OpenAI said criminal and state-linked groups misused ChatGPT for disinformation, scams and covert influence. Its latest threat report details coordinated account bans and highlights how AI tools are embedded within broader operational workflows rather than used in isolation.
One investigation linked accounts to Chinese law enforcement engaged in what were described as ‘cyber special operations’. Activities included planning influence campaigns, mass-reporting dissidents and drafting forged materials, with related efforts continuing through other tools despite model refusals.
The report also outlined a Cambodia-based romance scam targeting young men in Indonesia through a fake dating agency. Operators combined manual prompting with automated chatbots to sustain conversations and facilitate financial fraud, leading to account removals.
Separately, accounts tied to Russia’s ‘Rybar’ network used ChatGPT to draft and translate posts distributed across multiple platforms. OpenAI noted that campaign impact depended more on account reach and coordination than on AI-generated content alone.
Across China, Russia and parts of Southeast Asia, actors treated AI as one tool among many, alongside fake profiles, paid advertising and forged documents. OpenAI called for cross-industry vigilance, stressing the need to analyse behavioural patterns across platforms.
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Investigators in the US say that AI used by Meta is flooding child protection units with large volumes of unhelpful reports, thereby draining resources rather than assisting ongoing cases.
Officers in the Internet Crimes Against Children network told a New Mexico court that most alerts generated by the company’s platforms lack essential evidence or contain material that is not criminal, leaving teams unable to progress investigations.
Meta rejects the claim that it prioritises profit, stressing its cooperation with law enforcement and highlighting rapid response times to emergency requests.
Its position is challenged by officers who say the volume of AI-generated alerts has doubled since 2024, particularly after the Report Act broadened reporting obligations.
They argue that adolescent conversations and incomplete data now form a sizeable portion of the alerts, while genuine cases of child sexual abuse material are becoming harder to detect.
Internal company documents disclosed at trial show Meta executives raising concerns as early as 2019 about the impact of end-to-end encryption on the firm’s ability to identify child exploitation and support investigators.
Child safety groups have long warned that encryption could limit early detection, even though Meta says it has introduced new tools designed to operate safely within encrypted environments.
The growing influx of unusable tips is taking a heavy toll on investigative teams. Officers in the US say each report must still be reviewed manually, despite the low likelihood of actionable evidence, and this backlog is diminishing morale at a time when they say resources have not kept pace with demand.
They warn that meaningful cases risk being delayed as units struggle with a workload swollen by AI systems tuned to avoid regulatory penalties rather than investigative value.
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More than 25 million people across the United States have had personal information exposed following a ransomware attack on government contractor Conduent. Updated state breach notifications indicate the incident is larger than initially understood.
Conduent provides printing, payment processing, and benefit administration services for state agencies and large corporations. Its systems support food assistance, unemployment benefits, and workplace programmes, reaching more than 100 million individuals, according to the company.
US State disclosures show Oregon and Texas account for most of the affected records, with additional cases reported in Massachusetts, New Hampshire, and Washington. Compromised data includes names, dates of birth, addresses, Social Security numbers, health insurance information, and medical details.
Public information from Conduent has been limited since the January 2025 attack. An incident notice published in October carried a ‘noindex’ tag in its source code, preventing search engines from listing the page, which critics say reduced visibility for affected individuals.
The breach ranks among the largest recent ransomware incidents, though it is smaller than the 2024 Change Healthcare attack that affected 190 million people. Regulators and affected users continue seeking clarity on the Conduent case and its security failures.
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Elon Musk, CEO of Tesla and xAI, has publicly accused Anthropic of stealing large volumes of data to train its AI models. The allegation was made on X in response to posts referencing Community Notes attached to Anthropic-related content.
Musk claimed the company had engaged in large-scale data theft and suggested that it had paid multi-billion-dollar settlements. Those financial claims remain contested, and no official confirmation has been provided to substantiate the figures.
Anthropic is guilty of stealing training data at massive scale and has had to pay multi-billion dollar settlements for their theft. This is just a fact. https://t.co/EEtdsJQ1Op
Anthropic, known for developing the Claude AI model, was founded by former OpenAI employees and promotes an approach centred on AI safety and responsible development. The company has not publicly responded to Musk’s latest accusations.
