The impacted tools are Windsurf, Google Antigravity, Cursor, Amazon Q Developer, Claude Code by Anthropic, and Augment. Wiz has termed the technique GhostApproval and posted it recently.
Three of the six AI assistants have addressed, two did not, while Anthropic argues if it is a bug. The most vulnerable are the tools that modify file before you can notice.
The threat actors exploit an old Unix feature called symlink (or symbolic link), that AI assistants cannot check.
A symlink silently directs to other files somewhere else on disk, hence writing to it particularly writes to the victim.
“Symbolic links have been a security headache since the early days of Unix. From /tmp race conditions to privilege escalation exploits, symlinks have a long history of bypassing security boundaries by making one path silently resolve to another. It's a well-documented attack primitive - CWE-61 dates back decades,” Wiz said.
Wiz made a malicious repository with a symbolic link called project_settings.json that really directs to target’s SSH login file, ~/.ssh/authorized_keys. The repo’s README commands the assistant to put “a line” to project_settings.json, and this line is the hacker’s SSH key mimicking an innocent setting. “
If you ask the agent to “set up the workspace” or “follow the README,” it writes the key directly via the symlink into the login file. Following this, if the machine plays an SSH service the threat actor can access, they can sign in without password.
Another variant of the attack writes to your shell startup file, ~/.zshrc, which the shell runs the next moment you open a terminal without needing an SSH. There are no indications that any of this has been abused in real-time operations, Wiz has only demonstrated it as their research.
“Symlinks have been exploited for decades – in race conditions (CVE-2018-15664), in package managers (CVE-2021-32803), in container escapes (CVE-2024-21626). Any time a tool writes to a user-controlled path without resolving it first, symlinks become a weapon,” Wiz wrote in its blog.
After this, the attacker could read chats, steal user data, and command bots to send hacker-written texts such as re-entering a password.
Cyber security firm Varonis discovered the tactic and called it ‘Rogue Agent.’ The bug impacted only businesses that make agents with custom Code Blocks and Dialogflow’s Playbooks, which allows hackers to add their own Python. The attack was not remote, or unauthorized.
For the attack to happen, it required the dialogflow.playbooks.update green light one such agent, which restricts the hacker to an infected insider or a breached developer account, not some stranger on the web. From that point, the reach extended to every agent inside the project.
Google has patched the bug, and Varonis and Google have said there are no signs that the flaw was deployed in a real attack or campaign.
Dialogflow’s Code Blocks allows developers to add custom Python to a chatbot’s flow to test input, invoke defined tools, and control behavior.
The code runs within a Google-operated Cloud Run environment, and every agent that uses Code Blocks in the similar Google Cloud project shares one incident of it. The customer cannot control or see the environment that Google runs, meanwhile Varonis discovered no real separation between the agents within it.
When the agent runs a Code Block, the code is added to internal setup code and sent to Python’s exec()function. The functions and variables that block can touch are defined by the setup.
Functions consist(), which makes the bot reply with a given string, whereas variables consist of a history of full chats and state for session information such as the session ID.
Varonis discovered code_execution_env.py, the file that does this wrapping, lying in the shared environment with write access.
As the file was writable, a single Code Block could change it. The block downloads an altered code_execution_env.py from a threat actor-controlled server and overwrites the original within the running container.
After that, the attacker’s variant commands every Code Block deployment throughout every agent that shares the environment. The attacker’s code sits in the same place as the real code, with similar access to respond(), state, and history,
Chinese artificial intelligence company Z.ai, formerly known as Zhipu AI, has introduced GLM 5.2, an open-weight large language model that is attracting attention among developers for combining advanced AI capabilities with the flexibility to run on privately owned hardware. Unlike proprietary AI platforms such as ChatGPT and Claude, which are primarily accessed through cloud-based subscriptions, GLM 5.2 allows developers to download, customize, and deploy the model within their own computing environments, offering greater control over infrastructure, privacy, and operational costs.
The release comes as open-weight AI models continue to narrow the performance gap with leading commercial systems. While proprietary models have traditionally dominated the AI ecosystem with stronger reasoning capabilities, newer open-weight alternatives, including Meta's Llama family, Mistral, and now GLM 5.2, are demonstrating that many enterprise workloads no longer require exclusive reliance on premium cloud-hosted models. Businesses commonly use AI to summarize extensive document repositories, generate and debug software code, automate repetitive workflows, and retrieve information from internal knowledge bases, making cost-efficient deployment an increasingly important consideration.
