A critical vulnerability in the Junos OS Evolved network operating system running on PTX Series routers from Juniper Networks could allow an unauthenticated attacker to execute code remotely with root privileges.

PTX Series routers are high-performance core and peering routers built for high throughput, low latency, and scale. They are commonly used by internet service providers, telecommunication services, and cloud network applications.

The security issue is identified as CVE-2026-21902 and is caused by incorrect permission assignment in the ‘On-Box Anomaly Detection’ framework, which should be exposed to internal processes only over the internal routing interface.

However, the glitch allows accessing the framework over an externally exposed port, Juniper Networks explains in a security advisory.

Because the service runs as root and is enabled by default, successful exploitation would allow an attacker who is already on the network to take full control of the device without authentication.

The issue affects Junos OS Evolved versions before 25.4R1-S1-EVO and 25.4R2-EVO, on PTX Series routers. Older versions may also be impacted, but the vendor does not assess releases that have reached the end-of-engineering or end-of-life (EoL) phase.

Versions before 25.4R1-EVO, and standard (non-Evolved) Junos OS versions are not impacted by CVE-2026-21902. Juniper Networks has delivered fixes in versions 25.4R1-S1-EVO, 25.4R2-EVO, and 26.2R1-EVO of the product.

Juniper’s Security Incident Response Team (SIRT) states that it was not aware of malicious exploitation of the vulnerability at the time of publishing the security bulletin.

If immediate patching is not possible, the vendor’s recommendation is to restrict access to the vulnerable endpoints to trusted networks only using firewall filters or Access Control Lists (ACLs). Alternatively, administrators may disable the vulnerable service entirely using:

'request pfe anomalies disable'

Juniper Networks products are typically an attractive target for advanced hackers as the network equipment is used by service providers requiring high bandwidth, such as cloud data centers and large enterprises.

In March 2025, it was revealed that Chinese cyber-espionage actors were deploying custom backdoors on EoL Junos OS MX routers to drop a set of ‘TinyShell’ backdoor variants.

In January 2025, a malware campaign dubbed ‘J-magic’ targeted Juniper VPN gateways used in the semiconductor, energy, manufacturing, and IT sectors, deploying network-sniffing malware that activated upon receiving a “magic packet.”

In December 2024, Juniper Networks Smart routers became targets of Mirai botnet campaigns, getting enlisted in distributed denial of service (DDoS) swarms.

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This is the first Galaxy S26 Ultra deal that actually feels worth talking about.

Amazon has the Samsung Galaxy S26 Ultra 512GB pre-order bundle for $1,299.99, and it comes with a $200 Amazon gift card. That alone is a strong launch offer, but the real reason this stands out is the storage angle: this promo gives you double the storage without forcing you to pay the usual premium for it.

That matters because flagship phone deals at launch are usually underwhelming. You might get a tiny credit, maybe a trade-in boost, and that’s it. This one is better. You’re getting the 512GB model, not the base-tier version, and Amazon is still sweetening it with store credit. If you were already planning to buy the S26 Ultra, this is the version to get.

What you’re getting

This bundle is for the unlocked Galaxy S26 Ultra 512GB. It’s Samsung’s newest top-end phone, so you’re getting the full flagship treatment: Privacy Display, Galaxy AI, AI camera features, Super Fast Charging 3.0, and the latest Ultra-level hardware.

More importantly, you’re getting the storage tier most people should buy anyway. On a phone like this, 512GB just makes more sense. Cameras are better, video files are bigger, AI features are heavier, and people keep their phones longer now. The base storage option always looks fine on paper, then starts feeling tight much sooner than you expect.

Why it’s worth it

This is a confident, easy recommendation because it’s genuinely a good deal if you were already planning on pre-ordering the S26 Ultra.

You aren’t just getting a bonus gift card. You’re also avoiding the usual upcharge for more storage. That means Amazon is solving the two biggest launch-day problems at once:

• Flagship phones cost too much
• The better storage tier usually costs even more

Here, the 512GB model is the obvious choice, and Amazon is making that choice easy. The $200 gift card is a great addition too. If you already buy from Amazon, that’s real value back in your pocket. Put it toward accessories, earbuds, a case, chargers, or just treat it like a straight offset against the cost of the phone. Either way, it makes this launch price a lot easier to swallow.

The bottom line

If you want the Galaxy S26 Ultra, buy it this way. The phone is brand new, the 512GB model is the one you actually want, and Amazon is pairing it with a $200 gift card while the pre-order offer is live. That is a better-than-usual launch bundle, full stop.

Apple has begun rolling out OS-level age verification to users in the UK, starting with the latest iOS 26.4 beta.

