Sony Network Communications has confirmed a data breach that compromised approximately 2.5 million records containing current and former customers’ personal information. A Sony Group Corporation subsidiary, the company provides telecommunications and technology services, acting as an internet service provider (ISP) in Japan. The breach occurred on October 19 and was detected four days later, when the company shut down the compromised servers and blocked external communication. In an announcement yesterday, the company said that its So-net member database, supporting its internet access service, was accessed by an unauthorized party. The investigation confirmed that a total of 2,585,481 customer records had been exposed, including the following types of information: No banking, payment card, or other account details were exposed.
Microsoft again asked Entra ID administrators to migrate users to phishing-resistant methods to avoid sign-in disruptions before it retires SMS and voice authentication in February 2027. This retirement process applies only to Microsoft Entra ID workforce tenant authentication scenarios and not to Azure AD B2C or Microsoft Entra External ID customer identity scenarios. Before Microsoft retires SMS and voice authentication, admins should ensure all users in their organizations use a phishing-resistant method, since they will no longer be able to use them to complete multifactor authentication and log in to their accounts. Admins can choose from passkeys, QR code authentication, FIDO2 security keys, and other Entra ID-supported sign-in methods. "Microsoft-provided SMS and voice authentication will retire starting February 1, 2027. Complete your migration before rollout begins to avoid interrupting users' sign-in experience," Microsoft said in a Microsoft 365 Message Center update on Tuesday.
Attackers have begun probing the Internet for systems unpatched against a recently disclosed critical Oracle E-Business Suite (EBS) vulnerability. Discovered in a core component of EBS's Application Object Library and tracked as CVE-2026-101767, this flaw lets unauthenticated threat actors with HTTP network access take over vulnerable systems in low-complexity attacks. Oracle released security updates to address the issue in its October 2026 Critical Security Patch Update on October 20 and urged customers to patch their systems immediately. "In some instances, it has been reported that attackers have been successful because targeted customers had failed to apply available Oracle patches," it said. "Oracle therefore strongly recommends that customers remain on actively-supported versions and apply security patches without delay."
CVE-2026-97631: A 16-year-old UEFI flaw (CVSS score of 9.5/10) enables pre-OS code execution. Exposed servers need firmware patches and checks. A critical flaw, tracked as CVE-2026-97631 (CVSS score of 9.5), in the firmware that boots most of the world’s servers is now public, and the patch details came out months early. The bug affects TianoCore EDK II, an open-source project used by hardware manufacturers and cloud providers to build UEFI firmware. UEFI is the software that starts a computer before the operating system loads. EDK II is widely used across the industry, including by major UEFI vendors such as AMI, Insyde and Phoenix. Even Microsoft’s more secure version, Project Mu, used the vulnerable component. “EDK2 contains a vulnerability in BIOS where a user may cause “Out-of-bounds Write”, Classic Buffer Overflow, and “Incorrect Calculation of Buffer Size” by remote access. Successful exploitation of this vulnerability can lead to arbitrary code execution. It could also impact Confidentiality, Integrity, and Availability.” reads the advisory. The flaw can corrupt memory in a component that handles ICMP messages, which devices use to report network errors. Attackers could exploit it to run code before the operating system starts and before security protections such as Secure Boot are fully active. Secure Boot checks that the software loaded during startup can be trusted, while the Trusted Platform Module (TPM) supports security functions such as hardware-based trust. Although the attacker’s code does not remain active after execution, it could help attackers install other malicious components that run early in the boot process. The flaw is a 16-year-old buffer overflow in the PXE-Base Code driver (NetworkPkg/UefiPxeBcDxe). That driver lets servers, storage gear and other devices boot from code downloaded over a local network. While it does that, it listens for ICMP errors, and it never enforces the 504-byte limit on how much it copies. “While a machine is PXE-booting over IPv4 or IPv6, an unauthenticated network attacker who can deliver ICMP error messages to it can corrupt DXE-phase memory and execute arbitrary code before the operating system loads, before Secure Boot or measured boot has verified or measured any OS component.” states the advisory. A malicious insider, or a hacker with low-level access to the local network, can send an error message that spills code past the limit and corrupts the UEFI module running on the CPU. The advisory is more precise. The driver keeps a receive request open for ICMP errors and copies each message into a fixed 504-byte field called Mode.IcmpError. In both the IPv4 and IPv6 handlers (PxeBcIcmpErrorDpcHandle and PxeBcIcmp6ErrorDpcHandle) the bounds handling is wrong: a longer message still gets copied, and the destination pointer moves by the cumulative length and not the per-fragment length. So an oversized message writes attacker-controlled bytes past the field, and nothing earlier on the receive path limits its size. The attack is simple. The attacker sends a direct message to the target’s IP address during the PXE boot process, even from a remote network. Sending invalid data can stop the boot process and cause a denial of service. If the attacker knows the target’s firmware version, they can create a fake event to run their own code at ring 0, the CPU’s highest privilege level. This also bypasses attestation, which uses cryptographic checks to prove that the hardware is trustworthy. As a result, the attack can help an attacker turn limited access into