Unlike fully open-source AI projects that typically publish training code, data processing pipelines, evaluation frameworks, and other development components, open-weight models primarily provide access to the trained model parameters. This enables organizations to fine-tune and integrate the model into their own applications while maintaining considerably more flexibility than closed AI services, where the underlying model remains inaccessible.
Interest in GLM 5.2 has also grown following demonstrations showing the model running locally on high-end Apple systems, including the Mac mini. Although these deployments require powerful hardware, they illustrate how advanced AI models are gradually becoming practical outside centralized cloud infrastructure. For organizations handling sensitive financial information, medical records, intellectual property, or confidential research, local deployment reduces the need to transmit data to third-party platforms, strengthening privacy protections while supporting regulatory compliance and data sovereignty requirements.
Despite its flexibility, GLM 5.2 remains an exceptionally demanding model. Built using a Mixture-of-Experts architecture containing between 744 billion and 753 billion parameters, the model occupies approximately 1.51TB of storage and memory in its original form. Developers therefore rely on quantization, a compression technique that reduces memory requirements by lowering the numerical precision of model weights. Even after aggressive optimization, approximately 240GB of memory is still required to load the model. GLM 5.2 also supports a one-million-token context window, allowing it to process entire software repositories, lengthy technical documentation, and extensive research collections within a single prompt, though doing so places additional demands on system memory.
As organizations continue evaluating how AI should be deployed across their operations, GLM 5.2 reflects a broader industry movement toward flexible AI ecosystems where proprietary, open-weight, and locally hosted models each serve different operational needs. Rather than replacing commercial AI platforms outright, models such as GLM 5.2 provide businesses with additional options to balance performance, cost, security, and data control as enterprise AI adoption continues to evolve.
ChatGPT Atlas from OpenAI
Comet from Perplexity
Anthropic’s Claude browser
Fellou
Genspark browser
Sigma browser
LayerX experts made a proof-of-concept (PoC), which was tested against these agentic AI browser products. The findings revealed that only one browser addressed the issue after receiving the report.
An AI browser can streamline the entire workflow for the users. If you switch it to agent mode, it can click type, and visit sites that the user has already logged into. Access is the key point hare, which also becomes the problem.
Experts made a (PoC) in which an infected webpage showed a BioShock-themed puzzle that rewards wrong answers. This tricks the browser that normal rules are not applicable.
The trap works because of how these AI-powered browsers read. The webpage and instruction surface as a single stream of text, which allows a malicious page access in commands mimicking ordinary content or game rules. The agent can not tell which is which. Experts have termed this indirect prompt injection.
For instance, the compromise starts with a web page made as a puzzle. 3+4+=9 is a wrong answer but the browser rewards it. When the agent accepts that wrong answer is the reward, it follows game puzzle logic not security logic. Following this, the puzzle asks the browser to record login credentials. All six browsers could not flag it as something malicious. To win the game, the agent is commanded to go to a GitHub repository and share the data in the code, such as sensitive data like passwords.
When the link is sent to the target's GitHub repository, it retrieves SSH login credentials and sends them to the hackers. The main issue here is that browsers can’t differentiate between real scenarios and malicious fictional ones.
According to LayerX, “Once the agents figured out the rules and learned that 'incorrect' actions are acceptable, they were no longer tied to reality.” “When tasked with the final step of the puzzle – compromising user credentials – all 6 agents failed to identify it as going against their safety guardrails,” the experts continued.
The PoC did not execute any malicious commands but warned that it could do so.
According to experts, only OpenAI implemented a working patch for BioShocking in its browser.
Anthropic tried to fix the issue on its chrome login, but the patch was not working against the PoC. Perplexity did not fix the issue, and closed the report.
LayerX advises that AI vendors should add specific user acknowledgement for sensitive work, and stronger security checks.
Anthropic is preparing to restore access to its Claude Fable 5 artificial intelligence model after the U.S. Department of Commerce lifted export controls that had temporarily restricted deployment of the company's most advanced AI systems.
The company announced on X that access to Claude Fable 5 will begin returning on Wednesday following the government's decision. Anthropic also confirmed that the export restrictions affecting both Claude Fable 5 and Claude Mythos 5 have been removed.
