tools can direct users to services that create fake sexual images of real people without their consent. Removing these ads after they appear does not undo the harm they may have caused.

Meta needs to provide more than figures showing how many ads it has removed. The company should explain how it screens ads before they go live, how quickly it removes violations, and how many harmful promotions are identified only after external reports. It should also explain how it stops banned advertisers from returning with new accounts.

Independent researchers should have better access to the information they need to examine Meta’s ad moderation systems. Their findings can help expose weaknesses and show how advertisers bypass existing safeguards.

The Kromix investigation shows why this oversight matters. WIRED reported on the ads, and Meta removed them after the publication contacted the company. This raises questions about why its own review systems did not block the promotions earlier.

Meta’s new AI tools may help prevent similar cases, but the company needs to show that they work. Clear data, independent scrutiny, and stronger checks before ads go live would provide better evidence of progress than removal figures alone.

Meta Must Turn Better AI Detection Into Real User Protection

Meta Must Turn Better AI Detection Into Real User Protection

Meta’s reported detection rate is important because finding child sexual exploitation content before users report it can help limit its spread. Its new AI tools for checking ad destinations and testing safety measures may also help close gaps in its existing systems.

However, Meta’s progress should be measured by how well these tools protect users, not just by the number of violations it detects.

The company reported acting on 33.2 million pieces of child sexual exploitation content in the first half of 2026. It also said it detected more than 97% of that content before users reported it. These numbers show the scale of its enforcement efforts, but they do not reveal how much harmful material its systems failed to catch.

Similarly, removing harmful ads after journalists flag them does not prove that Meta’s existing review process is working effectively.

Meta needs to show that its systems can block prohibited ads before they reach users, detect new ways of bypassing its rules, and prevent banned advertisers from returning. It should also share enough data for independent researchers and regulators to assess whether its safeguards are improving.

AI can help platforms review content faster, but it cannot solve every moderation problem on its own. Clear rules, independent oversight, transparent reporting, and action against repeat offenders are also necessary.

Meta says its AI systems are getting better at detecting abuse. The real test is whether these improvements reduce the amount of harmful content users encounter in the first place.

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Meta’s Ad System Helped Promote an AI App That Could Nudify Female Politicians

AI nudify tools have made it easier to turn photos of real people into fake nude images. The problem is worse when social media platforms promote AI nudify apps that create sexual images without consent.

In August 2026, a WIRED investigation found that Meta had run ads for an AI app called Kromix. The app promoted features that could create pornographic deepfakes of real people. One ad showed a woman who looked like a well-known female US politician. The video then switched to an explicit clip featuring the same face.

This has raised concerns about Meta’s ad review process. They also questioned whether the company does enough to stop harmful AI tools from reaching users on Facebook and Instagram.

Meta prohibits sexual content in ads and restricts the promotion of apps that create nonconsensual intimate images. Yet, the Kromix ads still appeared on its platforms. This raises questions about how effectively Meta enforces its own advertising policies.

The issue goes beyond one app or ad campaign. Social media platforms must check what advertisers promote when they pay to reach users.

Meta Ran 32 Ads for an AI Nudify App

According to WIRED, Kromix presented itself as an AI image-editing app. However, its features went beyond basic photo editing.

The investigation found that paying users could upload photos and place people in different situations, including sexual scenes. One ad showed a woman who looked like a well-known female US politician. It then cut to a pornographic scene featuring a woman with the same face.

The ads also suggested that users could create these images with few restrictions on who they depicted. WIRED reported that the account behind the campaign ran 32 ads, according to Meta’s Ad Library. The ads stayed active for between five and 46 hours. Most received 10 or fewer impressions before they were removed.

The low number of impressions may have limited the campaign’s reach. But it does not address the main concern. Meta’s advertising system still allowed the ads to run, despite its rules against promoting this type of sexual content.

Meta removed the ads after WIRED contacted the company about them. This case raises questions about how Meta reviews advertisements before they appear on Facebook and Instagram. The number of people who see an ad is only part of the problem. Even ads with limited reach can expose gaps in the system used to review millions of advertisements.

Meta’s Ad Review System Failed to Stop the Campaign

Meta’s Ad Review System Failed to Stop the Campaign

Meta says it reviews ads before they appear on Facebook and Instagram. Its advertising policies and safety rules also restrict the promotion of nudify apps and content that creates intimate images without consent. However, the Kromix campaign raises questions about how well Meta enforces these rules.

According to WIRED, Meta relies mainly on automated tools to review ads. The page linked to the campaign was also created shortly before the ads appeared and had no followers. These details could have raised concerns. However, the report does not confirm whether Meta’s review system flagged them.

Automated tools help platforms review large numbers of ads quickly. But they can miss harmful content if advertisers find ways to hide what their products do. Meta needs safeguards that can identify these tactics before an ad reaches users.

WIRED also reported that some Kromix ads used harmless images of beaches, mountains, and waterfalls as cover images for videos containing pornographic material. These tactics may make it harder for automated systems to identify harmful ads during the initial review.

