• Defiant warned of active exploitation of WooCommerce Wholesale Lead Capture Plugin flaw (CVE‑2026‑27540)
• Critical unauthenticated file‑upload bug lets attackers deploy PHP webshells for site takeover
• Patch released in Feb 2026 (v2.0.3.2); Wordfence blocked 100,000+ attacks, users urged to update and check uploads

A critical vulnerability in a popular WooCommerce plugin is being actively exploited to upload malware and possibly take over entire websites, security experts have warned.

The plugin in question is called Wholesale Lead Capture Plugin for WooCommerce. It adds a dedicated registration and onboarding system for wholesale and B2B customers, letting businesses collect company and other custom information, review applications, assign wholesale user roles, and automate registration and onboarding emails.

It is a premium plugin that costs between $99 and $600 and which, according to the Wordpress store page, has more than 20,000 active installations.

A thousand victims

The plugin was vulnerable to an unauthenticated arbitrary file-upload flaw which, as the name suggests, allows unauthenticated parties to upload arbitrary files, including PHP webshells and executable code that can result in full site takeover. It is tracked as CVE-2026-27540 and carries a severity score of 9.0/10 (critical).

Versions 2.0.3.1 and older were said to be affected. Version 2.0.3.2, released on February 20, was said to have addressed the bug, meaning the patch has been available for almost half a year. However, WordPress security outfit Defiant said its Wordfence web application firewall blocked more than 100,000 attacks, and Wordfence added that it observed two attack spikes - one between June 4 and 17, and another one between July 1 and August 30.

In these incidents, the attackers were mostly uploading reconnaissance webshells, possibly mapping out the landscape before deploying more serious malware.

“The uploaded shell.php is a PHP webshell that reports host details and provides a browser-based upload form for writing additional malicious files to the site,” the researchers said.

If you are running the plugin, it is advised you update it to the newest version as soon as possible, and check upload directories for unexpected, or recently created, PHP files.

Via BleepingComputer

For the past few years, Enterprise AI has largely been defined by experimentation. Organizations rushed to explore use cases, test pilot programs and give teams access to the latest models. Success metrics have often been related to adoption and speed.

Across boardrooms now, the conversation around AI is changing. CFOs are no longer asking what AI tools can do, rather they are asking what it has done, what value it has created, and whether that value justifies the growing cost of compute.

The next chapter of Enterprise AI will not be defined by who deploys the most agents or consumes the most tokens. It will be defined by who generates the greatest business outcomes from the most efficient use of compute. AI is entering its capital discipline phase.

The hidden cost of agentic AI

Many businesses are moving beyond AI chatbots and copilots to AI agents that can complete tasks, make decisions, and act with minimal human input. The business benefits can be significant, however they must be factored against cost.

To balance this consideration, companies often start small, deploying a single AI agent to support a specific process. As early results show promise, more agents are introduced across various different functions in the business, such as finance, customer service, procurement and supply chain operations.

The benefits can grow quickly, but so can the expense. Unlike traditional software, where costs are often tied to the number of users, AI costs are driven by usage - quantified by tokens (i.e., the individual blocks of data processed by AI models).

Every prompt, decision, workflow, and interaction consumes tokens. As more agents are deployed and given greater autonomy, those costs can increase rapidly. As a result, businesses need to think differently about AI investments.

Measuring impact per token

Businesses should change how they assess AI altogether. Rather than focusing on the number of tokens consumed or the cost-per-token, the emphasis should be on understanding the impact of each individual token. In other words, the business outcome created for each unit of compute consumed.

Part of the challenge is that operations do not translate neatly into a simple input-output equation. Not every action an employee takes, and not every action an AI agent takes, has an immediate impact on the top or bottom line. For example, an agent may chase a late payment or reroute a shipment, however the value often appears only when those actions are connected to the wider process.

Without operational context, the impact is very difficult to measure accurately. AI can still generate recommendations, but leaders cannot reliably see whether those recommendations improve customer satisfaction or revenue growth. This is where token waste occurs and enterprises purchase AI to rediscover information their organization already has, while struggling to distinguish useful automation from expensive activity.

Operational context also helps agents work better. When an agent understands the process it is operating within, it can make more targeted decisions with fewer prompts, fewer retries and less human correction. That means agents become more accurate, more efficient and better aligned to how the business actually runs.

The rise of token taming

As costs become more visible, AI governance has become increasingly vital for enterprises. Many are now establishing frameworks to monitor and manage AI consumption. The goal is not necessarily to reduce token usage, but add a level of accountability that didn’t previously exist; to tame an out-of-control token ogre.

CIOs and business leaders need to understand which AI initiatives are generating measurable outcomes and which are merely generating activity. That means connecting AI consumption directly to business performance indicators such as customer satisfaction, operational efficiency, revenue growth, or delivery performance.

Over time, enterprises may also develop increasingly sophisticated measures that link AI investment to economic return. The metric that ultimately matters is not tokens consumed, but the value created per token consumed.

Context is a strategic asset

Most IT assets depreciate over time. Systems become outdated, technical debt accumulates, and maintenance costs increase. Context works differently. Every business process mapped, every decision codified, and every operational relationship captured creates an asset that can be reused by future AI systems.