"We've received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5," the company said in its statement, adding that it will begin restoring access on Wednesday and provide additional updates as the rollout progresses.
Anthropic also thanked its community for its patience during the temporary suspension and acknowledged the teams involved in preparing the models for redeployment.
Although the rollout is set to begin immediately, the company has not clarified whether Claude Fable 5 will become available to all users at the same time. It remains uncertain whether users outside the United States will regain access during the initial phase of the deployment or whether availability will expand gradually across different regions.
The export restrictions were introduced earlier after U.S. authorities raised national security concerns surrounding the deployment of highly capable frontier AI models. During that period, Anthropic temporarily suspended access while it worked to comply with government requirements and strengthen safeguards governing the release of its latest systems.
While restoring access to its models, Anthropic also appears to be expanding identity verification measures for certain Claude services.
Recent references to Know Your Customer (KYC) procedures discovered on the company's website suggest that some users may soon be required to verify their identities before accessing specific Claude capabilities. The references have prompted speculation that advanced models such as Claude Fable 5 could initially be limited to verified users or become available only in certain regions as Anthropic gradually expands access.
According to Anthropic's support documentation, identity verification is being introduced for a limited number of use cases. Users may encounter verification requests when using particular Claude features, during routine platform integrity reviews, or as part of broader safety, security and regulatory compliance checks.
The company says the verification process is intended to reduce abuse of its AI systems, enforce platform usage policies and meet legal obligations associated with operating increasingly powerful AI technologies.
"Being responsible with powerful technology starts with knowing who is using it," Anthropic said while explaining the purpose of the new verification measures.
Anthropic has selected Persona as its identity verification provider. Users who are asked to complete verification may be required to submit a valid government-issued photo identification document, including a passport, driver's license, state or provincial identification card, or a national identity card.
The company notes that several forms of identification will not be accepted during the verification process. These include photocopies, screenshots, scanned documents, mobile IDs, student identification cards, employee badges, bank cards and temporary paper identification documents.
Some users may also be asked to complete a live selfie verification using the camera on a computer or mobile device. According to Anthropic, the entire verification process typically takes less than five minutes to complete.
Addressing privacy concerns, the company says identity documents and selfie data are collected and stored by Persona rather than directly within Anthropic's own systems. However, Anthropic may access verification records through Persona when necessary, including during account review or appeal processes.
Anthropic also emphasized that identity verification information is not used to train Claude's AI models. Instead, the data is used solely to confirm a user's identity and to satisfy the company's legal, safety and compliance responsibilities.
The restoration of Claude Fable 5, together with the introduction of targeted identity verification measures, reflects the growing intersection of frontier AI development, government oversight and platform security. As developers release increasingly capable AI systems, compliance requirements, export regulations and stronger user verification are becoming a more prominent part of deploying advanced models responsibly.
The development, first reported by The Information on June 25, 2026, reflects the growing role of the US government in overseeing the deployment of cutting-edge artificial intelligence models. The move also signals a shift in how frontier AI systems may be introduced to the public going forward.
The government's request comes shortly after its dispute with rival AI startup Anthropic. Earlier this month, on June 12, the Trump administration directed Anthropic to temporarily take its latest AI models, Fable 5 and Mythos 5, offline under new export control measures aimed at preventing access by foreign nationals. Officials cited national security risks behind the decision.
Anthropic described the action as a "misunderstanding" and said it hoped to restore access "as soon as possible," though the incident established a significant precedent for government intervention in AI model releases.
Mythos had been shared with around 40 organisations, including Google, Microsoft and JPMorgan Chase, through a restricted programme known as Project Glasswing. According to reports, the model's ability to autonomously identify software vulnerabilities and carry out complex, multi-step cybersecurity attacks without human involvement raised concerns among US officials.
A source familiar with the matter said both OpenAI and the US administration consider GPT-5.6 to be "on par" with Anthropic's Mythos, particularly regarding its cybersecurity capabilities. That assessment prompted officials to recommend a phased rollout instead of an immediate public launch.
OpenAI CEO Sam Altman reportedly informed employees during an internal Q&A session on June 25 that GPT-5.6 would first be made available to a select group of enterprise customers.
