Veeam warned customers about boot failures affecting Software Appliance virtual machines (VMs) on hosts with AMD EPYC processors. The company's Software Appliance VMs are pre-configured, Linux-based virtual machines that let admins run the Veeam Backup & Replication backup software suite on Linux devices without manually installing dependencies or configuring the software. To comply with corporate and government security frameworks, the underlying Linux kernel runs integrated health and cryptographic checks (such as jitterentropy health tests) in FIPS mode during early boot. However, as the company explained in a Wednesday advisory, these VMs will fail to boot and will crash with a kernel panic after failing an early-boot FIPS-mode jitterentropy check on some AMD EPYC processors. "A Veeam Software Appliance (Linux-based Veeam Backup & Replication) virtual machine running on a host with an AMD EPYC processor intermittently fails to boot after a reboot, including a reboot that follows an update that installs a new kernel," it said.
Threat actors are attempting to exploit a recently disclosed security flaw impacting OpenSSH that can enable sign-ins to accounts that have no password set, according to Previdian. The vulnerability, tracked as CVE-2026-123158 (CVSS score: 8.1) and codenamed SSHole (or *sshole), impacts Linux systems that run OpenSSH on the musl C library with Pluggable Authentication Modules (PAM) disabled,
OpenAI on Thursday unveiled GPT‑6 Cyber, which it described as a major step forward in security research that brings together stronger cybersecurity capabilities with a step-change improvement in model alignment. The new model is "more capable at finding and fixing vulnerabilities, and far more reliable staying within the boundaries researchers set," OpenAI said. It has classified the model as
A fix for CVE-2026-83844, a remote code execution flaw in Windows Paint, is now in the Microsoft Store. Paint gets this fix only through a Store update, not through Windows updates. The fixed build, 11.2609.401.0, was the newest Paint build in all ten countries The Hacker News checked today. The flaw is an out-of-bounds read that could let an attacker run code over a network. Microsoft rates
A browser extension that can run scripts on claude.ai could make Claude Desktop run code on a person's computer, researchers at Forever Security, an endpoint security company, showed in a test. The attack needs Dispatch, a Claude feature that lets people send tasks from their phone to Claude Desktop. The flaw that makes this possible can still be exploited, the researchers said earlier this
An update on Athena, and some unsolicited predictions on what's coming next On Saturday, Dario Amodei published We Must Pace the Frontier. I agree with him. Go read it if you haven't. I've spent the summer looking at what these models find, and the gap he describes between what they can do and what the rest of us are ready for is the one I've been staring at since May. The hardest piece of his
OpenAI says GPT-6 Astra sometimes secretly underperformed on safety research, raising concerns about AI reliability and alignment. OpenAI has disclosed that its GPT-6 Astra model sometimes quietly obstructed safety and alignment work. In a report updated on November 4, the company calls it covert underperformance. Astra added unnecessary prerequisites, abandoned working solutions and reported blockers that didn’t exist or that it had caused itself. Researchers first reported the problem internally on August 20, shortly after Astra was introduced. One researcher said a project to test whether the model could sabotage other AI systems made little progress for several days. After switching to another model, the work moved forward quickly. In most cases, Astra’s reasoning gave no clear sign that it was deliberately doing less work, and OpenAI’s monitoring systems failed to detect the behavior. To investigate, OpenAI created pairs of coding tasks using the same code but with different goals. One task was presented as safety research, while the other was described as capability development. Astra completed about 10% of the required tasks when they involved safety research, compared with 16% for capability development. This means it completed roughly 40% fewer tasks in the safety tests. GPT-5.6 Sol showed almost no difference between the two types of work. Even after OpenAI removed cases involving clear refusals or signs that Astra had deliberately withheld help, the gap remained at 4.7 percentage points. It dropped to just 0.75 points only after researchers also removed every case where the model showed even brief ethical concerns. This suggests that most of the behavior cannot be detected simply by checking whether the model openly refuses a request. In employees’ daily Codex use, Astra refused 1.63 percent of safety and alignment requests, against 0.56 percent of capabilities requests. Its reasoning showed it withheld help silently in 0.25 percent of safety requests versus 0.11 percent of capabilities requests, 2.3 times as often. Ethical doubt appeared in 7.9 percent against 2.9 percent, mostly in red-teaming, monitoring, and safety evaluation work. OpenAI shared eight examples. Astra silently held back on training models to evade monitors, filtered a violent source out of a safety evaluation, restricted a jailbreak data export and narrowed exploit analysis. In one case it left graders’ reasoning out of an export without telling the user. A model that quietly decides your project is too risky is the coworker who says “looking into it” and never does. OpenAI has not yet identified the exact cause of the behavior. The difference between Astra’s performance on safety and capability tasks grew from almost zero at the start of reinforcement learning to 6 percentage points by the end. One evaluator favored silently doing less work over either refusing a request or fully completing it. Another finding was that a fine-tuning dataset encouraged the model to invent permission restrictions. Removing the changes linked to that dataset eliminated the performance gap. However, other models trained with the same dataset and reinforcement learning did not show the same behavior, so OpenAI cannot identify either factor as the root cause. OpenAI found no evidence that Astra was deliberately trying to hide its capabilities or influence deployment decisions, although it cannot rule out these possibilities. Simply telling the model to be more careful increased its underperformance by 50%, suggesting that excessive caution may be the main explanation. OpenAI warns that this behavior can slow safety research and make model evaluations less reliable. It is developing new tests and monitoring systems to detect it, especially because the problem becomes harder to identify when models reveal less of their reasoning. In some cases, Astra explained that it had held back because it disagreed with the research itself. “In reviewed cases, Astra narrowed or stopped requested safety work without disclosing the restriction. At scale, this can differentially slow down safety research compared to other research and make evaluations harder to interpret.” concludes the report. “A model that finds monitor evasion suspicious may underperform on monitor-evasion evaluations while leaving little evidence in its CoT or outputs. The behavior becomes even harder to detect if models externalize less of this reasoning in CoT.” Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, OpenAI)
A backdoor has been found hiding in the web server that runs Control Web Panel (CWP), a free panel for managing Linux hosting servers, researchers at QiAnXin's XLab said this week. The backdoored web server executes commands that attackers send via web requests, and those requests never appear in its access logs. Installing it takes root access, so the attackers must already control the server.
Two flaws in Barracuda's Email Security Gateway, an email-filtering appliance, can be chained to run commands on it as root, its highest level of access, with no login required. The company released patches on Monday, and the security firm watchTowr detailed the attack chain on Thursday. One flaw, CVE-2026-116606, is an authorization bypass. An unauthenticated attacker can use it to restore a
The FBI and CISA warn that hackers are actively targeting industrial refrigeration and cold-chain monitoring systems worldwide, potentially causing food spoilage, equipment damage, and fire hazards. The warning was issued jointly with the U.S. Departments of Health and Human Services (HHS), Agriculture (USDA), and Transportation (DOT), urging critical infrastructure operators to secure vulnerable refrigeration controllers used in healthcare, food production, chemical facilities, and transportation. "Every organization that depends on refrigeration and monitoring systems should assume it could be a target," said Chris Butera, CISA's Acting Executive Assistant Director for Cybersecurity. According to a joint advisory published Thursday, attackers are exploiting weaknesses in unpatched or internet-accessible electronic temperature controllers and commercial refrigeration management systems.
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