In this sense, context behaves less like a static data store and more like a learning loop. Each AI deployment enriches the organization's understanding of how work actually happens. For example, which approvals slow decisions or which outcomes indicate success. When that knowledge is fed back into the organization's context layer, every subsequent AI system starts from a stronger baseline rather than relearning the same patterns.

The business logic, governance structures, and operational understanding from one AI project, form the foundation for future projects, creating a compounding effect. Businesses that build and manage context can deploy new AI capabilities faster, more accurately, and at lower cost than organizations that start from scratch with every initiative.

From AI adoption to AI economics

The AI conversation is maturing. For the last few years, the focus has been on capability. Organizations have rushed to experiment with new models and explore what AI can do. The next decade will be defined by economics.

The organizations that succeed will not necessarily be those with the largest AI budgets or the latest models. They will be the ones that establish clear governance, build reusable context, eliminate unnecessary token waste, and remain focused on measurable business outcomes.

The winners will be the organizations that turn those principles into a repeatable operating pattern and practice that can be applied consistently across hundreds, or even thousands, of AI agents. Ultimately, competitive advantage will come not from using the most AI, but from using it most effectively and efficiently.

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In July 2026, an OpenAI model being tested in a security research environment broke out of its sandbox, exploited a zero-day vulnerability and autonomously compromised systems at Hugging Face, executing more than 17,000 attacker actions in under five days with no human directing the attack.

It’s the most autonomous, most damaging agentic AI attack documented to date, and it signals a significant shift from AI-assisted hacking to fully autonomous cyber operations.

It isn’t the first: Anthropic disrupted a similar Claude Code-driven espionage campaign, which it attributed with high confidence to a Chinese state-sponsored group, some ten months earlier, and Sysdig documented the first fully agentic ransomware attack just over a week before the Hugging Face intrusion began.

But it’s the starkest proof yet that autonomous AI attacks have moved from lab hypothesis to live threat. This event was a wake-up call not just for the security industry but for everyone with an online presence, and the implications are sobering.

Up until this point, discussions around AI-powered cyber threats had largely focused on how AI can help attackers work faster and at scale. As an industry, we discussed how the technology could write more convincing phishing emails, analyze larger datasets, or accelerate malware development. But the OpenAI case points to something far more troubling: AI carrying out the attack itself, from start to finish.

The old cybersecurity playbook no longer works

The OpenAI incident shows how quickly the threat landscape is evolving, and how exceptionally adept agentic AI now is at finding and exposing existing flaws. It’s a stark illustration that the very tools helping to fight attacks can also cause them. The situation shows how vulnerabilities that once may have slipped through the cracks are now becoming far easier to find and exploit.

That’s a problem for the sector as a whole. But it’s an even bigger problem for organizations that continue to overlook the basics of cyber hygiene. Despite the security industry’s best efforts, known weaknesses, unpatched systems and outdated software remain the norm rather than the exception.

Verizon’s 2026 Data Breach Investigations Report found that vulnerability exploitation overtook stolen credentials as the leading breach vector for the first time in nineteen years, accounting for 31% of breaches, while organizations patched only 26% of flaws on CISA’s Known Exploited Vulnerabilities list, down from 38% the year before. Too many organizations still treat security tools as a last line of defense rather than closing the gaps before attackers, human or artificial, get there first.

If Hugging Face’s encounter with a rogue AI agent has taught us anything, it’s that a reactive security posture is no longer sustainable. As AI becomes more capable of identifying and exploiting weaknesses at machine speed, IT leaders have far less time to detect and respond before a minor flaw becomes a major breach.

The UK’s NCSC and its Five Eyes partners said as much in their May 2026 joint guidance on agentic AI: the question every organization must now answer is whether it can understand, monitor and contain what its AI agents, and the ones attacking it, actually do.

In practice, that means shifting focus from detecting attacks to reducing the opportunities for them to succeed in the first place, building continuous visibility, effective governance and the ability to act before minor vulnerabilities become major attack paths, rather than relying on periodic checks and reactive response.

The need for real-time visibility

After all, you cannot defend what you cannot see. As IT environments become more complex, maintaining an accurate understanding of assets, vulnerabilities and configurations becomes increasingly difficult.

OpenAI’s agentic attack reinforces why this matters. If autonomous AI can identify and exploit vulnerabilities without human input and at machine speed, organizations can no longer rely on scheduled scans to understand their exposure.

Instead, real-time visibility provides a continuously updated picture of an organization's environment, allowing them to identify risks as they emerge rather than discovering them by chance and when it’s too late.

But updating technology is only half of the equation. Organizations also need strong governance so that they can understand who owns risk, how vulnerabilities are prioritized and how quickly issues are addressed.

And if attacks are being carried out by agentic AI, defenders must use AI to fight AI. Hugging Face’s own investigation shows why. When its frontier-model tools refused, on safety grounds, to help analyze the malware, the team turned to GLM-5.2, a Chinese open-weight model, running on its own infrastructure, and got the job done in hours instead of days.

