CVE-2026-65115 in Infrastructure Controller
Summary
by MITRE • 09/22/2026
NVIDIA Infrastructure Controller for Linux contains a vulnerability where an attacker may cause uncontrolled resource consumption. A successful exploit of this vulnerability may lead to denial of service.
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Analysis
by VulDB Data Team • 09/22/2026
The NVIDIA Infrastructure Controller for Linux, often referred to as the NVIDIA Management Library or related daemon components within the data center infrastructure stack, is susceptible to a flaw that allows for uncontrolled resource consumption. This type of vulnerability typically arises when the software fails to properly validate input parameters or manage state transitions during specific administrative operations or health checks performed by the controller agent. In enterprise environments where these controllers are responsible for monitoring GPU temperature, power limits, clock speeds, and hardware status across multiple nodes, the integrity of the management plane is critical. When an attacker can trigger a sequence of requests that forces the controller to enter a loop or allocate excessive memory without releasing it, they effectively exploit a resource exhaustion condition. This flaw does not necessarily require authentication if the affected endpoints are exposed on internal network segments with insufficient access controls, although many such services may be protected by local system permissions depending on the specific deployment configuration and version of the NVIDIA software suite.
From a technical perspective, this vulnerability aligns closely with CWE-787: Out-of-bounds Write or CWE-400: Uncontrolled Resource Consumption, often manifesting as a denial of service through memory leaks or CPU saturation. The operational impact is severe because the Infrastructure Controller serves as a central point for telemetry and configuration management in high-performance computing clusters. If the process responsible for managing these resources crashes or becomes unresponsive due to resource exhaustion, it can lead to a cascading failure where monitoring tools lose connectivity with GPU nodes. This results in blind spots regarding hardware health, potentially masking overheating events or power fluctuations that could damage physical infrastructure. Furthermore, if the controller is integrated with orchestration platforms like Kubernetes via device plugins, its unavailability may prevent new workloads from being scheduled on affected nodes, thereby reducing overall cluster throughput and availability.
In terms of threat modeling, this vulnerability maps to MITRE ATT&CK technique T1499: Endpoint Denial of Service, specifically under the sub-technique for resource exhaustion attacks. An adversary could leverage automated scripts to send a high volume of malformed or repetitive requests to the management interface, forcing the Linux-based service to consume all available system resources such as RAM or CPU cycles on the host machine running the NVIDIA driver stack. This is particularly dangerous in multi-tenant environments where one compromised container or virtual machine might attempt to impact neighboring workloads by exhausting shared kernel resources managed by the GPU drivers. The lack of rate limiting, input sanitization for internal API calls, or proper timeout mechanisms in older versions of the management software exacerbates this risk, allowing a low-effort attack vector to achieve high-impact disruption.
Mitigation strategies must focus on both immediate remediation and long-term architectural hardening. The primary defense is to apply the latest patches provided by NVIDIA for the specific version of the Infrastructure Controller and GPU driver stack in use. These updates typically include fixes that enforce stricter input validation, implement resource quotas, or add timeout mechanisms to prevent infinite loops during state queries. Additionally, network segmentation plays a crucial role; management interfaces should never be exposed directly to untrusted networks and must be restricted to authorized administrative subnets using firewall rules. Implementing strict access control lists on the local system level ensures that only privileged processes can interact with the daemon. For environments utilizing container orchestration, it is advisable to configure resource limits for any containers interacting with NVIDIA device plugins to prevent a single workload from monopolizing host resources. Regular vulnerability scanning and monitoring of system metrics such as memory usage by the nvidia-smi process or related daemons can also help detect anomalous behavior indicative of an ongoing exploitation attempt before total service failure occurs.