In a follow-up internal memo, Altman explained that the government would be "approving access customer by customer during this preview period." The request reportedly came from the Office of the National Cyber Director and the Office of Science and Technology Policy, while Commerce Secretary Howard Lutnick also advised OpenAI not to proceed without approvals from multiple federal agencies.
Although OpenAI agreed to the arrangement, Altman indicated that the company does not see this as a long-term solution. According to The Information, he wrote: "We’ve made clear to the U.S. government that this is not our preferred long-term model, and will work with them and others in industry to achieve a more sustainable approach for future releases."
Meanwhile, a White House official told CNN that the administration continues "to collaborate with frontier AI labs to develop shared approaches for addressing the challenges of scaling this technology."
The broader public release of GPT-5.6 is expected to take place a "couple of weeks" after the limited preview, depending on how the government-led approval process progresses.
The latest development highlights the absence of a formal federal regulatory framework governing the review of advanced AI models before public deployment.
President Trump's executive order on "Promoting Advanced AI Innovation and Security" encourages AI companies to voluntarily provide frontier models to the government for cybersecurity assessments for up to one month before public release. However, compliance with the programme is voluntary rather than legally required.
For now, OpenAI's agreement with the US government represents one of the clearest examples of collaboration between federal authorities and an AI company. The outcome of GPT-5.6's controlled rollout could influence how other leading AI developers introduce powerful new models in the future.
Anthropic has accused Chinese technology conglomerate Alibaba and its AI research division, Qwen, of carrying out a large-scale effort to extract capabilities from its Claude family of artificial intelligence models, describing the incident as the most extensive distillation operation the company has encountered.
The allegations were detailed in a June 10 letter sent to U.S. Senate Banking Committee Chair Tim Scott and Ranking Member Elizabeth Warren. In the correspondence, Anthropic claimed that operators linked to Alibaba and Qwen systematically interacted with Claude in an attempt to capture and reproduce some of the model's most advanced capabilities.
According to the company, the activity occurred between April 22 and June 5, 2026. During that period, Anthropic says it recorded more than 28.8 million exchanges associated with the operation. The requests were allegedly distributed across nearly 25,000 fraudulent accounts, enabling the actors to conduct high-volume interactions with the platform while obscuring the true source of the activity.
Anthropic stated that the campaign was not focused on general-purpose chatbot functions. Instead, it allegedly targeted capabilities considered among the most valuable within the Claude ecosystem, including software engineering tasks and advanced agentic reasoning. These functions form a critical component of the company's Mythos Preview model, one of Anthropic's most sophisticated AI systems designed to perform complex reasoning and autonomous task execution.
At the center of the allegations is a technique known as adversarial distillation. In machine learning, distillation generally refers to the process of training a model using outputs generated by another system. While the approach itself is commonly used within the AI industry, Anthropic argues that the method becomes problematic when it relies on unauthorized access to proprietary models.
According to the company, the actors behind the campaign repeatedly queried Claude and collected its responses at scale. Those outputs could then be used as training material for another AI system, allowing developers to reproduce aspects of Claude's behavior without investing the time, computational resources, and research expenditure typically required to build a frontier model from the ground up.
Anthropic warned lawmakers that such activity enables organizations to appropriate years of research and development through large-scale extraction campaigns. The company argued that these operations are designed to gather capabilities developed by leading U.S. AI laboratories and incorporate them into competing systems without bearing the costs associated with original model development.
Beyond intellectual property concerns, Anthropic also raised questions about safety. The company noted that models trained through adversarial distillation may replicate useful capabilities while failing to inherit the safeguards, alignment mechanisms, and risk controls embedded within the original system. As a result, the practice could create AI models that retain advanced functionality but operate with fewer protections against misuse.
The allegations against Alibaba follow earlier claims made by Anthropic regarding unauthorized access attempts linked to Chinese AI developers. In February 2026, the company disclosed that DeepSeek, the startup whose low-cost AI models attracted global attention in 2025, was among several organizations accused of attempting to improperly obtain Claude outputs. Anthropic now characterizes these incidents as part of a broader pattern of repeated efforts to extract capabilities from leading U.S. AI systems.