After installing the update, some users are prompted to confirm they’re over 18 (via The Verge). Apple warns that those who don’t verify their age may be unable to download apps, make purchases, or complete in-app transactions.

Screenshots shared by beta users show Apple explaining that it may automatically confirm someone’s age using the payment method linked to their Apple ID or existing account information. If that isn’t possible, users could be asked to scan a credit card.

[Matt Denton]’s SpoolBot is a surprisingly agile remote-controlled robot that doesn’t just repurpose filament spool leftovers. It looks exactly like a 2 kg spool of filament; that’s real filament wound around the outside of the drum. In fact, Spoolie the SpoolBot looks so much like the real thing that [Matt] designed a googly-eye add-on, because the robot is so easily misplaced.

SpoolBot works by rotating its mass around the central hub, which causes it to roll forward or back. Steering is accomplished by tank-style turning of the independent spool ends. While conceptually simple, quite a bit of work is necessary to ensure SpoolBot rolls true, and doesn’t loop itself around inside the shell during maneuvers. Doing that means sensors, and software work.

To that end, a couple of rotary encoders complement the gearmotors and an IMU takes care of overall positional sensing while an ESP32 runs the show. The power supply uses NiMH battery packs, in part for their added weight. Since SpoolBot works by shifting its internal mass, heavier batteries are more effective.

The receiver is a standard RC PWM receiver which means any RC transmitter can be used, but [Matt] shows off a slick one-handed model that not only works well with SpoolBot but tucks neatly into the middle of the spool for storage. Just in case SpoolBot was not hard enough to spot among other filament rolls, we imagine.

The googly-eye add-on solves that, however. They clip to the central hub and so always show “forward” for the robot. They do add quite a bit of personality, as well as a visual indication of the internals’ position relative to the outside.

The GitHub repository and Printables page have all the design files, and the video (embedded just below) shows every piece of the internals.

The kind of hardware available nowadays makes self-balancing devices much more practical and accessible than they ever have been. Really, SpoolBot has quite a lot in common with other self-balancing robots and self-balancing electric vehicles (which are really just larger, ridable self-balancing robots) so there’s plenty of room for experimentation no matter one’s budget or skill level.

For the last six months, enterprises wanting to deploy high quality AI image generation at scale have faced an uncomfortable trade-off: pay premium prices for Google’s Nano Banana Pro model, or settle for cheaper (sometimes free), faster, but noticeably inferior alternatives — especially in terms of enterprise requirements like embedded accurate text, slides, diagrams, and other non aesthetic information.

Today, Google DeepMind is attempting to collapse that gap with the launch of Nano Banana 2 (formally Gemini 3.1 Flash Image) — a model that brings the reasoning, text rendering, and creative control of the Pro tier down to Flash-level speed and pricing.

The release comes just sixteen days after Alibaba’s Qwen team dropped Qwen-Image-2.0, a 7-billion parameter open-weight challenger that many developers argued had already matched Nano Banana Pro’s quality at a fraction of the inference cost.

For IT leaders evaluating image generation pipelines, Nano Banana 2 reframes the decision matrix. The question is no longer whether AI image models are good enough for production — it’s which vendor’s cost curve best fits the workflow.

The production cost problem: why Nano Banana Pro stayed in the sandbox

When Google released Nano Banana Pro in November 2025, built on the Gemini 3 Pro backbone, the developer community was impressed by its visual fidelity and reasoning capabilities.

The model could render accurate text in images, maintain character consistency across multi-turn conversations, and follow complex compositional instructions — all capabilities that previous image generators struggled with.

But Pro-tier pricing created a barrier to deployment at scale. According to Google’s API pricing page, Nano Banana Pro’s image output is priced at $120 per million tokens, working out to roughly $0.134 per generated image at 1K pixel resolution.

For applications generating thousands of images daily — think e-commerce product visualization, marketing asset pipelines, or localized content generation — those costs compound quickly.

Nano Banana 2, built on the Gemini 3.1 Flash backbone, dramatically undercuts that pricing. Flash-tier image output is priced at $60 per million tokens, approximately $0.067 per 1K image per image — roughly 50% cheaper than the Pro model. For enterprises running high-volume image generation workflows, that’s the difference between a proof of concept and a production deployment.

What Nano Banana 2 actually delivers

The model is not simply a cheaper Nano Banana Pro. According to Google DeepMind’s announcement, Nano Banana 2 brings several capabilities that were previously exclusive to the Pro tier while introducing new features of its own.

The headline improvement is text rendering and translation. The model can generate images with accurate, legible text — a historically weak point for AI image generators — and then translate that text into different languages within the same image editing workflow. 