That’s an uncomfortable irony for the Western AI industry, and a preview of the governance decisions security leaders will increasingly have to make. That’s why autonomous IT operations are increasingly taking center stage. Rather than replacing people, they enable teams to focus on strategic decisions while routine remediation, patching and risk reduction activities happen at the speed required to keep pace with modern threats.

Preparing for a new threat landscape

Whether OpenAI’s rogue agent turns out to be a one-off or the new normal, organizations can’t afford to wait and see. The threat landscape is evolving at machine speed, and defenders need to be ready for what’s coming next.

But organizations are not powerless. The technologies needed to improve resilience already exist; it’s now a question of adopting the right tools and using them effectively.

Going forward, businesses can no longer take a reactive approach to cybersecurity. The ones still on the back foot when the next agentic AI attack lands will be the ones counting the cost.

The OpenAI incident shows that the age of AI-assisted attacks is rapidly giving way to something more sophisticated. The direction of travel is clear, and the days of relying on security tools as a last line of defense are over. It’s time to get on the front foot and close the gaps before attackers, human or artificial, find them first.

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For the past few years, the spotlight has been firmly on AI models.

Organizations have poured investment into generative AI, machine learning platforms and large language models. New capabilities appear almost weekly. Yet despite all this progress, a familiar question keeps surfacing in boardrooms and leadership meetings – where is the business value?

Part of the answer is that the technology itself is no longer the main obstacle. Powerful AI tools are now within reach of most organizations. What remains difficult is turning AI-generated insights into decisions and measurable business outcomes.

This is the last mile of AI, where many projects lose momentum. A model may produce a recommendation in seconds, but acting on it is often far more complicated. Data may be incomplete, critical context may sit elsewhere, and governance teams may not be confident in the output.

As a result, many organizations find themselves investing heavily in AI while struggling to move beyond pilots and proofs of concept. The missing piece is often the ability to connect intelligence to the reality of how the business really operates.

The real AI bottleneck

When executives talk about successful AI programs, they rarely focus on the sophistication of the model. What matters is the outcome. For example, whether fraud losses fall, if customers can be onboarded faster, or if downtime is reduced. Those outcomes depend on much more than AI itself.

For AI to create value, it needs a clear view of what is happening across the business, enough context to understand what those events mean, and the ability to operate within governance guardrails. When any of those elements are missing, recommendations become harder to trust, automation stalls, and adoption suffers.

Many organizations discover that the real bottleneck sits between the model and the business process. Data is fragmented across systems, and essential context is missing. Often, information arrives too late to be useful. Different teams define the same business concepts in different ways. These are not new problems, but AI has exposed them more clearly than ever before.

We see this challenge regularly in financial services. One institution had invested heavily in AI-driven fraud detection, yet investigators still struggled to act quickly because customer records, transaction histories, and external fraud signals were spread across multiple systems. Once those sources were brought together in a trusted, governed view, fraud losses fell, false positives declined, and onboarding processes became more efficient.

AI needs a broader view of the business

One reason many organizations struggle to get AI into production is that the technology depends on a much broader view of the business than traditional analytics ever did.

Most companies have spent years investing in ERP platforms, CRM applications, and operational databases. These systems remain the authoritative record of the business and provide much of the information AI needs to work effectively.

To make good decisions, AI increasingly needs information from outside those core systems. It may need supplier data to understand a disruption in the supply chain. It may need information from partners, SaaS applications, or external intelligence sources to provide business context. Increasingly, it also needs access to live operational signals, whether that's customer interactions, fraud alerts, connected devices, or event streams that reveal what's happening right now.

You can think of this as three layers of information:

• The first is the authoritative data held within core enterprise systems.
• The second is contextual information from partners, suppliers and external sources that helps explain why events are occurring.
• The third is real-time operational awareness, the signals that show what is happening in the moment.

Most organizations have made significant progress managing the first layer. Far fewer have found an effective way to combine all three. A global manufacturer we worked with faced a similar challenge.

Its AI models could predict equipment failures, but critical information about production schedules, supplier delays, and maintenance activities sat across different systems. Connecting those sources gave teams the context needed to identify risks earlier, reduce downtime, and make better operational decisions.

As a result, AI often operates with an incomplete picture of reality. It may understand what happened yesterday, but not what is happening now. It may have access to internal records, but lack the external context needed to make a confident recommendation.

This is where the last mile challenge starts to emerge.

The ecosystem effect

The rise of AI is also changing how organizations think about data sharing. For years, most businesses focused primarily on their own internal systems. Today, many of the decisions that matter depend on information that sits beyond organizational boundaries.

A manufacturer responding to supply chain disruption needs visibility into suppliers and logistics partners. A bank trying to detect fraud benefits from external signals as much as internal customer data. Public sector organizations often need information from multiple agencies to improve services and outcomes.

The common thread is that intelligence increasingly flows across an ecosystem rather than a single enterprise. Trusted AI outcomes depend on trusted access to data wherever that data resides.

Technology leaders therefore face a balancing act: making data accessible across distributed environments while maintaining governance, security and ownership. Without that foundation, even the most advanced models struggle to deliver consistent results.

Closing the last mile

If there is one lesson emerging from the first wave of enterprise AI adoption, it is that success depends less on deploying another model and more on creating the conditions that allow AI to operate effectively.