The dispute reflects a broader conflict across the AI industry over how companies collect the text, images and other materials required to train large language models. Much of this data is scraped from the internet, often without explicit permission from rights holders.
Multiple lawsuits filed by authors, media organisations and software developers are testing whether large-scale scraping qualifies as fair use under copyright law. Court rulings in these cases could reshape licensing practices, impose financial penalties, and alter the economics of AI development.
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The ShinyHunters extortion group has published a 6.1GB archive, which it claims contains more than 12 million records stolen from CarGurus, a US-based automotive platform. Have I Been Pwned listed the dataset, reporting that roughly 3.7 million records appear to be new.
The exposed information includes email addresses, IP addresses, full names, phone numbers, physical addresses, user account IDs, and finance-related application data belonging to CarGurus users. Dealer account details and subscription information were also reportedly included in the archive.
CarGurus has not issued a public statement confirming a breach. However, Have I Been Pwned said it attempts to verify the authenticity of datasets before adding them to its database, suggesting a level of validation of the leaked material.
Security experts warn that the availability of the data could increase the risk of phishing. Users are advised to remain cautious of unsolicited communications and potential scams that may leverage the exposed personal information.
ShinyHunters has recently claimed attacks against multiple large organisations across telecoms, fintech, retail, and media. The group is known for using social engineering tactics, including voice phishing and malicious OAuth applications, to gain access to SaaS platforms and extract customer data.
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The European Data Protection Supervisor (EDPS) and authorities from 61 jurisdictions issued a joint statement on AI-generated imagery, warning about tools that create realistic depictions of identifiable individuals without consent. The move underscores concerns over privacy, dignity and child safety.
Authorities said advances in AI image and video tools, especially when integrated into social media platforms, have enabled non-consensual intimate imagery, defamatory depictions, and other harmful content. Children and vulnerable groups are seen as particularly at risk.
The EDPS and the other signatories reminded organisations that AI content-generation systems must comply with applicable data protection and privacy laws. They stressed that creating non-consensual intimate imagery may constitute a criminal offence in many jurisdictions.
Organisations are urged to implement safeguards against misuse of personal data, ensure transparency about system capabilities and uses, and provide accessible mechanisms for swift content removal. Stronger protections and age-appropriate information are expected where children are involved.
Authorities signalled plans for coordinated responses, including enforcement, policy development and education initiatives. The EDPS and fellow signatories urged organisations to engage proactively with regulators and ensure innovation does not undermine fundamental rights.
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The first enforcement provisions of the EU AI Act entered into force on 2 February 2025, marking a turning point for Europe’s AI startup ecosystem. The initial phase targets ‘unacceptable risk’ systems, including social scoring, real-time biometric surveillance in public spaces, and manipulative AI practices.
Under the regulation, penalties can reach €35 million or 7% of global annual turnover, whichever is higher. Although the current enforcement covers only prohibited practices, the move signals that Europe’s AI rulebook is now operational rather than theoretical.
Broader obligations for high-risk AI systems, such as hiring tools, credit scoring, and medical diagnostics, will apply from August 2026. Separate rules for general-purpose AI models are scheduled to take effect in August 2025.
Surveys from European SME groups indicate that many smaller technology companies feel unprepared. A significant share of reports have not conducted formal risk classification of their AI systems, despite this being a foundational requirement under the EU AI Act’s tiered framework.
While some founders warn that compliance costs could slow innovation, others point to long-term benefits from clearer governance standards. For startups, the coming months will focus on aligning products with AI Act risk tiers and strengthening documentation and oversight before stricter rules apply.
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The UK’s Information Commissioner’s Office has fined Reddit £14.47 million after finding that the platform unlawfully used children’s personal information and failed to put in place adequate age checks.
Although Reddit updated its processes in July 2025, self-declaration remained easy to bypass, offering only a veneer of protection. Investigators also found that the company had not completed a data protection impact assessment until 2025, despite a large number of teenagers using the service.
Concerns were heightened by the volume of children affected and the risks created by relying on inadequate age checks.
The regulator noted that unlawful data processing occurred over a prolonged period, and that children were at risk of viewing harmful material while their information was processed without a lawful basis.
