Ofcom media regulators said none of the company made any serious efforts to make recommendations feeds/explore pages safer, despite proof that these platforms are the main entry point through which underage kids face harm.
Ofcom said the platforms are “not safe enough”. The report comes after Ofcom’s call for stricter action on children’s online safety, saying Roblox, meta, and Snap had each complied to stronger anti-grooming actions.
TikTok said it was quite disappointing that Ofcom didn’t acknowledge its safety measures, whereas Youtube said it worked with child safety researchers to give industry grade, age-appropriate experiences for children.
Ofcom’s latest report explains how five large social media and video platforms responded to its call for safety measures. The report said that, "Notably, TikTok and YouTube failed to commit to any significant changes to reduce harmful content being served to children, maintaining their feeds are already safe for children.” Ofcom added, "Our wealth of evidence, published today, suggests they are still not safe enough."
Responding to the criticism, YouTube and TikTok said that safety measures already existed. YouTube’s short-form video timer allowed parents to control scrolling time for Shorts feed, whereas TikTok stopped direct messaging (DM) for under-16 children.
Governments have taken measures to address online child safety. UK PM Keir Starmer has urged social media platforms to take greater responsibility. Britain is discussing tighter restrictions, this includes a potential ban on under-16 children that use social media, inspired from Australia's landmark decision that tackled addictive design features.
According to social media analyst Matt Navarra, the report has shown a shift in how we perceive online harm as a “product problem.” Earlier, the debate was, “did the platform remove harmful content quickly enough?' - the new one has shifted towards, 'why did the platform show it to a child in the first place?”
Ofcom reported that 73% of 11-17 year olds were exposed to malicious content for four weeks, primarily through recommendation feeds. TikTok was the most cited, followed by YouTube, Instagram and Snapchat. Experts stress that YouTube and TikTok said their existing platforms were adequate, but media regulators have found their feeds to be unsafe.
A cybercrime group known as TeamPCP has been linked to an expanding series of software supply chain attacks that researchers say have affected hundreds of organizations, with GitHub becoming the latest high-profile name connected to the campaign.
GitHub recently disclosed that it had identified thousands of repositories impacted after a developer reportedly installed a compromised extension for Visual Studio Code (VSCode), Microsoft's widely used source-code editor. TeamPCP later claimed on the cybercrime forum BreachForums that it had gained access to roughly 4,000 GitHub repositories and attempted to advertise what it described as GitHub source code and internal organizational data for sale. GitHub stated that it had identified at least 3,800 affected repositories but said its investigation indicated the exposed repositories contained the company's own code rather than customer code.
The incident highlights the growing danger of software supply chain attacks. Unlike traditional intrusions that target a company directly, these operations focus on software that developers trust and use every day. By secretly inserting malicious code into legitimate tools, attackers can potentially reach thousands of downstream users through a single compromise.
Security researchers tracking TeamPCP believe the group has transformed what was once considered an occasional cybersecurity threat into a recurring problem. According to software supply chain security firm Socket, the group has launched around 20 separate attack waves in recent months, embedding malicious code into more than 500 unique software projects. When different compromised versions are counted, that number rises to well over a thousand malicious releases.
Researchers say the group's success stems from a self-reinforcing attack cycle. TeamPCP typically begins by compromising a development environment associated with an open-source project. Malware is then inserted into software packages that are downloaded by other developers. Once installed, the malicious code can steal credentials, authentication tokens, and publishing permissions, allowing attackers to compromise additional software projects and continue spreading through the development ecosystem.
Recent investigations indicate that TeamPCP has increasingly automated this process through a worm known as Mini Shai-Hulud. The malware has been observed creating GitHub repositories containing encrypted credentials stolen from victims while leaving references to Frank Herbert's science-fiction universe Dune. Researchers note that although the name resembles an earlier worm called Shai-Hulud, there is currently no evidence linking TeamPCP to that previous campaign.
GitHub is not the only organization mentioned in connection with the operation. Researchers have previously linked TeamPCP activity to incidents involving OpenAI, Mercor, and several widely used software development projects. During a major expansion of its campaign earlier this year, the group reportedly compromised software and infrastructure associated with Trivy, LiteLLM, Checkmarx, pgserve, TanStack, and Mistral AI. The stolen credentials obtained through those attacks were allegedly used to fuel further compromises.