That starts with trusted data. Business users need confidence that the information feeding AI systems is accurate, up to date, and governed consistently. The challenge is also making it available in a way that AI can use without creating new silos or adding more complexity. AI needs access to core business systems, external ecosystem data, and real-time operational signals. If those remain disconnected, the quality of the output will always be limited.

Why understanding matters

Data alone, however, rarely tells the full story. AI needs to not only understand the data itself, but the business context around it. For example, where it came from, how it relates to other information, and the policies that govern its use. That context is often what separates a useful recommendation from a misleading one. Without it, even accurate data can lead to poor decisions.

This is where active context comes in. It provides AI with the richer understanding needed to interpret information correctly, connect it to the wider business environment, and make decisions that are grounded, relevant, and well governed.

Turning insight into action

The final step is where many organizations still struggle. Insights need to become actions.

Too often, AI generates recommendations that sit in dashboards, reports or isolated applications. The greatest value comes when intelligence is embedded directly into operational workflows, helping people make decisions faster or automating routine processes altogether. That is where AI starts to move beyond experimentation and deliver measurable business outcomes.

For technology leaders, a useful test is to ask a handful of simple questions.

• Can AI access information as events happen, or is it relying on yesterday's data?
• Can it combine internal and external sources to build a complete picture?
• Are governance policies applied consistently across different environments?
• Do people trust the outputs enough to act on them?

If the answer to any of those questions is no, the next investment should probably be focused on the data foundation rather than the model itself.

Where the next gains will come from

The organizations generating the greatest returns from AI are not necessarily those deploying the newest technology. More often, they are the ones that have found a way to connect trusted data, business context, and operational processes.

In banking, that might mean reducing fraud losses while accelerating onboarding. In manufacturing, it could mean identifying disruptions earlier and reducing downtime. In the public sector, it may involve improving citizen services through better collaboration across departments. In each case, the value comes not from the model itself, but from embedding intelligence into operational decisions and workflows.

That is the last mile of AI. For many organizations, it remains the biggest barrier to success. It may also be the biggest opportunity.

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• Elastic Security Labs uncovered REF9334, a Brazilian banking malware campaign active since May 2025
• Malware “Kremlin” deploys fake docs and malicious Chrome/Edge extensions to steal banking data
• 1,515 infections found, 98% in Brazil

Security researchers from Elastic Security Labs have discovered a new Brazilian banking malware campaign that uses browser extensions to compromise users and steal sensitive information.

In an in-depth report published earlier this week, the researchers said the campaign has been active since at least May 2025. Dubbed REF9334, the campaign uses fake banking, invoice, and business documents, to trick victims into installing malware which, in turn, deploys a malicious extension in Chrome and Edge browsers.

The researchers named the malware “Kremlin”, and say it can steal browser credentials, cookies, session information, monitor browser activity, grab screenshots, and steal information from websites that the victims visit. But the goal of the campaign is primarily to target Brazilian bank users.

A thousand victims

The malware really makes an effort to hide and persist in the target environment. For example, it first checks to see if it’s in a sandbox and if so - it simply won’t run. If instead it determines that it’s running on a real user’s computer, it will deploy an extension with the name “AVSync System Inc.” in an attempt to trick the victim into thinking they have an antivirus addon running in the browser.

It also doesn’t use a fixed C2 server, but rather stores the information on the Ethereum blockchain, since it’s a lot harder to disrupt the communication between the operators and the infected machines that way.

During their investigation, Elastic researchers were able to take control of a domain that the malware used and discovered that it had infected 1,515 systems. Almost all of them (98%) were located in Brazil. They were also able to register the network canary domain and point it to their webhost, which resulted in the loader assuming it was in a sandbox. This also meant “the infections have not moved past the initial access”, Elastic explained.

The full list of indicators of compromise can be found on this link.

Via The Hacker News

In the span of just over two weeks this summer, three of the world's most closely watched AI developers admitted the same uncomfortable thing. Their own models broke out of the sandbox and touched systems they were never supposed to interact with.

OpenAI disclosed on July 21 that models it was evaluating exploited a vulnerability and compromised production infrastructure at Hugging Face, an incident the company said was driven end-to-end by an autonomous agent with no human directing it.

Days later, Anthropic said three of its Claude models, including Opus 4.7 and its newest Mythos 5, had accessed and compromised the systems of three outside organizations during cybersecurity testing exercises, after a misconfiguration left the models connected to the open internet when they had been told they weren't.

And on August 5, Meta confirmed its Muse Spark 1.1 model breached an unnamed company's systems under strikingly similar circumstances.

A pattern, not an anomaly

At the current pace, this isn't a rare event security teams can plan around once a year. It's becoming a recurring line item. Notably, Anthropic and Meta's incidents traced back to the same third-party evaluation partner, and in Meta's case, the model's cyber risk had already been assessed as no higher than moderate before the very testing process meant to confirm that assessment ended up breaching a real company.

That detail matters as it shows the failure point isn't just the model. It's the surrounding scaffolding of evaluations, permissions, and network paths that organizations assume is contained until it isn't.