The dispute emerges amid growing government scrutiny of advanced AI technologies. Earlier this month, Anthropic revealed that it had received guidance from the Trump administration requiring the company to restrict access to its newest AI models, including Fable 5 and Mythos 5. Under the directive, access would be limited to U.S. persons, preventing non-U.S. citizens, including some employees, from interacting with the latest systems.
The issue is also beginning to influence policy discussions on Capitol Hill. Senators Bill Hagerty and Andy Kim are reportedly preparing legislation that would authorize sanctions or other penalties against Chinese organizations found to have improperly obtained outputs from U.S. AI models for the purpose of training competing systems. The proposal reflects growing concern among lawmakers that frontier AI capabilities have become both strategic economic assets and matters of national security.
Alibaba has not publicly responded to the allegations.
The dispute surfaces a new battleground in the global AI race. As companies invest billions of dollars to develop increasingly capable models, concerns are shifting beyond traditional cybersecurity threats toward the protection of model knowledge itself. For AI developers, the challenge is no longer limited to securing infrastructure and data. It increasingly involves preventing the large-scale extraction of capabilities that can be repurposed to accelerate the development of rival systems.
With governments, technology companies, and regulators paying closer attention to model security, the Anthropic-Alibaba dispute may become an early test case for how the industry addresses unauthorized AI capability harvesting and the growing geopolitical competition surrounding advanced artificial intelligence.
The responsibilities placed on cybersecurity leaders are becoming increasingly difficult to manage as organizations face a growing number of cyber threats, rapid adoption of artificial intelligence technologies, and increasing demands for security oversight across the business.
A recent survey conducted by the Information Systems Security Association (ISSA) International and research firm Omdia found that 68% of cybersecurity and IT professionals believe their jobs are more difficult today than they were two years ago. More than half of respondents reported heavier workloads and greater operational complexity (55%), while 52% said the volume and intensity of cyber threats have become more overwhelming.
Security teams are being asked to protect increasingly complex digital environments while also helping organizations adopt new technologies such as generative AI. At the same time, many security leaders say they are struggling to secure sufficient support from other parts of the business.
According to Shawn Murray, former president of ISSA and a fractional Chief Information Security Officer (CISO), many security executives regularly work long hours while attempting to address security concerns that are often introduced without their involvement. In some organizations, new technologies are adopted before security teams are included in planning discussions, creating additional challenges for risk management and governance.
As a result, some experienced CISOs are leaving traditional full-time leadership positions and choosing consulting or fractional roles instead. These arrangements allow security professionals to work with multiple organizations while focusing on businesses that are willing to involve cybersecurity leaders in strategic decision-making.
While legal accountability was once considered one of the largest concerns facing CISOs, the survey suggests that anxiety around personal liability has become less prominent than in previous years. Instead, many respondents identified the security implications of artificial intelligence as one of the most significant new sources of pressure.
AI has created both opportunities and challenges for cybersecurity teams. One growing concern is the rise of "shadow AI," where employees begin using AI tools and services without notifying security teams or obtaining formal approval. Similar issues emerged during the early stages of cloud adoption, when departments could deploy new services independently without providing visibility to cybersecurity staff.
This lack of visibility can create greater security gaps. When security teams do not know which AI applications, models, or processes are being used across an organization, it becomes more difficult to identify risks, monitor suspicious activity, and respond effectively to potential incidents.
Despite these concerns, cybersecurity professionals are increasingly interested in using AI to improve their own operations. The survey found that 37% of respondents are already using AI-powered tools to address cybersecurity challenges, while another 46% plan to adopt such technologies in the future.
Among the most common use cases identified by respondents were automated cybersecurity assessments, software testing, predictive risk analysis, and threat detection. These capabilities could help security teams reduce manual workloads and process large volumes of security data more efficiently.
Alex Hutton, CISO at Atlantic Union Bank, noted that the cybersecurity environment has changed significantly in recent years. Whether organizations fully embrace advanced AI systems or not, security professionals must continuously learn about new technologies, understand emerging risks, and adapt their security strategies accordingly.
The survey also highlighted a notable shift in how organizations obtain cybersecurity leadership. The percentage of companies employing full-time CISOs declined from 76% in 2024 to 63%, while the use of fractional CISOs increased from 6% to 15% over the same period.
Industry observers believe this trend reflects growing demand for cybersecurity expertise rather than a reduction in the importance of the CISO role. Many small and mid-sized organizations face the same security, compliance, and governance challenges as larger enterprises but often lack the budget required to hire a full-time executive.