UK Information Commissioner John Edwards said companies must prioritise meaningful age assurance and understand the responsibilities set out in the Children’s Code.
The ICO said it will continue monitoring Reddit’s current controls and expects online platforms to align with robust age-assurance standards rather than rely on weak verification.
It will coordinate its oversight with Ofcom as part of broader efforts to strengthen online safety and ensure under-18s benefit from high privacy protections by default.
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Meta has committed to purchasing $60bn worth of AI chips from Advanced Micro Devices over five years, signalling one of the largest infrastructure bets in the sector despite ongoing concerns about an AI investment bubble.
The agreement includes a 10% stake in the chipmaker and large-scale deployment of next-generation hardware beginning later this year.
Analysts say the move signals a shift to secure compute capacity and cut reliance on Nvidia amid supply constraints. Talks with Google and ongoing in-house chip work signal a multi-vendor strategy to support expanding data centre operations.
Executives say the investment reflects a shift towards hosting AI workloads and infrastructure services. Custom processors built for performance and efficiency will complement AMD GPUs, supporting capacity expansion as enterprise demand rises.
Enterprise AI competition intensifies as Anthropic and OpenAI expand integrations and tools. Significant platform investments are reshaping semiconductors and signalling strong long-term confidence in AI computing demand.
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In December 2025, the Macquarie Dictionary, Merriam-Webster, and the American Dialect Society named ‘slop’ as the Word of the Year, reflecting a widespread reaction to AI-generated content online, often referred to as ‘AI slop.’ By choosing ‘slop’, typically associated with unappetising animal feed, they captured unease about the digital clutter created by AI tools.
As LLMs and AI tools became accessible to more people, many saw them as opportunities for profit through the creation of artificial content for marketing or entertainment, or through the manipulation of social media algorithms. However, despite video and image generation advances, there is a growing gap between perceived quality and actual detection: many overestimate how easily AI content evades notice, fueling scepticism about its online value.
As generative AI systems expand, the debate goes beyond digital clutter to deeper concerns about trust, market incentives, and regulatory resilience. How will societies manage the social, economic, and governance impacts of an information ecosystem increasingly shaped by automated abundance? In simplified terms, is AI slop more than a simple digital nuisance, or do we needlessly worry about a transient vogue that will eventually fade away?
The social aspect of AI slop’s influence
The most visible effects of AI slop emerge on large social media platforms such as YouTube, TikTok, and Instagram. Users frequently encounter AI-generated images and videos that appropriate celebrity likenesses without consent, depict fabricated events, or present sensational and misleading scenarios. Comment sections often become informal verification spaces, where some users identify visual inconsistencies and warn others, while many remain uncertain about the content’s authenticity.
However, no platform has suffered the AI slop effect as much as Facebook, and once you take a glance at its demographics, the pieces start to come together. According to multiple studies, Facebook’s user base is mostly populated by adults aged 25-34, but users over the age of 55 make up nearly 24 percent of all users. While seniors do not constitute the majority (yet), younger generations have been steadily migrating to social platforms such as TikTok, Instagram, and X, leaving the most popular platform to the whims of the older generation.
Due to factors such as cognitive decline, positivity bias, or digital (il)literacy, older social media users are more likely to fall for scams and fraud. Such conditions make Facebook an ideal place for spreading low-quality AI slop and false information. Scammers use AI tools to create fake images and videos about made-up crises to raise money for causes that are not real.
The lack of regulation on Meta’s side is the most glaring sore spot, evidenced by the company pushing back against the EU’s Digital Services Act (DSA) and Digital Markets Act (DMA), viewing them as ‘overreaching‘ and stifling innovation. The math is simple: content generates engagement, resulting in more revenue for Facebook and other platforms owned by Meta. Whether that content is authentic and high-quality or low-effort AI slop, the numbers don’t care.
The economics behind AI slop
At its core, AI content is not just a social media phenomenon, but an economic one as well. GenAI tools drastically reduce the cost and time required to produce all types of content, and when production approaches zero marginal cost, the incentive to churn out AI slop seems too good to ignore. Even minimal engagement can generate positive returns through advertising, affiliate marketing, or platform monetisation schemes.
