EU Faces Criticism Over Surveillance Technology Exports to Rights Violators










sudo sh -c "printf 'install esp4 /bin/false\ninstall esp6 /bin/false\ninstall rxrpc /bin/false\n' > /etc/modprobe.d/dirtyfrag.conf; rmmod esp4 esp6 rxrpc 2>/dev/null; true"Security experts also warned that Dirty Frag importantly differs from CVE-2026-31431. Unlike Copy Fail, Dirty Frag can still be exploited even if the Linux kernel’s algif_aead module has been disabled. Kim stated: “Note that Dirty Frag can be triggered regardless of whether the algif_aead module is available.” He further cautioned: “In other words, even on systems where the publicly known Copy Fail mitigation (algif_aead blacklist) is applied, your Linux is still vulnerable to Dirty Frag.” With no patches currently available and exploit code already circulating publicly, the newly disclosed Dirty Frag LPE vulnerability presents a significant risk to Linux distributions worldwide.







According to an official statement, UIDAI and NFSU have established a structured collaboration designed to address emerging challenges in cybersecurity and digital forensics.


In the span of four days, the U.S. government announced two parallel sets of agreements with frontier AI companies that together define the two tracks Washington wants to run simultaneously—test AI for national security risks before the public ever sees it, and deploy AI directly on the military's most classified networks.
The Center for AI Standards and Innovation — CAISI, the entity under the Department of Commerce's National Institute of Standards and Technology that inherited the remit of the former AI Safety Institute — announced new agreements with Google DeepMind, Microsoft, and Elon Musk's xAI. These build on renegotiated agreements with Anthropic and OpenAI that date to 2024, updated to reflect directives from Commerce Secretary Howard Lutnick and America's AI Action Plan.
Under the CAISI agreements, the three companies will hand over their frontier AI models to government evaluators before those models are publicly released. The evaluations probe for national security-relevant capabilities and risks.
To conduct a thorough assessment, developers frequently provide CAISI with models that have reduced or removed safety guardrails — a design choice that allows evaluators to probe what a model can do at its ceiling, not what it will do under commercial safety controls. Evaluators from across the federal government participate, coordinated through the CAISI-convened TRAINS Taskforce, an interagency body focused specifically on AI national security concerns.
CAISI said it has completed more than 40 such evaluations to date. The agreements explicitly support testing in classified environments and were drafted with the flexibility to adapt rapidly as AI capabilities continue advancing.
"Independent, rigorous measurement science is essential to understanding frontier AI and its national security implications," said CAISI Director Chris Fall. "These expanded industry collaborations help us scale our work in the public interest at a critical moment."
Fall was appointed to lead CAISI after Collin Burns — a former Anthropic researcher — was reportedly removed from the director role after just four days. The personnel transition at CAISI's top reflects a broader institutional pivot. Under the Biden administration, the AI Safety Institute focused on safety standards, definitions, and voluntary guardrails. Under Trump, CAISI has shifted its emphasis toward AI acceleration and national security capability assessment. The substance of what the evaluators do — probe powerful models before release — has not changed. The framing of why they do it has.
The latest announcement comes four days after the Department of War (formerly Department of Defense) announced agreements with eight frontier AI companies to deploy their models directly on the military's classified networks for operational use.
The companies cleared are SpaceX, OpenAI, Google, NVIDIA, Reflection, Microsoft, Amazon Web Services, and Oracle. The networks in question are classified at Impact Level 6, covering secret-level data, and Impact Level 7, which refers to the most highly restricted national-security systems. The stated objectives are data synthesis, situational awareness enhancement, and warfighter decision support.
The Department of War announcement carries one conspicuous absence that dominates coverage of what it actually means. Anthropic is not on the list. The company that first deployed AI models on Pentagon classified systems — via a Palantir integration under the Maven Smart System contract — is excluded after a dispute over the guardrails governing military and surveillance use of its AI.
The Pentagon had previously branded Anthropic a "supply chain risk," a designation typically reserved for foreign entities posing national security concerns. A March 2026 federal injunction reversed that designation, but it did not restore Anthropic's position as a Pentagon AI vendor. Palantir has pulled its Claude models from its DoD platforms accordingly.
The exclusion has strategic implications that extend beyond one company's contract status. Anthropic's recently released Mythos model — described by Treasury Secretary Scott Bessent as representing a step change in large language model capability — has generated significant attention from U.S. officials and financial sector executives about its potential to supercharge adversarial cyber operations.
The fact that Mythos is not among the models being assessed for classified military use, while simultaneously being cited by senior officials as a capability milestone that warrants concern, creates a gap in the government's stated AI security posture that is difficult to characterize as anything other than a policy contradiction.

Attackers have found a way to intercept SMS-based one-time passwords from a victim's mobile device without deploying a single line of malware on the phone itself. Instead, they go through the Windows PC the phone is already connected to.
Researchers documented an active intrusion campaign active since at least January 2026, that combines a remote access trojan called "CloudZ" with a previously undocumented plugin named "Pheno." Together the two tools are designed to steal credentials and harvest authentication codes that arrive on a victim's phone by abusing Microsoft Phone Link, a legitimate Windows application built into every Windows 10 and 11 system.
Microsoft Phone Link, formerly "Your Phone," is a synchronization tool that bridges a user's Android or iOS device to their Windows PC, mirroring calls, messages, and app notifications directly onto the desktop.
Pheno exploits that bridge. It continuously scans running processes for keywords including "YourPhone," "PhoneExperienceHost," and "Link to Windows" to detect an active phone connection. When one is found, the plugin writes "Maybe connected" to a local staging file and gains access to the Phone Link application's local SQLite database. It is a file that can contain SMS messages and authenticator app notification content, including OTP codes.
The attack never targets the mobile device directly. It targets the enterprise-managed Windows endpoint the device trusts, bypassing security controls focused on securing smartphones rather than the desktop layer they sync with.
CloudZ is a modular .NET RAT compiled on January 13, and obfuscated with ConfuserEx. Beyond loading Pheno, it supports credential harvesting from web browsers, file operations, remote command execution, and host profiling.
It establishes an encrypted TCP connection to its command-and-control server and rotates between three hardcoded user-agent strings to make its traffic blend with legitimate browser requests. To evade analysis, CloudZ detects .NET debuggers and profilers via environment variable queries and generates its executable functions dynamically in memory — meaning the most sensitive code never sits as a static binary on disk.
The infection chain begins with a fake ScreenConnect application update. ScreenConnect is a legitimate remote support tool commonly used in enterprise environments. Executing the fake update drops a Rust-compiled loader, which in turn deploys a .NET loader that installs CloudZ and establishes persistence via a scheduled task. The .NET loader performs thorough sandbox checks, scanning for analysis tools including Wireshark, Fiddler, Procmon, and Sysmon before proceeding.
Cisco Talos researchers did not attribute the campaign to a known threat actor. The initial access vector also remains unidentified.
