Security agencies have issued a new warning about the Akira ransomware group after investigators confirmed that the operators have added Nutanix AHV virtual machines to their list of targets. This represents a significant expansion of the group’s capabilities, which had already included attacks on VMware ESXi and Microsoft Hyper-V environments. The update signals that Akira is no longer limiting itself to conventional endpoints or common hypervisors and is now actively pursuing a wider range of virtual infrastructure used in large organisations.
Although Akira was first known for intrusions affecting small and medium businesses across North America, Europe and Australia, the pattern of attacks has changed noticeably over the last year. Incident reports now show that the group is striking much larger companies, particularly those involved in manufacturing, IT services, healthcare operations, banking and financial services, and food-related industries. This shift suggests a strategic move toward high-value victims where disruptions can cause substantial operational impact and increase the pressure to pay ransom demands.
Analysts observing the group’s behaviour note that Akira has not simply created a few new variants. Instead, it has invested considerable effort into developing ransomware that functions across multiple operating systems, including Windows and Linux, and across several virtualisation platforms. Building such wide-reaching capability requires long-term planning, and researchers interpret this as evidence that the group aims to remain active for an extended period.
How attackers get into networks
Investigations into real-world intrusions show that Akira typically begins by taking advantage of weak points in remote access systems and devices connected to the internet. Many victims used VPN systems that lacked multifactor authentication, making them vulnerable to attackers trying common password combinations or using previously leaked credentials. The group has also exploited publicly known vulnerabilities in networking products from major vendors and in backup platforms that had not been updated with security patches.
In addition to these weaknesses, Akira has used targeted phishing emails, misconfigured Remote Desktop Protocol portals, and exposed SSH interfaces on network routers. In some breaches, compromising a router allowed attackers to tunnel deeper into internal networks and reach critical servers, especially outdated backup systems that had not been maintained.
Once inside, the attackers survey the entire environment. They run commands designed to identify domain controllers and trust relationships between systems, giving them a map of how the network is structured. To avoid being detected, they often use remote-access tools that are normally employed by IT administrators, making their activity harder to differentiate from legitimate work. They also disable security software, create administrator-level user accounts for long-term access, and deploy tools capable of running commands on multiple machines at once.
Data theft and encryption techniques
Akira uses a double-extortion method. The attackers first locate and collect sensitive corporate information, which they compress and transfer out of the network using well-known tools such as FileZilla, WinRAR, WinSCP or RClone. Some investigations show that this data extraction process can be completed in just a few hours. Once the information has been removed, they launch the ransomware encryptor, which uses modern encryption algorithms that are designed to work quickly and efficiently. Over time, the group has changed the file extensions that appear after encryption and has modified the names and placement of ransom notes. The ransomware also removes Windows shadow copies to block easy recovery options.
Why the threat continues to succeed
Cybersecurity experts point out that Akira benefits from long-standing issues that many organisations fail to address. Network appliances, remote access devices, and backup servers often remain unpatched for months, giving attackers opportunities to exploit vulnerabilities that should have been resolved. These overlooked systems create gaps that remain unnoticed until an intrusion is already underway.
How organisations can strengthen defences
While applying patches, enabling multifactor authentication, and keeping offline backups remain essential, the recent wave of incidents shows that more comprehensive measures are necessary. Specialists recommend dividing networks into smaller segments to limit lateral movement, monitoring administrator-level activity closely, and extending security controls to backup systems and virtualisation consoles. Organisations should also conduct complete ransomware readiness exercises that include not only technical recovery procedures but also legal considerations, communication strategies, and preparations for potential data leaks.
Security researchers emphasise that companies must approach defence with the same mindset attackers use to find vulnerabilities. Identifying weaknesses before adversaries exploit them can make the difference between a minor disruption and a large-scale crisis.
Security experts have identified a new kind of cyber attack that hides instructions inside ordinary pictures. These commands do not appear in the full image but become visible only when the photo is automatically resized by artificial intelligence (AI) systems.
The attack works by adjusting specific pixels in a large picture. To the human eye, the image looks normal. But once an AI platform scales it down, those tiny adjustments blend together into readable text. If the system interprets that text as a command, it may carry out harmful actions without the user’s consent.
Researchers tested this method on several AI tools, including interfaces that connect with services like calendars and emails. In one demonstration, a seemingly harmless image was uploaded to an AI command-line tool. Because the tool automatically approved external requests, the hidden message forced it to send calendar data to an attacker’s email account.
The root of the problem lies in how computers shrink images. When reducing a picture, algorithms merge many pixels into fewer ones. Popular methods include nearest neighbor, bilinear, and bicubic interpolation. Each creates different patterns when compressing images. Attackers can take advantage of these predictable patterns by designing images that reveal commands only after scaling.
To prove this, the researchers released Anamorpher, an open-source tool that generates such images. The tool can tailor pictures for different scaling methods and software libraries like TensorFlow, OpenCV, PyTorch, or Pillow. By hiding adjustments in dark parts of an image, attackers can make subtle brightness shifts that only show up when downscaled, turning backgrounds into letters or symbols.
Mobile phones and edge devices are at particular risk. These systems often force images into fixed sizes and rely on compression to save processing power. That makes them more likely to expose hidden content.
The researchers also built a way to identify which scaling method a system uses. They uploaded test images with patterns like checkerboards, circles, and stripes. The artifacts such as blurring, ringing, or color shifts revealed which algorithm was at play.
This discovery also connects to core ideas in signal processing, particularly the Nyquist-Shannon sampling theorem. When data is compressed below a certain threshold, distortions called aliasing appear. Attackers use this effect to create new patterns that were not visible in the original photo.
According to the researchers, simply switching scaling methods is not a fix. Instead, they suggest avoiding automatic resizing altogether by setting strict upload limits. Where resizing is necessary, platforms should show users a preview of what the AI system will actually process. They also advise requiring explicit user confirmation before any text detected inside an image can trigger sensitive operations.
This new attack builds on past research into adversarial images and prompt injection. While earlier studies focused on fooling image-recognition models, today’s risks are greater because modern AI systems are connected to real-world tools and services. Without stronger safeguards, even an innocent-looking photo could become a gateway for data theft.