Subject consistency has also improved significantly. Nano Banana 2 can maintain character resemblance across up to five characters and preserve the fidelity of up to 14 reference objects in a single generation workflow.

This enables storyboarding, product photography with multiple SKUs, and brand asset creation where visual continuity matters. Google’s documentation highlights the ability to provide up to 14 different reference images as input, allowing the model to compose scenes incorporating multiple distinct objects or characters from separate sources.

On the technical specification side, the model supports full aspect ratio control, resolutions ranging from 512 pixels up to 4K, and two thinking levels that let developers balance quality against latency.

One notable addition that Nano Banana Pro lacks is an image search tool — the model can perform image searches and use retrieved images as grounding context for generation, expanding its utility for workflows that require visual reference material.

The Qwen-Image-2.0 factor: why Google needed to move fast

Google’s timing is not coincidental. On February 10, Alibaba’s Qwen team released Qwen-Image-2.0, a unified image generation and editing model that immediately drew comparisons to Nano Banana Pro — but with a dramatically smaller footprint.

Qwen-Image-2.0 runs on just 7 billion parameters, down from 20 billion in its predecessor, while unifying text-to-image generation and image editing into a single architecture.

The model generates natively at 2K resolution (2048×2048 pixels), supports prompts up to 1,000 tokens for complex layouts, and ranks at or near the top of AI Arena’s blind human evaluation leaderboard for both generation and editing tasks.

For enterprise buyers, the competitive dynamics are significant. Qwen-Image-2.0’s 7B parameter count means substantially lower inference costs when self-hosted — a critical consideration for organizations with data residency requirements or high-volume workloads.

The Qwen team’s previous model, Qwen-Image v1, was released under Apache 2.0 approximately one month after its initial announcement, and the developer community widely expects the same trajectory for v2.0. If open weights materialize, organizations could run a Nano Banana Pro-competitive image model on their own infrastructure without per-image API charges.

The model’s unified generation-and-editing architecture also simplifies deployment. Rather than chaining separate models for creation and modification — the current industry norm — Qwen-Image-2.0 handles both tasks in a single pass, reducing latency and the quality degradation that occurs when outputs are passed between different systems.

Where Qwen-Image-2.0 currently trails is ecosystem integration. Google’s Nano Banana 2 launches today across the Gemini app, Google Search (AI Mode and Lens), AI Studio, the Gemini API, Google Antigravity, Vertex AI, Google Cloud, and Flow — where it becomes the default image generation model at zero credit cost. That breadth of distribution is difficult for any challenger to replicate, particularly one whose API access is currently limited to Alibaba Cloud’s platform.

What this means for enterprise AI image strategies

The simultaneous availability of Nano Banana 2 and Qwen-Image-2.0 creates a decision framework that IT leaders haven’t had before in the image generation space.

For organizations already embedded in Google’s cloud ecosystem, Nano Banana 2 is the obvious first evaluation. The cost reduction from Pro pricing, combined with native integration across Google’s product surface, makes it the path of least resistance for teams that need production-quality image generation without re-architecting their stack. The model’s text rendering capabilities make it particularly well-suited for marketing asset generation, localization workflows, and any application where legible in-image text is a requirement.

For organizations with data sovereignty concerns, high-volume workloads that make per-image API pricing prohibitive, or a strategic preference for open-weight models, Qwen-Image-2.0 presents a compelling alternative — provided Alibaba follows through on open-weight availability. The model’s smaller parameter count translates to lower GPU requirements for self-hosting, and its unified generation-editing architecture reduces pipeline complexity.

The wild card is Nano Banana Pro itself, which isn’t going away. Google AI Pro and Ultra subscribers retain access to the Pro model for specialized tasks, accessible via the regeneration menu in the Gemini app. For use cases demanding maximum visual fidelity and creative reasoning — think high-end creative campaigns or applications where every image needs to look bespoke — Pro remains the ceiling.

The provenance layer: a quiet but important enterprise differentiator

Buried in Google’s announcement is a detail that may matter more to enterprise legal and compliance teams than any quality benchmark: provenance tooling. Nano Banana 2 ships with SynthID watermarking — Google’s AI-generated content identification technology — coupled with C2PA Content Credentials, the cross-industry standard for content authenticity metadata.

Google reports that since launching SynthID verification in the Gemini app last November, the feature has been used over 20 million times to identify AI-generated images, video, and audio. C2PA verification is coming to the Gemini app soon as well.

For enterprises operating in regulated industries or jurisdictions with emerging AI transparency requirements, baked-in provenance is no longer optional. It’s a compliance checkbox — and one that self-hosted open-weight alternatives like Qwen-Image-2.0 don’t natively provide.

