Technology · United States

High-severity Nvidia bug could crash GPU monitoring on exposed servers

Credited to The Register · theregister.com · 7h ago

Photograph · The Register

The GPU giant released a fix for the flaw, tracked as CVE-2026-47483

Researchers found thousands of GPU servers exposing Nvidia's DCGM Exporter to the internet, with hundreds potentially vulnerable to a high-severity flaw that could let unauthenticated attackers crash the GPU monitoring service and disrupt AI workloads.

DCGM Exporters read telemetry from the GPUs on a host, including its hardware, utilization, memory usage, power consumption, and error events. Each GPU has its own unique ID, or UUID, and all of these metrics are exposed in plaintext over HTTP.

This exposure provides would-be attackers with detailed information useful for reconnaissance, including mapping GPU infrastructure, identifying potentially vulnerable systems, and monitoring workload activity.

Michael Katchinskiy, a researcher at datacenter security startup Lava, found and reported the bug in the GPU health and performance monitoring service. In September, the GPU giant Nvidia released a fix for the flaw, tracked as CVE-2026-4748 3, and gave it an 8.2 CVSS high-severity rating.

“Once we realized how much these endpoints revealed, the next question was: How many of them are exposed to the internet?” Katchinskiy said in a Thursday blog.

So the researchers started scanning the internet for exposed DCGM Exporters. And the scale of exposure proved “especially significant,” he wrote.

Over the course of four scans between March and May, the threat hunters found about 2,100 GPU servers exposing DCGM Exporter metrics to the open internet. These included 12,000 GPU UUIDs. None of these required authentication.

The hosts belonged to about 300 organizations, according to Katchinskiy, and nearly half - 5,274 of the exposed GPUs, or 44 percent of the total - were located in the US.

These GPUs represented about $100 million in hardware, and included Nvidia Blackwell Ultra B300 GPUs, H200s, and H100s - used to run large-scale AI workloads - plus consumer RTX 5090 and 4090 systems.

While investigating the exposed systems, the Lava team found that about 25 percent of the exposed DCGM hosts also revealed data from Go’s /debug/pprof/ built-in profiling tool. The profiler collects and exposes runtime performance data such as CPU and memory usage for running Go applications. This includes CPU usage, memory allocations, goroutine states, and blocking events.

“With enough concurrent unauthenticated requests, the exporter could run out of memory and crash, cutting off visibility into GPU health and activity,” Katchinskiy wrote.

The CPU and memory pressure could also affect AI training or inference workloads.

Nvidia fixed the issue in version 4.8.2, and operators should upgrade to that version or later.

In addition to GPU telemetry, Lava looked into Prometheus Node Exporter, which monitors server hardware and operating systems, and also exposes metrics over HTTP. The team found 12,096 public Node Exporter hosts exposing data on server models, operating systems, firmware versions, hostnames, storage paths and networking hardware commonly used in GPU clusters.

This information reveals how environments are built and configured, which could also be used by attackers for reconnaissance, matching the system to known vulnerabilities.

The publicly exposed monitoring services affected customer infrastructure across neocloud and GPU cloud providers including Nebius, Voltage Park, Lambda, Northern Data, and DigitalOcean. Lava reported all of this to the affected providers, and Katchinskiy says that these providers worked with customers to address the exposures.

For operators: the security shop says Nvidia DCGM Exporter, Node Exporter and Prometheus services should not be directly reachable from the public internet and recommends restricting them to authorized monitoring infrastructure.

“The findings highlight a growing security gap in AI infrastructure: companies are spending millions on GPUs while leaving critical systems exposed,” Katchinskiy wrote. “Those exposures can reveal how AI environments are built and, in some cases, allow attackers to disrupt them.” ®

Chips & SupplyUnited States

Networking Startup Eliyan Draws Takeover Interest, Held Early Talks With Arm

Credited to The Information$ · theinformation.com1h ago

Useful

Semiconductor M&A has been humming. One of the next deals could be for Eliyan, whose technology allows separate chips inside an AI system to talk to one another more quickly and efficiently. The five-year-old startup, valued at $1 billion in July, has notified some of its…

View the article · $The Information

Front Method

Technology · United States

Gallatin AI raises $50M in funding for its military logistics platform

Credited to SiliconANGLE · siliconangle.com · 2h ago

Photograph · SiliconANGLE

Gallatin AI Inc., a developer of military logistics software, today announced that it has raised $50 million in funding.

The Series A round included contributions from 8VC, Silent Ventures and several others. It follows a $15 million seed raise in 2024.

El Segundo, California-based Gallatin has built a software platform called Navigator that helps the U.S. military manage its supply chain. The company counts the U.S. Army and U.S. Air Force as customers. According to Gallatin, Navigator can also be used by the U.S. Defense Department’s commercial logistics partners. The company says that the software streamlines the movement of supplies and saves time for logistics teams in the process.

“The opportunity in front of us is bigger than building better software,” said Gallatin Chief Product Officer Brian Ballard. “It is making logistics a source of operational advantage, and giving sustainers the ability to make better decisions before constraints become mission problems.”

Determining what supplies are needed and where involves a significant amount of manual work. Logistics teams must review lengthy inventory reports and short, ad hoc supply requests transmitted via systems such as the U.S. military’s internal chat tools. In some cases, it’s also necessary to analyze data from vehicle sensors. Such sensors can detect when an important commodity such as fuel needs to be replenished.

Navigator automates the data collection workflow. It aggregates supply chain records from multiple systems and organize them in a standardized form. From there, the software uses artificial intelligence to visualize the data in dashboards.

It also lists all the assets that a logistics team is responsible for supporting. If those assets are aircraft, the platform might show details such as the fuel, corrosion prevention sprays and spare parts they require. The software turns some of that information into graphs, while other data points are overlaid on a map. There’s also a spreadsheet-like record viewer.

Navigator uses AI to predict when supplies will run out. It generates forecasts based on data points such as current inventory levels and historical consumption patterns. Furthermore, the software adjusts its estimates in response to new events such as an unexpected consumption spike.

Logistics teams can use the information surface by Navigator to develop resupply plans. According to Gallatin, the platform’s built-AI automatically flags areas for improvement in those plans. It also automates certain related tasks such as creating administrative documents.

Gallatin will use the proceeds from its funding round to enhance Navigator’s AI capabilities. The company will hire more engineers to support the effort.

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