Cyber insurance requirements are also contributing to demand for experienced security leadership. Organizations are increasingly expected to demonstrate strong cybersecurity practices and effective risk management controls before obtaining coverage or meeting insurer requirements. CISOs frequently play a central role in helping businesses assess risks, improve security programs, and document compliance efforts.
According to Hutton, the rise of fractional and virtual CISOs provides organizations with access to executive-level security guidance without requiring a full-time appointment. Rather than signaling the decline of cybersecurity leadership positions, the change may represent an expansion of cybersecurity services to organizations that previously could not afford dedicated executive expertise.
As cyber threats continue to grow and AI reshapes business operations, cybersecurity leaders are expected to remain critical decision-makers. However, the role itself is changing, requiring security professionals to balance technical oversight, business strategy, regulatory expectations, and emerging technologies in an increasingly demanding environment.
Nvidia has announced a new processor designed to run artificial intelligence applications directly on personal computers, signaling the company's latest effort to expand beyond the data center market and into everyday computing devices.
The announcement was made by Nvidia Chief Executive Officer Jensen Huang during a keynote presentation in Taipei ahead of Computex, one of the world's largest technology trade shows. The new chip, called RTX Spark, was developed as part of a long-running collaboration between Nvidia and Microsoft aimed at adapting personal computers for increasingly complex AI workloads.
Unlike many current AI services that rely on cloud infrastructure to process requests, the RTX Spark platform is designed to execute AI tasks locally on laptops and desktop systems. This allows certain AI functions to operate directly on the device rather than sending data to remote servers for processing. Industry observers believe this approach could improve response times, reduce dependence on internet connectivity, and give users greater control over sensitive information.
Nvidia said the processor was developed in partnership with Taiwanese semiconductor company MediaTek. Systems powered by the chip are expected to become available later this year through several major computer manufacturers, including Dell, HP, Lenovo, ASUS, MSI, and Microsoft's Surface product line. Additional products from Acer and GIGABYTE are also expected to follow.
The launch places Nvidia in more direct competition with companies such as AMD, Intel, Apple, and Qualcomm, all of which are pursuing their own strategies for bringing artificial intelligence capabilities to personal computers. While Nvidia has established a dominant position in hardware used to train large AI models, the company is now increasingly focused on technologies that run AI applications after those models have already been developed.
A major objective behind the RTX Spark platform is support for so-called AI agents. Unlike conventional chatbots that simply answer user questions, AI agents are designed to perform sequences of tasks with limited human intervention. Potential applications include managing schedules, conducting research, organizing information, generating content, and carrying out routine administrative work.
According to Nvidia, future personal computers will need significantly more processing capability to support these systems because AI agents are expected to operate continuously in the background rather than responding only when a user initiates an action.
The company's emphasis on local AI processing reflects a broader trend emerging across the technology sector. Many firms are exploring ways to move AI workloads closer to users instead of relying entirely on cloud-based infrastructure. Supporters of this approach argue that local processing can improve performance while reducing network delays and operational costs.
The commercial success of AI-powered PCs, however, remains uncertain. Although several manufacturers have promoted AI-enabled devices as the next phase of personal computing, adoption has been uneven. Some vendors have reported positive contributions to sales, while others have indicated that demand has not reached the levels initially anticipated when the category was introduced.
Technology analysts nevertheless view the market as an area with long-term growth potential. Neil Shah, co-founder of Counterpoint Research, said the shift from application-centered computing toward AI-assisted systems could fundamentally change how users interact with their devices. He suggested that personal AI agents operating on local hardware may become increasingly common as the technology matures.
During his presentation, Huang also highlighted Nvidia's Vera central processing unit, which he previously described as providing access to a market opportunity worth approximately $200 billion. Nvidia stated that organizations including OpenAI, Anthropic, and SpaceX are among the early adopters evaluating the technology.
The Computex presentation also featured discussion about the future direction of artificial intelligence across the computing industry. Qualcomm Chief Executive Officer Cristiano Amon, speaking separately ahead of the event, argued that the industry is moving beyond AI systems that simply generate responses to prompts and toward software capable of carrying out tasks independently. He described 2026 as a potential turning point for agent-based AI, adding that existing device architectures were largely designed around actions initiated by users rather than autonomous software systems.