Meta’s head of women’s safety, Cindy Southworth, told WIRED that the company had removed more than 340,000 ads promoting nudify apps late last year. She also said Meta was improving its detection tools and taking legal action against app developers. The company acknowledged that some ads had bypassed its systems.

These steps are important, but they do not solve the problem on their own. Removing an ad after a journalist or researcher reports it is different from stopping it before it appears.

Meta needs to explain why the Kromix ads passed its review process. It should also show what changes it is making to prevent similar campaigns from appearing again. Stronger checks before publication could help stop ads promoting nonconsensual sexual images from reaching users in the first place.

ALSO READ: UK Bans Ads for AI Nudify Apps That Sexualize Real Women

Female Politicians Face a Different Risk From AI Deepfakes

AI-generated sexual images can harm anyone whose photos are used without consent. But for female politicians, the risks can extend beyond personal privacy. Such content can be used to attack their reputation and undermine their role in public life.

Female politicians regularly share photos, campaign videos, and interviews online. AI tools can misuse these public images to create fake sexual content without their permission.

The WIRED investigation showed how an image resembling a prominent US politician was used in an ad promoting pornographic content. The investigation did not establish that the politician herself appeared in or helped create the explicit material.

This highlights a key risk of deepfake pornography. Fake images and videos can make people appear connected to sexual content they never agreed to create. Even after removal, the material may already have been shared.

For women in politics, this abuse can damage their reputation, distract from their work, and discourage them from participating in public debate.

Meta should take these risks seriously. Its ad review system needs to do more to identify and block campaigns that promote nonconsensual sexual deepfakes, especially those that use recognisable faces to attract attention.

ALSO READ: Nudify Sites Target Nearly 150 European Politicians in Deepfake Porn Campaign

Meta Has Faced Repeated Problems With Nudify App Ads

The Kromix case is not the first time Meta has faced questions about ads promoting AI nudify apps. Previous investigations have also found advertisements for tools that can create sexual images of real people without their consent.

In June 2025, Meta announced legal action against the entity behind CrushAI. The company also said it was developing tools to detect ads promoting nudify services and sharing information about these apps with other technology companies.

However, similar advertisements continued to appear on Meta’s platforms. A CBS News investigation in June 2025 found hundreds of ads promoting AI tools that could create sexual deepfakes of real people.

In July 2026, the Los Angeles Times reported that the Tech Transparency Project had identified around 7,600 ads linked to a Meta advertising partner in China. The ads promoted apps that could create sexualised images and videos using women’s faces.

Meta has also reported removing large numbers of these ads. According to its first-half 2026 adversarial threat report, the company removed more than 344,000 ads promoting nudify apps from Facebook and Instagram between November 2025 and January 2026.

These figures show that Meta has taken action against such advertisements. But they also show the scale of the problem. Repeated investigations have continued to find similar campaigns despite the company’s enforcement efforts.

Meta needs to make it harder for advertisers to return with new accounts, different app names, or altered ads. It should also explain how many harmful advertisements its systems block before publication. Removal figures alone do not show how well its review process works or how many violating ads reach users first.

Apple Removed Kromix After WIRED’s Investigation

Meta was not the only company linked to the Kromix case. The app was also available on Apple’s App Store before WIRED contacted the company. Apple removed it after the publication asked questions about its features.

An Apple spokesperson told WIRED that Kromix appeared to have added prohibited content and features after its initial review. Apple said it bans apps designed to generate, distribute, or access pornography and removes apps that break its rules.

The case raises questions about how app stores and advertising platforms check AI tools. Apple decides which apps can remain on its App Store, while Meta reviews ads shown on Facebook and Instagram. Both companies have a role in stopping harmful AI apps from reaching users.

An app may initially appear to be a basic photo editor before adding features that enable sexual deepfakes. Its advertisements may also promote these features more openly than its app store listing.

Removing Kromix was necessary, but companies also need better ways to identify apps that change after approval. Closer cooperation between app stores, advertising platforms, and safety researchers could help prevent similar tools from spreading across services.

Meta Needs to Be More Accountable for the Ads It Runs

Meta Needs to Be More Accountable for the Ads It Runs

Meta has taken action against nudify apps through legal measures, improved detection tools, and ad removals. However, the Kromix case shows that the company needs stronger checks to stop harmful ads before they reach users.

Meta should strengthen advertiser checks and review apps more closely. New accounts, misleading thumbnails, suspicious ad language, and links to previously flagged services should trigger further review.

The company should also make it easier for researchers to report harmful ads and track what happens after a report. Its Ad Library provides useful information, but reports must lead to timely action.

Meta should publish clearer data on its ad moderation system. The number of removed ads does not show how many harmful ads escaped detection, how long they remained active, or whether the same advertisers returned with new campaigns.

Human reviewers should also play a clear role in cases involving nonconsensual sexual images. Automated tools can review large numbers of ads, but they need stronger support to catch advertisers who disguise harmful content.

These changes would help users, researchers, and regulators judge whether Meta’s advertising rules are working in practice.

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