Security researchers warn hackers are exploiting a new feature in Microsoft Copilot Studio. The issue affects recently launched Connected Agents functionality.
Connected Agents allows AI systems to interact and share tools across environments. Researchers say default settings can expose sensitive capabilities without clear monitoring.
Zenity Labs reported attackers linking rogue agents to trusted systems. Exploits included unauthorised email sending and data access.
Experts urge organisations to disable Connected Agents for critical workloads. Stronger authentication and restricted access are advised until safeguards improve.
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The Association of Chartered Certified Accountants (ACCA) has announced it will largely end remote examinations in the UK from March 2026, requiring students to sit tests in person unless exceptional circumstances apply.
The decision aims to address a surge in cheating, particularly facilitated by AI tools.
Remote testing was introduced during the Covid-19 pandemic to allow students to continue qualifying when in-person exams were impossible. The ACCA said online assessments have now become too difficult to monitor effectively, despite efforts to strengthen safeguards against misconduct.
Investigations show cheating has impacted major auditing firms, including the ‘big four’ and other top companies. High-profile cases, such as EY’s $100m (£74m) settlement in the US, highlight the risks posed by compromised professional examinations.
While other accounting bodies, including the Institute of Chartered Accountants in England and Wales, continue to allow some online exams, the ACCA has indicated that high-stakes assessments must now be conducted in person to maintain credibility and integrity.
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US federal agencies planning to deploy agentic AI in 2026 are being told to prioritise data organisation as a prerequisite for effective adoption. AI infrastructure providers say poorly structured data remains a major barrier to turning agentic systems into operational tools.
Public sector executives at Amazon Web Services, Oracle, and Cisco said government clients are shifting focus away from basic chatbot use cases. Instead, agencies are seeking domain-specific AI systems capable of handling defined tasks and delivering measurable outcomes.
US industry leaders said achieving this shift requires modernising legacy infrastructure alongside cleaning, structuring, and contextualising data. Executives stressed that agentic AI depends on high-quality data pipelines that allow systems to act autonomously within defined parameters.
Oracle said its public sector strategy for 2026 centres on enabling context-aware AI through updated data assets. Company executives argued that AI systems are only effective when deeply aligned with an organisation’s underlying data environment.
The companies said early agentic AI use cases include document review, data entry, and network traffic management. Cloud infrastructure was also highlighted as critical for scaling agentic systems and accelerating innovation across government workflows.
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A new computational brain model, built entirely from biological principles, has learned a visual categorisation task with accuracy and variability matching that of lab animals. Remarkably, the model achieved these results without being trained on any animal data.
The biomimetic design integrates detailed synaptic rules with large-scale architecture across the cortex, striatum, brainstem, and acetylcholine-modulated systems.
As the model learned, it reproduced neural rhythms observed in real animals, including strengthened beta-band synchrony during correct decisions. The result demonstrates emergent realism in both behaviour and underlying neural activity.
The model also revealed a previously unnoticed set of ‘incongruent neurons’ that predicted errors. When researchers revisited animal data, they found the same signals had gone undetected, highlighting the platform’s potential to uncover hidden neural dynamics.
Beyond neuroscience research, the model offers a powerful tool for testing neurotherapeutic interventions in silico. Simulating disease-related circuits allows scientists to test treatments before costly clinical trials, potentially speeding up the development of next-generation neurotherapeutics.
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Meta Platforms has acquired Manus, a Singapore-based developer of general-purpose AI agents, as part of its continued push to expand artificial intelligence capabilities. The deal underscores Meta’s strategy of acquiring specialised AI firms to accelerate product development.
Manus, founded in China before relocating to Singapore, develops AI agents capable of performing tasks such as market research, coding, and data analysis. The company said it reached more than $100 million in annualised revenue within eight months of launch and was serving millions of users worldwide.
Meta said the acquisition will help integrate advanced automation into its consumer and enterprise offerings, including the Meta AI assistant. Manus will continue operating its subscription service, and its employees will join Meta’s teams.
Financial terms were not disclosed, but media reports valued the deal at more than $2 billion. Manus had been seeking funding at a similar valuation before being approached by Meta and had recently raised capital from international investors.
The acquisition follows a series of AI-focused deals by Meta, including investments in Scale AI and AI device start-ups. Analysts say the move highlights intensifying competition among major technology firms to secure AI talent and capabilities.