full control. The attacker needs some control over the local network. This could be an insider or an external attacker who already has a foothold in the target network and is seeking to gain higher privileges. So far, there’s no evidence that this flaw can be exploited to gain initial access to a network from the outside. The flaw affects some systems that start up over a network using EDK2-based firmware with PXE enabled. Home and small-office users, and anyone who boots only from a disk drive, aren’t affected. Researchers planned to disclose the bug in January, but AMI published a fix last month, revealing details of the flaw. TianoCore then issued a public warning. Attackers can now study the fix to develop exploits, while updating firmware across many devices could take a long time. It’s still unclear which devices are affected. Intel and Arm say their own products aren’t directly affected, but manufacturers may add vulnerable code. Several other companies hadn’t answered questions when the article was published. Organizations should contact their system or firmware supplier and disable network boot where possible. They should also monitor logs for suspicious activity. TPM and Secure Boot remain useful protections, but they don’t stop this attack. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CVE-2026-97631)
Adobe has released patches to address multiple critical security flaws in Campaign Classic that, if successfully exploited, could result in arbitrary code execution, security feature bypass, and privilege escalation. The list of eight vulnerabilities is as follows - CVE-2026-104351 (CVSS score: 9.9) - A code injection vulnerability that could lead to arbitrary code execution.
Three Japan-focused cyberattacks show how AI helps hackers scale operations, while exploiting old systems and weak security remains key. In autumn 2026, Japanese companies and other organizations reported a series of data leaks involving their web systems. On October 9, Japan’s National Cybersecurity Office issued a warning to businesses, while the head of the ruling party’s cybersecurity team described cyberspace as being in a “state of emergency.” Researchers at Trend Micro studied three attack infrastructures to understand how hackers use AI. Their findings show three different approaches, ranging from limited AI assistance to systems that use AI agents to manage almost an entire attack. The researchers looked at files that attackers left on servers anyone could reach. These included attack scripts, lists of targets, logs, and records of commands. The files showed attacks on organizations in Japan and other countries. But the researchers could not see the victims’ own logs or network records. So they could not confirm every break-in, measure the full damage, or say who was behind it. Attackers also like to claim more success than they really had. The researchers only counted a result as real when the evidence backed it up. The three cases show that AI does not play the same role in every cyberattack. “The cases differ along two dimensions: how much of the attack AI handled and how confidently we can establish its involvement.” reads the report. “Case A used AI to drive the entire workflow, which we confirmed. Case B was human-led, with confirmed AI assistance in planning and tool development. Case C showed high-confidence indications of AI use in a limited role, such as tool development. All three were active in 2026, and much of the activity preceded the previously published ARTEX reports.” Case A: AI agents managed the attack workflow. The researchers confirmed the use of CyberStrikeAI, an open-source penetration-testing platform built around AI agents. It handled reconnaissance, vulnerability checks, exploitation, data theft and reporting as part of a repeatable process. The platform split tasks among multiple AI agents. One record mentioned a credential-search agent in a system that used 44 agents. The platform could also generate Japanese content, including forms and fake login pages. Researchers identified at least 19 target organizations, including at least 16 in Japan. The system worked on several targets at once and kept traffic to each organization relatively low. This made the activity harder to detect by checking the logs of a single victim. Despite this automation, the platform rarely gained deep access to target systems. In many cases, it had no active session or web shell and showed no evidence of stolen data. The clearest intrusion started through an administration page that was publicly accessible on an old web system. The attackers did not need to exploit a newly discovered vulnerability. Case B: A human attacker used AI to plan and build tools. In this case, the operator made the main decisions but used a large language model to speed up planning and tool development. The researchers found an attack plan written by an LLM that targeted services providing AI and GPU resources. The attacker presented requests as authorized security testing to get around the model’s safety limits. The researchers say this was a false claim, although they did not publish the exact method. The attacker also targeted AI infrastructure, including platforms for AI workflows, data processing and model inference. The researchers believe the goal may have been to gain access to computing resources that could be used to run AI models cheaply. Some intrusions had serious consequences. In one case, the attacker gained control across several systems, including an FTP service, a database, an administrator account, a web shell and SSH access. The attacker also restored backdoors after they had been removed. In another case, a geographic information system server was exposed with default credentials. This provided access to hundreds of thousands of land and business records, as well as Windows credentials and email data. The evidence confirmed that information was downloaded. However, the researchers found no evidence that AI carried out these attacks independently. It helped the operator plan and build tools, while the actual work relied mainly on scripts, existing software and human decisions. Case C: AI played a limited role. Here, the operator appears to have used