Security analysts describe credential theft as the group's primary enabler. Long-lived access tokens and poorly managed credentials allow attackers to move from one environment to another with relatively little effort. According to researchers, once a single trusted credential is stolen, it can provide access to additional repositories, cloud resources, and development systems.
The group's activities have also evolved beyond software tampering. Threat intelligence researchers report that TeamPCP has engaged in ransomware deployment, data extortion, and data-sale operations. In April, the group reportedly began adopting elements of a ransomware-as-a-service model through associations with cybercriminal platforms such as BreachForums and DragonForce. Researchers have additionally observed activity involving CanisterWorm, malware that targeted Kubernetes environments and reportedly deployed destructive functionality against selected Iranian targets.
The scale of the campaign has renewed debate over how organizations should safely consume open-source software. Experts recommend strengthening credential management practices, regularly rotating access tokens, limiting permissions wherever possible, and closely monitoring software dependencies. They also advise organizations to avoid automatically installing newly released software updates without first validating their integrity. In some recent cases, security teams detected malicious updates within minutes, but users who relied on automatic updates had already installed the compromised code.
The bigger lesson, researchers say, is that trust alone is no longer sufficient in modern software development. Open-source software remains a cornerstone of the global technology ecosystem, but organizations increasingly need verification processes, update review procedures, and continuous monitoring to reduce the risk posed by rapidly spreading supply chain attacks.
Researchers have identified a technique that could allow malicious content embedded within a web page to appear inside ChatGPT responses, creating an opportunity for phishing, tracking, and social-engineering attacks through a platform users generally regard as trustworthy.
The attack method, named "ChatGPhish" by cybersecurity firm Permiso Security, focuses on how ChatGPT handles Markdown-formatted content when summarizing information from external websites. Markdown is a commonly used formatting language that allows web content to include elements such as hyperlinks and images.
According to Permiso Security researcher Andi Ahmeti, ChatGPT's web interface trusts Markdown links and image URLs originating from third-party pages that users ask the assistant to summarize. When a response is generated, the platform can automatically retrieve those images and present hyperlinks as active, clickable elements within the chatbot's interface.
In a scenario outlined by the researchers, an attacker could place a small hidden payload within a web page. If a user later asks ChatGPT to summarize that page, the embedded content may become part of the model's processing context. During response rendering, attacker-controlled images could be automatically requested, potentially exposing information such as the visitor's IP address, browser User-Agent string, and Referer data.
The researchers also found that links embedded in a manipulated page could appear as legitimate clickable items inside the AI-generated summary. Beyond directing users to phishing destinations, attackers could display fabricated security notifications, account-warning messages designed to imitate system alerts, or QR codes hosted on attacker-controlled infrastructure such as an Amazon S3 bucket. A victim scanning such a code with a mobile device could be redirected to a malicious destination, bypassing certain desktop-based URL filtering mechanisms and enterprise security controls.
The research adds to a growing body of evidence showing that AI-powered summarization tools can become unintended delivery channels for attacker instructions. Earlier this year, Permiso Security disclosed a separate attack involving Microsoft Copilot, where specially crafted instructions hidden inside an email influenced the output generated by the AI assistant. That technique was classified as a cross-prompt injection attack, also known as indirect prompt injection.
According to the researchers, the primary issue is not simply that prompt injection is possible. The more significant concern is how the manipulated content is ultimately presented to the user. A standard web page summarized by ChatGPT can cause phishing links, deceptive warnings, QR codes, and remotely hosted content to be displayed directly inside the assistant's interface, giving attacker-controlled material an appearance of legitimacy.
As AI assistants become common tools for workplace research, document review, and information gathering, this behavior introduces a new risk. Any web page processed by an employee could potentially contain hidden instructions or malicious content capable of influencing both the generated summary and the way that information is displayed.
Permiso Security noted that this shifts phishing activity beyond traditional delivery methods. Users no longer need to open a suspicious attachment or interact with an obviously fraudulent email. In some cases, simply asking an AI assistant to summarize a webpage may expose them to attacker-controlled content.
The disclosure arrives alongside research from Adversa AI detailing two attack techniques aimed at AI coding assistants and agentic development tools. The first, known as SymJack, allows a malicious code repository to achieve remote code execution through an AI-powered coding assistant.
According to Adversa AI researcher Rony Utevsky, the attack relies on convincing the AI assistant to perform what appears to be a harmless file-copy operation. The destination, however, is a symbolic link pointing to the assistant's own configuration file. As a result, attacker-controlled content is written into the configuration. When the assistant is restarted, a malicious Model Context Protocol (MCP) server is launched and executes arbitrary code using the victim's privileges.