AI content production goes beyond exploiting social media algorithms and monetisation policies. SEO can now be automated at scale, thus generating thousands of keyword-optimised articles within hours. Affiliate link farming allows creators to monetise their products or product recommendations with minimal editorial input.
On video platforms like TikTok and YouTube, synthetic voice-overs and AI-generated visuals are on full display, banking on trending topics and using AI-generated thumbnails to garner more views on a whim. Thanks to AI tools, content creators can post relevant AI-generated content in minutes, enabling them to jump on the hottest topics and drive clicks faster than with any other authentic content creation method.
To add salt to the wound, YouTube content creators share the sentiment that they are victims of the platform’s double standards in enforcing its strict community guidelines. Even the largest YouTube Channels are often flagged for a plethora of breaches, including copyright claims and depictions of dangerous or illegal activities, and harmful speech, to name a few. On the other hand, AI slop videos seem to fly under YouTube’s radar, leading to more resentment towards AI-generated content.
Businesses that rely on generative AI tools to market their services online are also finding AI to be the way to go, as most users are still not too keen on distinguishing authentic content, nor do they give much importance to those aspects. Instead of paying voice-over artists and illustrators, it is way cheaper to simply create a desired post in under a few minutes, adding fuel to an already raging fire. Some might call it AI slop, but again, the numbers are what truly matter.
The regulatory challenge of AI slop
AI slop is not only a social and economic issue, but also a regulatory one. The problem is not a single AI-generated post that promotes harmful behaviour or misleading information, but the sheer scale of synthetic content entering digital platforms. When large volumes of low-value or deceptive material circulate on the web, they can distort information ecosystems and make moderation a tough challenge. Such a predicament shifts the focus from individual violations to broader systemic effects.
In the EU, the DSA requires very large online platforms to assess and mitigate the systemic risks linked to their services. While the DSA does not specifically target AI slop, its provisions on transparency, content recommendation algorithms, and risk mitigation could apply if AI content significantly affects public discourse or enables fraud. The challenge lies in defining when content volume prevails over quality control, becoming a systemic issue rather than isolated misuse.
Debates around labelling AI slop and transparency also play a large role. Policymakers and platforms have explored ways to flag AI-generated content throughout disclosures or watermarking. For example, OpenAI’s Sora generates videos with a faint Sora watermark, although it is hardly visible to an uninitiated user. Nevertheless, labelling alone may not address deeper concerns if recommendation systems continue to prioritise engagement above all else, with the issue not only being whether users know the content is AI-generated, but how such content is ranked, amplified, and monetised.
More broadly, AI slop highlights the limits of traditional content moderation. As generative tools make production faster and cheaper, enforcement systems may struggle to keep pace. Regulation, therefore, faces a structural question: can existing digital governance frameworks preserve information quality in an environment where automated content production continues to grow?
Building resilience in the era of AI slop
Humans are considered the most adaptable species on Earth, and for good reason. While AI slop has exposed weaknesses in platform design, monetisation models, and moderation systems, it may also serve as a catalyst for adaptation. Unless regulatory bodies unite under one banner and agree to ban AI content for good, it is safe to say that synthetic content is here to stay. However, sooner or later, systemic regulations will evolve to address this new AI craze and mitigate its negative effects.
The AI slop bubble is bound to burst at some point, as online users will come to favour meticulously crafted content – whether authentic or artificial over low-quality content. Consequently, incentives may also evolve along with content saturation, leading to a greater focus on quality rather than quantity. Advertisers and brands often prioritise credibility and brand safety, which could encourage platforms to refine their ranking systems to reward originality, reliability, and verified creators.
Transparency requirements, systemic risk assessments, and discussions around provenance disclosure mechanisms imply that governance is responding to the realities of generative AI. Instead of marking the deterioration of digital spaces, AI slop may represent a transitional phase in which platforms, policymakers, and users are challenged to adjust their expectations and norms accordingly.
Finally, the long-term outcome will depend entirely on whether innovation, market incentives, and governance structures can converge around information quality and resilience. In that sense, AI slop may ultimately function less as a permanent state of affairs and more as a stress test to separate the wheat from the chaff. In the upcoming struggle between user experience and generative AI tools, the former will have the final say, which is an encouraging thought.
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