Huang also addressed concerns that advances in artificial intelligence could reduce employment opportunities for software developers. Rejecting that view, he argued that AI tools are increasing productivity and enabling organizations to undertake larger software projects, which in turn could create additional demand for engineering talent.
The announcements come as Nvidia continues to expand its presence across multiple segments of the AI market. After becoming one of the leading suppliers of hardware for AI model training, the company is now seeking a larger role in personal computing, inference processing, and AI applications designed to run directly on consumer devices.
The developments were unveiled in Taiwan, a location Huang described as central to the global AI supply chain. The Nvidia chief, who was born in the southern Taiwanese city of Tainan, has repeatedly emphasized the island's importance to the future development and production of advanced computing technologies.
The data comes from UK Government’s Cyber Security Breaches Survey 2025, which hints that 43% of businesses and 30% of charities listed an attack or a cyber breach or attack in the past 12 months. That’s a surprising 61,000 charities and 612,000 businesses impacted.
Despite the data, businesses can lower their risk of cyber threats. But it is important to understand these key risks to stay safe and prepare for the next danger.
1. Deepfakes: Deepfakes have shifted from niche technology to a major threat. Hackers nowadays use AI-generated audio and media to mimic organization staff. This can be risky in procurement or finance, where hackers push staff to send funds, share personal data, or approve finances, where the hackers pose as business leaders.
2. Supply-chain attacks: Instead of targeting organizations directly, hackers are targeting third-party vendors to get access to various firms at once via supply-chain attacks. The attack tactic abuses trust and internal security sometimes may not address all the threats in the supply chain. One hacked vendor can prompt a domino effect throughout hundreds of businesses.
3. AI-powered phishing hacks: Phishing is one of the most common attacks in the past 12 months, and the tactic has changed significantly over the years. Most of the phishing attacks today are supported by AI tools and hackers are copying internal comms.
4. Credential stuffing attack: Weak passwords are the biggest reasons for hacks these days. In such attacks, hackers use stolen login credentials from past hacks and test them automatically across distinct platforms.
5. IoT and device flaws: As IoT is increasing, the hack surface also widens. Many devices such as sensors, cameras and industrial machinery still have limitations. Hackers abuse these flaws to access larger corporate networks. Traditional cyber security methods tend to ignore these flaws, and this has resulted in a significant risk.
6. Cloud errors: A simple thing such as exposed storage bucket or false access setting can expose sensitive data publicly accessible. These cases don’t get hacked as the information is unprotected. Currently, cloud storage environments are advanced, and building robust configuration hygiene has become a top critical priority.
Akira, the infamous ransomware gang has extorted over $250 million from businesses globally. It is now blackmailing to leak 46 GBs of data allegedly extorted from the Buffalo Convention Center. The stolen data includes financial information, contracts, employee records, and private data linked to around 1,80,000 people.
Resilience director at Gate 15, Ben Taylor has warned that ransomware gangs often boast the amount of data stolen. The alleged figure of 1,80,00 impacted people suggests data retrieved via a third-party provider, exaggerated claims to extort victims, or direct breach of venue systems.
The dark web monitoring firm Breach Sense verified the Buffalo Convention Center data breach. The FBI has classified Akira as a ransomware-as-a-service gang that extorted over $250 million from hundreds of businesses since 2023.
Convention centers, which increasingly act as repository for guest registrations, exhibitor information, payment data, contracts, and operational systems, are facing an escalating cybersecurity issue as a result of the alleged incident.
Ransomware gangs claim that they have gained access to a company in order to obtain leverage for a swift and simple payment. According to Taylor, there are situations in which these assertions are true and some that are not.
Additionally, the attack illustrates how contemporary ransomware operations have evolved. "Double extortion" is a common method used by organizations such as Akira. Before encrypting networks, they take confidential files and threaten to reveal the information if payment is not received.
According to Taylor, developments in AI are intensifying the problem by making it simpler to scale and customize phishing campaigns and other cybercrime tactics.
Buffalo Convention Center was not the only enterprise to suffer a ransomware attack.
High-case hospital hacks showcase the operational effect of a ransomware attack. According to MGM Resorts, in 2023, a cyberattack leaked personal data linked to millions of guests and impacted hotel operations for days. Another famous enterprise, Caesars Entertainment was also breached and allegedly paid $15 million in ransom to hackers.