The second technique, called TrustFall, uses a repository containing a malicious MCP server together with configuration settings that automatically approve its execution. A developer only needs to clone or open the repository in an AI coding environment and accept a folder-trust prompt. Once that action is taken, the attacker-controlled MCP server can start automatically without requiring additional tool approval, running with the same operating-system permissions as the developer.
Adversa AI explained that a victim who clones the repository, launches Claude, and accepts the generic trust prompt effectively allows the malicious MCP server to start as a native process on the machine. The payload executes immediately when the server starts, before additional prompts or tool requests occur.
The ChatGPhish findings emerge amid a steady stream of research examining weaknesses in modern AI systems, coding agents, and autonomous workflows.
Researchers recently described a jailbreak method called Involuntary In-Context Learning (IICL), which exploits the tension between a model's contextual learning behavior and its safety mechanisms to bypass protections in GPT-5.4.
Separate research from Cisco found that many AI security evaluations fail to reflect how real-world attackers operate. Rather than relying on a single prompt, attackers often use multiple interactions, gradually changing their wording, adopting different personas, and breaking objectives into smaller steps. Cisco argued that single-turn testing overlooks these techniques because real attacks frequently unfold across extended conversations.
Additional research has uncovered a vulnerability affecting Anthropic Claude Code in which a user-level configuration file, "~/.claude.json," can be altered through a rogue npm package. The attack enables modification of MCP endpoints and can place an attacker between Claude Code and an OAuth-protected MCP server, creating an opportunity to capture authentication tokens used to access downstream software-as-a-service platforms.
Researchers have also documented a technique involving OpenClaw skills that appear harmless during installation but later retrieve remote updates. In one scenario, attackers can influence an AI agent through workspace files after instructing users to append specific content to a file called HEARTBEAT.md during setup.
Another study demonstrated how hidden text embedded inside phishing emails can manipulate AI-based email security products. Attackers concealed text taken from legitimate newsletters and romance novels to make malicious messages appear benign to automated filtering systems.
LayerX researchers separately disclosed a flaw known as ClaudeBleed affecting Claude's Chrome extension. According to the company, any browser extension, including one without elevated permissions, could communicate with Claude's language model through the extension's content script because the code does not adequately verify the source of incoming instructions. This could allow another extension to issue commands and trigger actions through the AI assistant.
Cisco researchers also examined typographic prompt injection attacks against vision-language models. In these attacks, adversarial text is embedded inside images. The manipulated image may appear unreadable or resemble visual noise to humans and OCR-based filters while remaining interpretable to the target AI model.
Other recently disclosed vulnerabilities include flaws in Microsoft Semantic Kernel, tracked as CVE-2026-25592 and CVE-2026-26030, which researchers said could allow prompt-injection attacks to progress into host-level remote code execution.
Researchers additionally described the Neural Exec attack and abuse of the Unicode right-to-left-override function to bypass safety mechanisms protecting Apple's local AI models. The issue has since been addressed in iOS 26.4 and macOS 26.4.
A separate indirect prompt-injection vulnerability known as WebPromptTrap affected BrowserOS, an open-source agentic browser. The technique relied on hidden instructions embedded in an otherwise legitimate article to influence an AI-generated summary and persuade users to approve an authorization request. The issue was patched in BrowserOS version 0.32.0.
Research into the broader AI-agent ecosystem has uncovered persistent security weaknesses. An audit covering 3,984 skills published through ClawHub and skills.sh found that 534 skills, representing 13.4% of the total, contained at least one critical security issue. Researchers also identified 1,467 skills with broader weaknesses, including malware distribution risks, prompt-injection opportunities, exposed secrets, hard-coded API credentials, insecure handling of authentication data, and unsafe exposure to third-party content.
Additional studies identified attacks against NemoClaw, NVIDIA's reference framework for securing OpenClaw agents. Researchers demonstrated methods for extracting OpenClaw data through the platform's default sandbox configuration using either a malicious GitHub repository or a compromised npm package.
Security researchers are increasingly examining how advances in AI capability could affect offensive cyber operations. According to researchers at Palo Alto Networks Unit 42, more capable AI models could allow attackers to exploit both newly discovered and previously known vulnerabilities at a scale, speed, and level of automation that has traditionally required specialized expertise.