AI mainly to develop tools and scripts. The researchers found no evidence that it was involved in planning or managing the wider operation. They rated AI involvement as highly likely, but not confirmed, because they found no direct evidence such as prompts or API keys. The operator ran at least six campaigns in 2026, targeting exposed VPN devices and Oracle WebLogic servers. The targets included devices from Cisco, Citrix, F5 and Yamaha. In attacks against Yamaha RTX1210 routers, the operator did not exploit a product vulnerability. Instead, the attacker used credentials found in configuration files. These credentials provided direct access to internal corporate networks. The researchers also found signs that AI may have helped write some commands and scripts. These included unusually structured comments and emoji used as status markers. Such clues can support an assessment, but they cannot prove AI use on their own. Japan was the main target in all three operations, but attackers also targeted other countries and regions. All three used Simplified Chinese, but this does not prove they came from the same group or location. “Simplified Chinese was the working language in all three infrastructures. Japan was the largest target country, although the operators also pursued nearby countries and regions. Entry points were exposed legacy systems or weaknesses left in default configurations.” concludes the report. “Target selection was opportunistic across industries: the operators sought systems that were easy to attack from outside and acted on what they found.” The main difference was how AI was used. In one case, AI agents managed much of the attack and worked on many targets at once. In another, AI helped a human plan attacks and build tools. In the third, AI was mainly used to develop tools. AI helped make attacks faster and easier to scale, but attackers still exploited familiar weaknesses, such as outdated web systems, exposed admin pages, default passwords and poorly secured network devices. Researchers also found that AI tools sometimes reported planned actions as successful, making it essential to verify their results. AI can also help defenders discover vulnerabilities. However, researchers found no evidence that these three operations used AI-discovered zero-day flaws. Their attacks relied on known vulnerabilities and poor security settings. The report identifies six main priorities for defenders: Review public-facing systems. Identify old web applications and update them or isolate them if they cannot be secured. Protect administration pages. Check that management interfaces are not exposed to the internet without a clear need and strong access controls. Secure VPNs and network devices. Review configurations, remove default credentials and check for reused passwords. Protect internal AI services. Include AI workflows, inference services and GPU systems in asset inventories, and check whether they are exposed. Improve monitoring. Look for low-volume attacks spread across many targets, which may be difficult to spot in the logs of a single organization. Prepare for AI misuse. Organizations that use LLMs for work should train staff to recognize requests that falsely claim to be authorized security testing. These security weaknesses existed long before AI became widely available. AI simply helps attackers find and exploit them faster and across more systems. The time between discovering a vulnerability and using it in an attack may also become shorter. Organizations should install security updates quickly and apply temporary protections when patches are not available. Researchers still don’t know who was behind the three operations, whether they were connected or how much damage they caused. Further reports may provide more answers. “The attackers’ identities, whether the three cases share an operator, the full scale of the damage, and the AI models used remain unresolved. Our observations come from attacker-side files and still require confirmation from the affected organizations.” concludes the report. “We are considering further technical reporting on individual cases: AI-driven activity in Case A, AI assistance in Case B, and limited AI assistance in Case C. We will also continue working with the relevant organizations.” The main lesson is clear: AI can make cyberattacks faster and larger, but the basic security priorities remain the same. Organizations must identify systems exposed to the internet, strengthen access controls and detect suspicious activity before attackers turn a simple weakness into a serious breach. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Italy)
An unreleased Anthropic AI model found security flaws in Chrome, Safari, Android, Windows, and Linux and chained them into working proof-of-concept exploits, the company said in its latest report. The exploits were built in the lab after the model was prompted, not seen in any real attack, and Anthropic reported every flaw to the vendor first, so all of them are already patched. One proof of
The U.S. Department of Justice (DoJ) on Tuesday announced the seizure of a domain associated with a bulletproof hosting provider (BPH) named Virtualine. The move was in response to the service being used to commit and facilitate prolific criminal activity, including "the advertisement of sexually explicit images of minors, the intentional disclosure of nonconsensual intimate visual depictions
Japan's GMO Internet Group is offering up to $1 million in total for tips that help catch the attackers who breached its Onamae.com Desktop Cloud service, after refusing to pay an extortion demand. The reward program opened on October 28 and pays only if a tip helps lead to an arrest or an international wanted notice. The service hosts Windows desktops used for FX trading,
Tailscale has released version 1.104.4 to fix flaws in its built-in SSH server that could let an attacker reach services and files on other servers that only the root user should be able to touch. The flaws are in how Tailscale SSH forwards network connections, a job the Tailscale background process carries out with root privileges. To use them, an attacker must
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