This should be viewed as an early warning for organizations about autonomous systems moving from content generation into action execution. The practical lesson, now repeated three times over, is that advanced AI systems can behave in harmful or unexpected ways even when the original goal is not malicious, especially when they are given tools, network paths, credentials, and incentives to complete a task at any cost.

For companies, the takeaway is not to halt AI adoption. It's to treat agentic AI as a new class of privileged workload that requires containment, observability, and enforceable runtime controls.

Govern agents like high-risk digital workers

That starts with AI agent identity management. Companies should double down on this discipline and be very deliberate about what agents are allowed to access and do. Each agent should have a unique identity, scoped permissions, short-lived credentials, and clear ownership, so organizations can trace actions back to a specific system, use case, and accountable business owner.

Access should be limited by default, with explicit approval gates for higher-risk activities such as internet access, code execution, credential retrieval, data movement, or changes to production systems.

In practical terms, organizations should govern AI agents like high-risk digital workers: least privilege by default, separation between test and production environments, detailed logging of tool use and system interactions, and a kill switch that security teams can trigger the moment behavior deviates from policy.

Prevention, monitoring, and the road ahead

Prevention also requires moving beyond traditional application security testing. Organizations should red-team agents against realistic misuse paths, including prompt injection, tool abuse, lateral movement, credential harvesting, data exfiltration, and attempts to bypass sandbox restrictions. They should also continuously monitor agents for harmful impacts, not just technical failures.

That means watching for unauthorized access attempts, unusual tool-chaining behavior, unexpected data movement, policy violations, and actions that could create operational, security, privacy, or reputational harm. Periodic audits should review agent permissions, identities, logs, business justification, and actual behavior to confirm that each agent is still operating within its intended purpose and risk tolerance.

Will this become a trend? With three disclosures in seventeen days, that question is close to settled. Autonomous agents will increasingly be able to discover, combine, and exploit weaknesses faster than traditional security processes can respond.

The risk is not simply “AI hacking AI.” It's autonomous decision-making operating inside complex digital ecosystems where one model, plugin, dataset, API, or identity path can become the bridge into another environment, exactly what played out at Hugging Face, inside Anthropic's testing environment, and now at Meta's.

The companies that will be best positioned are those that pair AI innovation with disciplined identity management, access limitation, continuous monitoring, and routine audit practices, rather than treating each new disclosure as an isolated incident to react to after the fact.

The pragmatic message for executives, especially as this list of companies keeps growing, is that agentic AI can create significant business value, but only if autonomy is matched with accountability, containment, and operational guardrails.

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• Salesforce CEO Marc Benioff calls the SaaSpocalypse "crazy nonsense"
• Instead, he predicts a new kind of software interface will make all the difference
• Nvidia CEO Jensen Huang agrees, saying the end of software isn't nigh

Salesforce CEO Marc Benioff has hit out at reports AI technology will lead to a "SaaSpocalypse", saying traditional software still has life in it yet.

Speaking at the opening keynote of Dreamforce 2026, Benioff said the talk of a SaaSpocalypse over the last six months was "crazy nonsense".

Instead, Benioff said the market trends would actually bring in a new age of software - one where AI would be playing a key role.

A new kind of interface

"Hasn't it been wild?" Benioff asked the Dreamforce crowd.

"But I think we realized that SaaSpocalypse - it's not about the end of software, but it may be about the end of software that makes humans do all the work."

Namely, this refers to AIforce, the company's new realization of the software interface, to reflect the needs of the AI age.

Benioff noted that over the past few decades, users have gone from Command Line interfaces to GUI, from web to mobile - and now, with AI assuming a much greater role in the world of work, there's a need for a new kind of interface.

AIforce looks to address this by enabling agents anywhere to reason and take action across all the data, workflows, and logic inside Salesforce, hopefully unlocking whole new ways of working for customers everywhere.

Benioff wasn't alone in his belief that this isn't the end of software - later in his keynote, he welcomed Nvidia CEO Jensen Huang on stage.

This was primarily to discuss Salesforce's new Koa CRM, developed alongside Nvidia, but the talk took a bit of a manic turn, as the two billionaires instead decided to pace about the theatre and have a discussion about the wider technology industry.

"Don't give up on the technology," Huang said, "You've heard me say it - I was the first CEO to come out not in the software industry to say the end of software is nonsense."

"(Instead) This is going to be a layer on top of sotware, this new layer is gojng to be agentic, and it's going to make the software so much better."

• Salesforce has revealed Ace, a new AI agent for the TSA
• Ace should take the pressure of answering basic questions off human agents
• Ace has already resolved 96% of basic inquiries so far

Salesforce has built the Transportation Security Administration (TSA) a new AI agent to try and help streamline travel for passengers everywhere.

Ace, a new AI agent built with Salesforce's Public Sector Solutions platform, has been live for several months now, helping the TSA deal with traveller requests and inquiries.

So far, Salesforce says Ace has already handled approximately 100,000 routine traveler conversations per month and resolved 96% of routine inquiries, such as how to pack liquids or enter a checkpoint with a medical device — without the need for human escalation.

TSA and AI

“Nearly three million people travel through US airports every day, and a simple question shouldn’t stand between them and a smooth trip,” said Kendall Collins, CEO of Missionforce and Government Cloud.