Last month, Unit 42 presented a proof-of-concept AI agent called Zealot that was capable of carrying out cloud attack operations with limited human involvement. The system chained together reconnaissance, exploitation, privilege escalation, and data-exfiltration activities by leveraging known weaknesses and misconfigurations.
Researchers argue that cloud environments are particularly susceptible to this type of automation because most administrative functions are accessible through APIs, multiple discovery mechanisms exist for identifying resources, configuration errors remain common, and access control often depends heavily on credentials.
According to Unit 42 researchers Yahav Festinger and Chen Doytshman, current large language models are already capable of coordinating reconnaissance, exploitation, privilege escalation, and data theft activities with relatively little human guidance. The techniques themselves are not necessarily new. What is changing is the speed and scale at which those established attack patterns can now be executed through AI-assisted automation.
For decades, university degrees in business, law, finance, and management were widely viewed as reliable pathways to stable office careers and long-term financial security. Throughout much of the late 20th century, white-collar professions became deeply associated with economic mobility, especially in countries like the United States where corporate and professional employment expanded rapidly.
Now, artificial intelligence is forcing technology leaders, economists, and workers to confront a different question: what happens if software systems become capable of performing many of those office-based jobs faster and at lower cost than humans?
That debate intensified after Mustafa Suleyman, the CEO of Microsoft AI, warned earlier this year that AI systems may soon handle most professional computer-based tasks with minimal human involvement. In an interview with the Financial Times, Suleyman predicted that the transition could happen far sooner than many people expect, estimating that major disruption may begin within the next 12 to 18 months.
According to Suleyman, artificial intelligence models are moving toward what he described as “human-level performance” across a wide range of professional responsibilities. He argued that jobs centered around sitting at a computer, processing information, reviewing documents, writing reports, managing workflows, or analyzing data are particularly vulnerable to automation.
The Microsoft AI executive specifically pointed to industries such as accounting, legal services, marketing, and project management as sectors where AI systems could eventually replace large portions of repetitive and administrative work.
His remarks add to a growing list of warnings from major AI executives who believe artificial intelligence may fundamentally reshape white-collar employment. The conversation has become increasingly urgent as businesses rapidly adopt generative AI systems capable of writing text, generating code, summarizing documents, automating customer support, and completing analytical tasks.
Suleyman’s prediction closely mirrored concerns raised this week by AI researcher Matt Shumer, whose widely circulated essay compared the current state of AI development to the early weeks of 2020 before the COVID-19 pandemic dramatically altered everyday life. Shumer argued that many people may still be underestimating the speed and scale of disruption AI could introduce into the global economy.
He suggested the impact of widespread automation may ultimately exceed the societal changes caused by the pandemic because AI has the potential to affect nearly every knowledge-based profession simultaneously.
One of Suleyman’s key arguments centers around the rapid expansion of computational power, often referred to within the industry as “compute.” Compute describes the hardware infrastructure and processing capability used to train and operate artificial intelligence models. As companies invest billions of dollars into advanced chips, data centers, and AI infrastructure, newer models are becoming increasingly capable of handling sophisticated tasks that previously required trained professionals.
Suleyman said improvements in compute could eventually allow AI systems to write software code more effectively than many human programmers. The claim reflects a broader trend in the technology industry, where AI-assisted coding tools are already being integrated into software engineering workflows to generate code, identify errors, and automate portions of development.
Even some of the people building advanced AI systems have publicly acknowledged concerns about how quickly the technology is progressing. OpenAI CEO Sam Altman and Matt Shumer have both written about the emotional discomfort of watching artificial intelligence evolve to the point where parts of their own expertise could become less valuable over time.
Warnings about large-scale job disruption have circulated repeatedly throughout 2025. Last May, Anthropic CEO Dario Amodei cautioned that AI could potentially eliminate up to half of entry-level white-collar positions. Although Amodei later moderated some of those predictions, his comments contributed to growing anxiety surrounding the future of professional employment.
Ford CEO Jim Farley also predicted that artificial intelligence may eventually reduce the number of white-collar jobs in the United States by approximately 50%, highlighting how concerns over AI automation are spreading beyond technology companies into traditional industries.
In a separate analysis published by The Atlantic, journalist Josh Tyrangiel argued that the United States remains largely unprepared for the economic and social consequences of rapid AI adoption. Tyrangiel compared the recent silence from many corporate leaders to spotting “a shark fin break the water,” suggesting that warning signs are visible even if the full disruption has not yet arrived.
