A Ukrainian national was sentenced to 70 months in prison for his role in Nefilim and Hive ransomware attacks targeting hospitals, government entities, and high-revenue businesses worldwide between 2018 and 2021. The sentence follows the extradition of 35-year-old Artem Aleksandrovych Stryzhak to the United States on April 30, 2025, after authorities arrested him in Spain in June 2024. "Artem Stryzhak is a prolific cybercriminal who worked with multiple ransomware groups to target U.S. businesses and hospitals," said Brett Leatherman, the FBI's Cyber Branch Operations Director. "These groups disrupted patient care and caused more than $100 million in losses. Today's sentence reflects the seriousness of Stryzhak's crimes and demonstrates the FBI's global reach and extensive network of international partners."
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
The critical Oracle PeopleSoft bug that ShinyHunters has exploited since May is not one flaw but three chained together, the researchers who reported it said this week. Oracle patched all three under a single identifier, CVE-2026-35273, in June. Its root is a server-side request forgery (SSRF) that reaches an internal management interface because PeopleSoft trusts its own loopback address
The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Tuesday added two vulnerabilities impacting Adobe Commerce and Magento, and Google's Brotli decoder library to its Known Exploited Vulnerabilities (KEV) catalog, citing evidence of active exploitation. The vulnerabilities are listed below - CVE-2026-111497 (CVSS score: 9.2) - An unchecked return value vulnerability in
The GMO Group has urged customers to disconnect their systems from the internet and shut down computers after a threat actor who breached the company’s Onamae Desktop Cloud threatened to launch a ransomware attack today. The company later confirmed that data belonging to both the company and its customers had been published online. The alert concerns a cyberattack on Onamae Desktop Cloud, a popular VPS provider in Japan operated by GMO Internet, a member of GMO Internet Group and one of Japan's largest domain registrars. Last week, the company announced it detected an intrusion on October 20 that could have installed malware on up to 26,739 customers' servers. To minimize the risk, the company shut down the servers and reduced service availability before launching an investigation. The internet company assured customers that their payment information had not been exposed but warned that MetaTrader 4 and MetaTrader 5 (MT4/MT5) credentials and trading-account details may have been exposed or compromised. It urged customers to reset their passwords and terminate active sessions.
OpenAI confirmed GPT-6.1 Terra is safer overall than some of its older models, but its safety testing also found other problems. According to OpenAI, GPT-6.1 Terra showed higher rates of exfiltration, destructive actions, and attempts to bypass restrictions than GPT-6.1 Sol and GPT-6 Astra. OpenAI performed simulations across approximately 48,690 matched tasks and found that Terra received 22 severity-3-or-higher flags, or 0.045%. For comparison, GPT-6 Sol received 38, while GPT-5.6 Sol received 61. "GPT-6.1 Terra received approximately 42% fewer severity level 3 or higher flags than GPT-6 Sol and 64% fewer than GPT-5.6 Sol," OpenAI noted in its system card. However, that is no longer the case when you compare Terra with other newer models on less severe behaviors. "Relative to GPT-6.1 Sol and GPT-6 Astra, we observed higher rates of flags for concealing uncertainty, circumventing restrictions, exfiltration, other misalignment, destructive action and inbound prompt injection," OpenAI explained.
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