“TSA’s new Ace AI agent gives travelers fast, accurate answers before they reach the checkpoint—from what they can pack to how they can prepare—while helping TSA officers stay focused on their essential security mission.”

The launch of Ace forms part of a broader modernization initiative, led by the TSA’s Customer Experience Branch to improve traveler service using Salesforce.

The amount of questions from travellers can ramp up hugely during peak travel periods and disruptions, so the TSA needed a flexible way to scale support, deliver consistent guidance, and reduce pressure on its contact center teams.

Deploying a secure, autonomous AI agent like Ace, which can answer routine questions and direct more complex needs to staff, means the TSA can improve services, control costs, and support its missions at scale, even at the busiest times of the year.

“The TSA is leading with trusted AI to meet the mission,” said Paul Tatum, EVP of Global Public Sector Solutions at Salesforce. “They are delivering a secure, data-driven, and AI-first foundation that scales operations, lowers costs, and protects public trust, even during the most stressful travel times.”

• Poland's new loitering munition borrows stealth aviation's shape for practical battlefield reasons
• A turbojet gives Gladius 2 a different propulsion approach from earlier Warmate variants
• The 30kg warhead places Gladius 2 between several established Warmate configurations

Polish defense manufacturer WB Group has revealed a new loitering munition whose flattened, wing-shaped body echoes the American B-2 Spirit bomber.

Gladius 2, also called Warmate 30, uses that shape mainly to cut down on radar and heat signatures rather than for looks.

Flying wing designs scatter radar waves more efficiently than boxy airframes, which is exactly why stealth bombers adopted the same silhouette decades ago.

A shape borrowed from stealth aviation, not aesthetics

The munition runs on a turbojet engine, giving it a faster run toward its objective than earlier electrically powered Warmate versions.

It carries an interchangeable 30 kg (66 lb) warhead and can launch from a shipping container or a dedicated vehicle.

WB Group designed the weapon to operate within its wider Gladius reconnaissance strike system rather than as a standalone munition alone.

That system pairs the FT-5 Łoś reconnaissance drone with separate strike effectors, all coordinated through a software platform called Topaz.

This arrangement lets one operator locate an objective using the reconnaissance aircraft, then destroy it with a separate strike weapon.

According to the company, it started developing this newer variant back in September 2022, drawing lessons from the ongoing conflict in Ukraine.

Poland is already investing heavily in the drone family

The new weapon arrives as Poland continues spending heavily on domestically produced unmanned systems and loitering munitions.

In May 2026, Poland's Armaments Agency signed agreements covering more than 400 Warmate systems and nearly 200 FlyEye reconnaissance drones.

The same contracts included 12 Gladius fire modules, with financing partly supplied through the European Union's SAFE defence lending programme.

WB Group has not confirmed whether Gladius 2 or Warmate 30 forms part of those existing procurement agreements.

That distinction matters because the newly shown aircraft could remain a future offering rather than an immediately contracted Polish military system.

WB Group has previously described several Warmate variants using payload categories, including models associated with 5kg, 20kg, and 50kg warheads.

Warmate 30 therefore fits between those larger and smaller configurations while carrying a 30kg payload of its own.

The earlier Warmate 3 reportedly carries a smaller warhead while reaching distances of up to 70km, or 43 miles.

The original Gladius system has also been offered with earlier electrically powered Warmate variants capable of exceeding 100km, or 62 miles.

However, those figures should not be treated as specifications for Gladius 2 until WB Group releases its own performance data.

Whether Gladius 2 performs as well as its shape suggests will remain unclear until WB Group releases actual test figures.

This bomber simply copies a proven stealth silhouette, which makes practical sense for a country expanding its arsenal under real wartime pressure.

Via Defence Blog

• Dell Technologies held the 14th edition of its parent-and-child PC assembly class at its Miyazaki Customer Center,
• where children learned how to build a 14-inch Dell laptop from nine parts and 23 screws
• Entries cost 113,275 Yen (approximately $730) and included keeping the finished laptop with a Core 5 CPU, 16GB of RAM, and a 512GB SSD, along with a year of on-site service

On the fifth floor of a building in central Miyazaki that once housed a department store, seven children spent a Saturday afternoon doing something almost no laptop owner will ever do.

They learned how to build a working Windows 11-based laptop from start to finish using spare parts on a tray that included nine different components and 23 screws.

The exercise concluded when they powered on their laptops, checked for a Wi-Fi connection, and were allowed to take the self-assembled laptops home.

An exercise focusing on reliability, self-service and promoting Dell's brand image

The event was Dell's 14th parent-and-child PC assembly class, who were given step-by-step access to the entire process. The event, which drew 14 parents and children, was divided into 6 groups, with one group including two children.

They were tasked with assembling a 14-inch laptop (Dell 14 DC14250) with an Intel Core 5 120U CPU, 16GB of RAM, and a 512GB SSD. The event focused on understanding the underlying hardware and how it worked once assembled, while underscoring the computer giant's focus on repairability and reliability.