The discussion surrounding artificial intelligence intensified further after SpaceX CEO Elon Musk stated during the World Economic Forum in Davos that artificial general intelligence, commonly known as AGI, could emerge as early as this year. AGI refers to hypothetical AI systems capable of matching or exceeding human intelligence across nearly all cognitive tasks rather than specializing in only one function.
Despite increasingly dramatic predictions from technology executives, current evidence suggests that AI’s real-world impact on professional jobs remains more limited than many forecasts imply.
A 2025 report published by Thomson Reuters found that professionals in industries such as law, accounting, and auditing are primarily using AI tools for targeted tasks including document review, routine analysis, summarization, and administrative support. While these tools have improved efficiency in some workflows, the report did not indicate widespread replacement of human professionals.
Several economists have also argued that the financial benefits of AI remain concentrated within large technology firms rather than spreading evenly across the broader economy.
Research conducted by Apollo Global Management chief economist Torsten Slok found that profit margins among major technology companies increased by more than 20% during the fourth quarter of 2025. However, companies included in the broader Bloomberg 500 Index showed little measurable improvement during the same period.
Slok also noted that many Wall Street investors remain unconvinced that artificial intelligence will generate substantial earnings growth outside the technology sector in the near future.
At the same time, there are early indicators that AI-related restructuring is beginning to affect parts of the workforce. Employment consultancy Challenger, Gray & Christmas reported that approximately 49,135 job cuts this year were linked to artificial intelligence.
Microsoft itself laid off around 15,000 employees last year. Although the company did not officially identify AI as the direct reason behind the cuts, CEO Satya Nadella stated in a memo released after the layoffs that Microsoft needed to “reimagine” its mission for what he described as a new technological era.
Financial markets have also reacted strongly to the possibility that AI systems could disrupt existing software business models. Earlier this year, software stocks experienced a major selloff driven by investor fears that advanced AI agents could reduce the need for traditional software-as-a-service products, commonly known as SaaS platforms.
Industry analysts referred to the market downturn as the “SaaSpocalypse.” The decline accelerated after Anthropic and OpenAI introduced enterprise-focused agentic AI systems capable of independently completing complex digital tasks that previously required multiple software tools and human oversight.
Agentic AI systems are designed to perform sequences of actions autonomously, including making decisions, interacting with applications, and executing workflows with limited human input.
Despite skepticism from some economists and analysts, Suleyman remains highly confident about AI’s long-term capabilities. He argued that organizations may eventually be able to customize AI systems for virtually any operational need, allowing businesses, institutions, and even individuals to create specialized AI models tailored to specific tasks.
Suleyman compared the future creation of AI models to producing a podcast or publishing a blog, suggesting the process may eventually become simple and accessible for ordinary users.
A major part of Suleyman’s strategy at Microsoft AI involves pursuing what he described as “superintelligence,” a term used to describe AI systems that significantly exceed human cognitive abilities.
Microsoft is also reportedly attempting to reduce its dependence on OpenAI by investing more heavily in its own internal AI models and infrastructure. Developing independent foundation models has become increasingly important for major technology companies competing in the global AI race.
However, skepticism surrounding the technology continues to grow. Critics argue that many current AI systems still struggle with factual accuracy, reasoning consistency, hallucinations, legal accountability, cybersecurity concerns, and reliability in high-risk professional environments.
Some analysts have also questioned whether current levels of investment in artificial intelligence are sustainable if measurable productivity gains outside the technology industry remain limited.
Competition within the AI industry is also intensifying rapidly. Anthropic’s Claude models have recently gained stronger traction among enterprise customers, increasing competitive pressure on OpenAI in the race to dominate business-focused AI services.
Even so, Suleyman continues to reject the idea that AI development is slowing down. In an interview featured by MIT Technology Review in April, he maintained that artificial intelligence research and capabilities are still accelerating rather than approaching a plateau.
For now, experts remain divided on how quickly AI will transform the workforce. While some executives believe widespread automation is approaching rapidly, others argue that human judgment, oversight, regulation, ethics, and organizational trust will continue to play a critical role in many professions for years to come.
The next few years may ultimately determine whether artificial intelligence becomes primarily a productivity assistant for professionals or a technology capable of permanently reshaping the structure of white-collar employment across the global economy.