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Protecting AI agents from manipulation has become a top priority for OpenAI after rolling out a major security upgrade to ChatGPT Atlas.
The browser-based agent now includes stronger safeguards against prompt injection attacks, where hidden instructions inside emails, documents or webpages attempt to redirect the agent’s behaviour instead of following the user’s commands.
Prompt injection poses a unique risk because Atlas can carry out actions that a person would normally perform inside a browser. A malicious email or webpage could attempt to trigger data exposure, unauthorised transactions or file deletion.
Criminals exploit the fact that agents process large volumes of content across an almost unlimited online surface.
OpenAI has developed an automated red-team framework that uses reinforcement learning to simulate sophisticated attackers.
When fresh attack patterns are discovered, the models behind Atlas are retrained so that resistance is built into the agent rather than added afterwards. Monitoring and safety controls are also updated using real attack traces.
These new protections are already live for all Atlas users. OpenAI advises people to limit logged-in access where possible, check confirmation prompts carefully and give agents well-scoped tasks instead of broad instructions.
The company argues that proactive defence is essential as agentic AI becomes more capable and widely deployed.
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Researchers are warning that AI chatbots may treat dialect speakers unfairly instead of engaging with them neutrally. Studies across English and German dialects found that large language models often attach negative stereotypes or misunderstand everyday expressions, leading to discriminatory replies.
A study in Germany tested ten language models using dialects such as Bavarian and Kölsch. The systems repeatedly described dialect speakers as uneducated or angry, and the bias became stronger when the dialect was explicitly identified.
Similar findings emerged elsewhere, including UK council services and AI shopping assistants that struggled with African American English.
Experts argue that such patterns risk amplifying social inequality as governments and businesses rely more heavily on AI. One Indian job applicant even saw a chatbot change his surname to reflect a higher caste, showing how linguistic bias can intersect with social hierarchy instead of challenging it.
Developers are now exploring customised AI models trained with local language data so systems can respond accurately without reinforcing stereotypes.
Researchers say bias can be tuned out of AI if handled responsibly, which could help protect dialect speakers rather than marginalise them.
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These rapidly produced AI videos aim to grab attention but make it harder for traditional creators to gain visibility. Analysis of top trending channels shows Spain leads in AI slop subscribers with 20.22 million, while South Korea’s channels have amassed 8.45 billion views.
India’s Bandar Apna Dost is the most-viewed AI slop channel, earning an estimated $4.25 million annually and showing the profit potential of mass AI-generated content.
The prevalence of AI slop and brainrot has sparked debates over creativity, ethics, and advertiser confidence. YouTube CEO Neal Mohan calls generative AI transformative, but rising automated videos raise concerns over quality and brand safety.
Researchers warn that repeated exposure to AI-generated content can distort perception and contribute to information overload. Some AI content earns artistic respect, but much normalises low-quality videos, making it harder for users to tell meaningful content from repetitive or misleading material.
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SoftBank Group has agreed to acquire DigitalBridge for $4 billion, strengthening its global digital infrastructure capabilities. The move aims to scale data centres, connectivity, and edge networks to support next-generation AI services.
The acquisition aligns with SoftBank’s mission to develop Artificial Super Intelligence (ASI), providing the compute power and connectivity needed to deploy AI at scale.
DigitalBridge’s global portfolio of data centres, cell towers, fibre networks, and edge infrastructure will enhance SoftBank’s ability to finance and operate these assets worldwide.
DigitalBridge will continue to operate independently under CEO Marc Ganzi. The transaction, valued at a 15% premium to its closing share price, is expected to close in the second half of 2026, pending regulatory approval.
SoftBank and DigitalBridge anticipate that the combined resources will accelerate investments in AI infrastructure, supporting the rapid growth of technology companies and fostering the development of advanced AI applications.
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China’s AI industry entered 2025 as a perceived follower but ended the year transformed. Rapid technical progress and commercial milestones reshaped global perceptions of Chinese innovation.
The surprise release of DeepSeek R1 demonstrated strong reasoning performance at unusually low training costs. Open access challenged assumptions about chip dominance and boosted adoption across emerging markets.
State backing and private capital followed quickly, lifting the AI’s sector valuations and supporting embodied intelligence projects. Leading model developers prepared IPO filings, signalling confidence in long term growth.
Chinese firms increasingly prioritised practical deployment, multilingual capability, and service integration. Global expansion now stresses cultural adaptation rather than raw technical benchmarks alone.
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