Masashi Hayashida, head of Dell Technologies' Miyazaki Customer Center, stated during the event (translated): "Computers can do so many things. I want people to use their finished computers until they break down and get to know them completely. If they break down, they can be fixed. Technology is constantly evolving. I hope you will look forward to the future advancements in technology."

The event included an office tour, and Hii-kun, one of the three Miyazaki-ken prefectural mascot dogs, turned up and performed a dance routine as part of the event. Participants also received a brief history of how Dell Technologies started in 1984 and grew into the monolithic giant it is today, with a presence in over 170 countries, starting with only a $1,000 investment from its founder, Michael Dell.

The exercise, a compressed version of what a Dell service technician does for a living, included plugging in memory and attaching the display, speaker, keyboard, SSD, battery, and Wi-Fi modules.

The children also received a brief overview of AI, how it is important, and that it can make mistakes and should not be given access to personal information beyond what is necessary. University interns at the center followed up by running a ten-question true-or-false quiz on Dell, Intel, and PCs.

The event also featured Yoko Oishi of Dell's advanced technical support team demonstrating how to replace a USB-C module on a Dell XPS laptop to invited members of the press.

The same company that is making record profits building AI data center racks spent a Saturday afternoon teaching children how a laptop is assembled and works, making for a nice change of pace that doubles as a great marketing and public service play for a conglomerate that still makes a significant amount of its revenue from consumer electronics, indicating that it is still committed to the 'personal' side of computing.

• Sam Altman admits AI development may require greater control
• Altman references Hugging Face "accident" as something which might cause wider distrust
• However Altman said he still believes AI technology can greatly benefit humanity as a whole

OpenAI CEO Sam Altman has admitted that the rapid pace of development in the AI industry is rightfully scaring some people, and that more controls might be necessary.

Speaking at Dreamforce 2026, Altman was quizzed by Salesforce CEO Marc Benioff on a variety of topics, giving his opinion on not just the most recent incidents in the world of technology, but where the future of AI could go next.

"This could go wrong"

Altman and Benioff's wide-ranging conversation began with the latter asking the former for his view on "what's been going on" concerning recent controversies in the AI industry.

This included the widely-reported Hugging Face hack, as well as increasing calls for greater AI regulation in recent weeks.

"People have been talking about the potential downsides or serious risks of AI for some time,” Altman said, noting that the increased capabilities of models have meant people are paying more attention than ever before.

Altman also highlighted the recent “Hugging Face accident”, which he later described as “obviously a terrifying incident”, as raising awareness among the wider world as to what AI is capable of.

“It doesn't take as much imagination as it used to, to imagine how this could go wrong,” he pointed out, adding that he would like to know that companies don’t intend to use their models for nefarious means - “there should be no qualifier on that,” he pointed out.

Altman also highlighted the “real fear” that “some companies working in AI could get too much power and be able to influence the economy”.

“I think the world is right to be afraid of this,” he pointed out.

Future hopes

However it wasn’t all doom and gloom, as Altman noted that OpenAI was navigating a “narrow path” with pragmatism, steadiness and consistency.

“People are now saying, this seems pretty important, so let's get it right...and we will get it right, by the way, I'm very confident in our ability to do this. The world should trust that we are going to do the right thing, because it is the right thing, and we know the magnitude of this.”

Altman was also confident that despite the continuing advances of AI, he had high hopes for the future of humanity, noting that while "people don't care that much what a machine does...we have a wonderful ability of being obsessed with other people.”

However this might still involve some tough work, as society and people would “need to uplift themselves very fast” to get all the advantages of technology - “but this seems very achievable to me,” Altman added.

Looking forward

Overall though, Altman noted that the degree that people care about other people is a major positive, and predicted that "we will stay in an extremely human-centric world, no matter what technology does.”

AI will help with this progress, though, with Altman saying that although the technology is capable of incidents such as the Hugging Face hack, it is also possible to use AI to defend against such attacks.

“We have to (use AI to defend) to get the prize of curing diseases, giving people this incredible creativity, this human entrepreneurship that I think is coming.”

“I think people, with this technology in their hands, can live a much better version of their lives.”

Benioff closed out the session by asking what he hoped the legacy of his company would be, with Altman noting that the company had set out on an "insane mission" to achieve AGI, and that it had only scratched the surface" of building great products".

“There is no doubt there will be more technological wonders by 2030, and they'll be big ones,” he added, highlighting the fact that you can talk to a computer and it knows what you want, understand your company and take action, “is one of the most profound changes to how we use technology that I've ever seen...this is one of the moments that feels like you're in a movie (and) I think this will transform the way we work and use computers”.

• Project Zenith adds developer tools before users have to install them manually
• The required 64GB memory immediately pushes Zenith beyond ordinary Windows hardware
• Microsoft wants Zenith machines ready for local AI development

Microsoft has unveiled Project Zenith, introducing specially configured Windows 11 machines aimed at developers and local artificial intelligence work.

The idea sounds practical until you consider the hardware required, because Zenith begins with computers that already sit firmly above mainstream prices.

That makes the project feel less like Windows optimization and more like an attempt to give expensive computers another reason to exist

Microsoft is creating another category for expensive PCs

Zenith is not a separate Windows edition, despite how Microsoft's announcement could initially make the concept sound.