The dangers go beyond convention centers. In April, Carnival Corporation was attacked by a gang that claims to have stolen over 8.7 million records such as dates of birth, names, and other personal data.
Recently, CySecurity reported that threat actors were using digital advertising data to attack US soldiers in war zones. The US law enforcement recently warned about the “anti-tech” extremism because the AI criticism was growing in the country.
What happens in a typical Distributed Denial-of-Service (DDoS) attack. A website that suddenly stops? Time out of a login page? Not being able to reach an online service when you need it the most? These causes are not internal, and are attributed to DDoS attacks.
Cloudflare reported stopping a 7.3 Tb/s attack last year and said it addressed a 31.4 Tb/s attack in its Q4 2025 DDoS report. According to Microsoft, Azure also blocked a 15.72 Tb/s attack last year in October. The activity was linked to the Aisuru botnet.
For all these instances, dark web actors are fighting over the same buyers with pitches. Flare experts analyzed dark web operations and detailed API access, reseller options, botnet-based capacity, monthly plans, Cloudflare bypass claims, and game-server tactics.
A comparative analysis of the DDoS-related dark web operations from the first five months of 2023 and the first five months of 2026 demonstrate how rapidly that offer has evolved. Scripts, tutorials, leaked tools, and sporadic forum posts used to be more common, but these days they are more typically provided as recurring products that are simpler to purchase and use.
A DDoS attack tries to crowd an application, network, server, or website with traffic from various servers at one time. Few attacks are aimed at network capacity, while the remaining emphasize on application layer resources like APIs and login pages. The aim is to dismantle any service or activity and make it unavailable, expensive to use, or unstable.
DDoS-as-a-service removes the barrier even further, a hacker can choose a victim, pay for accessing a web panel, select timeline, and depend on another person’s botnet, third-party attack infrastructure, or proxy network.
A hosting company that employs Magic Transit to protect their IP network and is a Cloudflare user was the target of the attack. According to Cloudflare’s recent DDoS threat assessment, DDoS attacks are increasingly targeting hosting providers and vital Internet infrastructure.
An assault campaign from January and February of 2025 that launched over 13.5 million DDoS attacks on Cloudflare's hosting providers and infrastructure was detailed by the experts on their blog.
The data will be allegedly given to government agencies. Already, privacy is a concerning issue amid rising data safety violations. Equipping buses with surveillance cameras will be unconstitutional and national-level spying of citizens in the US.
Bus Patrol, US’ leading provider of school bus stop-arm cameras has over 40,000 AI-based cameras throughout 24 states. These cameras are allowed in 30 states, and are installed on school buses, and capture images of vehicles violating traffic rules when the bus is stopped.
The footages captured by the buses are “recorded, reviewed, and submitted to local law enforcement for review and final approval,” says BusPatrol.
Stop-arm cameras claim to improve driver behaviour near school buses and student safety, but they have faced backlashes for failing on both ends. Stop-arm cameras also generate millions of dollars for businesses like BusPatrol.
Currently, the firm plans to increase its data collection, revenue, and teaming with local law enforcement by changing stop-arm camera into ALPRs, as per the leaked BusPatrol documents.
ALPR systems are run by firms such as Flock Safety. They record the license plate number of passing vehicles but unlike traffic signals or stop-cameras, ALPR "cameras photograph every vehicle that drives by and can use artificial intelligence to create a profile with identifying information that then gets stored into a massive data base,” said the Institute for Justice (I.J), a public interest law firm.
The data can be sent to law agencies which might use it for searching a vehicle or driver without requiring a legal warrant. The ALPR cameras fixed on moving school buses will help enforcement agencies to capture every moving vehicle they come across.
Without ethical enforcement, these cameras can be exploited. joshua Windham, a senior I.J. attorney, announced a nationwide campaign to oppose the uncontrolled and unconstitutional deployment of ALPR technology.
Earlier ALPR systems’ data security has come under scrutiny after cases of sharing databases with immigration agencies surfaced despite company policies forbidding it.
In Kansas, an officer used the data to trace his ex-girlfriend whereas in Texas, officers used the data to search for a woman who got an abortion. Such incidents have caused a few communities to termiate their contracts and discontinue ALPR entirely.