The bottom line

Nano Banana 2 doesn’t represent a generational leap in image generation quality. What it represents is the maturation of AI image generation from a creative novelty into a production-ready infrastructure component. By collapsing the cost and speed gap between Flash and Pro tiers while retaining the reasoning and text rendering capabilities that make these models useful for actual business workflows, Google is making a calculated bet: the next wave of enterprise AI image adoption will be driven not by the models that produce the most beautiful images, but by the ones that produce good-enough images fast enough and cheaply enough to deploy at scale.

With Qwen-Image-2.0 pushing from the open-weight flank and Nano Banana Pro holding the quality ceiling, Nano Banana 2 occupies exactly the middle ground where most enterprise workloads actually live. For IT decision-makers who’ve been waiting for the cost curve to bend, it just did.

DIY store chain ManoMano is notifying customers of a data breach that was caused by hackers compromising a third-party service provider.

The company confirmed to BleepingComputer that it learned of the hack in January 2026. An investigation into the incident determined that 38 million individuals are affected.

“We can confirm that ManoMano has recently notified customers about a security incident involving one of our third-party customer service providers (a subcontractor),” the company told BleepingComputer.

“In January 2026, we identified unauthorized access linked to this provider, which resulted in the unauthorized extraction of certain personal data associated with customer accounts and customer service interactions.”

ManoMano is a French e-commerce firm operating an online marketplace specializing in DIY, home improvement, gardening, and related products. It operates in France, Belgium, Spain, Italy, Germany, and the United Kingdom, and its e-stores reportedly have 50 million unique visitors per month.

Earlier this month, someone using the alias “Indra” claimed the ManoMano attack on a hacker forum, alleging that they were holding details on 37.8 million user accounts, as well as thousands of support tickets and attachments.

According to unconfirmed reports, the compromised organization was a Tunis-based customer support service provider that suffered a Zendesk breach.

Cybersecurity firm Hackmanac posted that ManoMano started notifying customers this week that their data had been stolen.

A spokesperson of ManoMano explained to BleepingComputer that the exposed information varies per individual, depending on the type of interactions they had with the platform. Exposed data types include:

• Full name
• Email address
• Phone number
• Customer service communications

ManoMano emphasizes that no account passwords were accessed and that no data modifications occurred on the company’s systems.

“Upon discovery, we took immediate steps to secure our environment, including disabling the relevant access, revoking the subcontractor’s access to customer data, and strengthening access controls and monitoring,” said a ManoMano spokesperson.

“We also notified the relevant authorities, including the CNIL and ANSSI, and informed impacted customers with guidance to remain vigilant against phishing and social engineering attempts.”

The notification sample ManoMano shared with BleepingComputer contains recommendations for customers, including verifying incoming communications and sender identity, monitoring bank accounts for fraudulent transactions, and avoiding clicking on suspicious links or downloading email attachments.

ManoMano notes that the investigation is ongoing and that they cannot share additional technical details at this stage.

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The raise will fund Allica’s expansion plans outside the UK.

London’s Allica Bank has joined the European unicorn league with a $155m Series D raise that values the company at $1.2bn. New investors Ventura Capital, GLG and Sona AM, and existing investors TCV and Blue Owl, took part in the round.

Allica’s digital banking services are geared toward small, and medium enterprises (SMEs), currently offering services to more than 30,000 SMEs across the UK. The 2011-founded company has been named the fastest growing technology company in the UK by Deloitte in both 2023 and 2024.

According to Allica, SMEs are an underserved customer base in the fintech market.

The digital bank said the capital will enable continued investment into AI to develop newer lending mechanisms for SMEs. The infusion will also fund the company’s expansion plans outside the UK.

“We’re building the category defining digital bank for established SMBs, and are excited to be taking our proprietary platform into new markets,” said Allica CEO Richard Davies.

“This Series D investment is a major vote of confidence in Allica’s strategy and performance.”

Allica joins the fintech unicorn league alongside companies such as the UK’s Cleo and Denmark’s Flatpay. It was one of the new firms invited to participate in a new UK scale-up unit designed to support fast-growing, innovative financial services firms to scale and create high-skilled jobs in the country.

“Allica is a world class business that is executing exceptionally well in a large, underserved market,” said Mo El Husseiny, the managing partner of Ventura Capital.

Earlier this week, Irish fintech Stripe, founded by brothers John and Patrick Collison, announced it had hit a $159bn valuation – up from $106bn a year ago. Bloomberg reported shortly after that Stripe is considering acquiring PayPal, which has been struggling to grow for a few years.

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