Instead, Microsoft is changing the default software configuration and hardware requirements for computers intended for people developing applications and AI systems.

Eligible machines need at least 64GB of unified memory and 250GB/s of memory bandwidth before receiving the Zenith configuration.

Microsoft also wants these systems capable of running models exceeding 30B parameters locally without metered usage restrictions, according to its announcement.

Windows Terminal and Visual Studio Code come pinned, while GitHub Copilot, PowerToys, WinAppCLI, and Windows Dev Skills arrive preinstalled.

File Explorer also starts with extensions, hidden files, complete paths, and other settings that developers might otherwise configure themselves.

Microsoft describes the objective as providing a “ready-to-code distraction-free Windows experience” for people using developer-class hardware.

That sounds sensible for professionals who repeatedly configure similar machines, especially when those machines are deployed across larger development teams.

However, most ordinary Windows users have little reason to want these settings or applications occupying their computers from the beginning.

The expensive machine gets another reason to sell

The timing makes the strategy particularly interesting because Microsoft is effectively attaching specialized software treatment to increasingly costly hardware.

This resembles the way manufacturers increasingly describe premium laptops as AI or creative machines, even when professionals remain their obvious buyers.

Zenith appears to take that familiar approach further by defining a software experience around hardware specifications that most consumers will never require.

The first systems will use AMD Ryzen AI Halo processors, with other platforms expected to receive support during subsequent months.

That requirement immediately narrows the field because 64GB of unified memory is far beyond what typical Windows laptops currently provide.

There is nevertheless a straightforward benefit for developers because several hours of repetitive setup can disappear when the computer starts ready for work.

Microsoft's configuration also does not lock these conveniences behind Zenith hardware, since existing Windows 11 installations can receive many changes manually.

That creates an awkward question about why someone should purchase an expensive Zenith machine when much of its software setup remains reproducible.

For companies managing many developer computers, the standardized configuration could save administrative time and reduce repeated setup work across machines.

For an individual buyer, however, the financial argument becomes considerably harder when the main advantage involves settings that require little effort manually.

Microsoft may therefore be selling convenience to people who already have compelling reasons to buy powerful hardware in the first place.

That makes Zenith look less like an entirely new Windows experience and more like a carefully packaged justification for expensive developer computers.

Via Windows Central

• Nvidia CEO Jensen Huang and Anthropic CEO Dario Amodei discuss AI at Dreamforce 2026
• Amodei says the pace of progress had surprised Anthropic
• Huang calls for greater safety, but hails the advantages AI can bring

Some of the world's leading AI minds have shared their opinions on the future of the technology, revealing some intriguinly diverging viewpoints.

Nvidia CEO Jensen Huang and Anthropic CEO Dario Amodei were both guest appearances during the opening keynote of Dreamforce 2026, where they were both quizzed by Salesforce CEO Marc Benioff on their opinions and views on the future of the technology.

The pair had some interestingly differing viewpoints, showing the trouble facing not just internal and external stakeholders, but also lawmakers and regulators.

Jensen vs Dario?

First up was Amodei, whose Claude platform has become increasingly partnered with Salesforce in recent months, culminating in the recent launch of Claudeforce.

Asked by Benioff about the state of the AI market right now, Amodei likened it to the motor industry, where safety records can be conflated, but companies can learn from each other - and he also seems keen to open things up internationally to set standards.

“I think that’s the way to lead the industry forward, to set an example, to say that everyone can always be better,” he pointed out.

Benioff also asked Amodei about the biggest surprises has had while developing his technology over the last decade.

The Anthropic head replied the "pace of the progress" - not just in the development of AI technology, but the economic impact of it too - noting his company didn't appreciate how quickly certain companies would grow and become central to everyday life.

Finally, Benioff asked what actions Amodei would like the public to take when using AI.

Amodei replied that even if development of AI technology was frozen right now, we'd only be using maybe 5-10% of the possible value we could get.

Making use of all the information with platforms such as Claude could open up new areas of business - "there's so much diffusion left," Amodei says.

Huang was much more positive in his appearance, leading Benioff in a walkabout around the keynote theatre as the pair discussed AI.

"We're going through a new industrial revolution," Huang pointed out,

However he did urge caution in some areas, particularly around safety.

“Safety is paramount in a lot of ways. It’s job one,” Huang said. “However, safety is an engineering problem.”

Speed and safety are not mutually exclusive, Huang noted, pointing out that companies should pace themselves until they are sure they are releasing something that the market will appreciate.

“It’s a false choice,” Huang told Benioff. “You could definitely have both at the same time.”

When it comes to model protections, Huang said that companies should, “run as fast as you can...but if you feel at any given point in time the company’s out of control or the product’s not going to be safe, take a pause and make sure you get it right.”

Wrapping up, Benioff asked Huang about his vision for the next few years - and what he wants to see from Salesforce.

Huang says he wants to "Agentforce every company", outlining an aim of "really deploying AI".

"You don't want to be left behind, this is too important of a technology revolution," Huang noted, "if it doesn't work for you straight away, don't give up on it."

"The most important thing - don't get left behind...the sky's the limit for us."