CVE-2026-9040 in Bifrost GPU Kernel Driver
Summary
by MITRE • 09/08/2026
A race condition vulnerability in Arm Ltd Bifrost GPU Kernel Driver, Arm Ltd Valhall GPU Kernel Driver, Arm Ltd Arm 5th Gen GPU Architecture Kernel Driver allows a local non-privileged user process to perform improper GPU memory processing operations to cause a denial of service or disclose sensitive information.
This issue affects Bifrost GPU Kernel Driver: from r12p0 through r49p5, from r50p0 through r51p0, from r54p1 through r54p2; Valhall GPU Kernel Driver: from r19p0 through r49p5, from r50p0 through r54p3, r55p0; Arm 5th Gen GPU Architecture Kernel Driver: from r41p0 through r49p5, from r50p0 through r54p3, r55p0.
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Analysis
by VulDB Data Team • 09/08/2026
The identified vulnerability represents a critical race condition within the kernel-level drivers for Arm’s Bifrost, Valhall, and 5th Generation GPU architectures. This flaw arises from improper synchronization mechanisms during memory management operations, allowing local non-privileged user processes to exploit timing windows where state checks and subsequent actions are not atomic. In modern operating systems, the graphics processing unit driver serves as a privileged interface between user-space applications and hardware resources. When this interface lacks robust concurrency controls, an attacker can manipulate the sequence of memory allocation, mapping, or deallocation requests to trigger undefined behavior within the kernel space. The core technical flaw lies in the failure to properly serialize access to shared GPU memory structures, leading to scenarios where a process can read from or write to memory regions that have been freed or are being reallocated by another context. This lack of atomicity creates a window of opportunity for race conditions, which is a classic manifestation of CWE-362: Concurrent Execution Using Shared Resource with Improper Synchronization.
The operational impact of this vulnerability is twofold, encompassing both confidentiality and availability compromises. On the one hand, an attacker can leverage the improper memory processing to read sensitive data residing in GPU memory buffers that should be isolated from user-space access. This could include cryptographic keys, session tokens, or other proprietary information processed by graphics-intensive applications running on the device. The disclosure of such information constitutes a severe breach of confidentiality and aligns with ATT&CK technique T1074: Data Staged, where data is collected locally before exfiltration. On the other hand, the race condition can lead to system instability or crashes when invalid memory addresses are accessed or corrupted kernel structures cause exceptions in privileged mode. This results in a denial of service against the local device and potentially any networked resources if the compromise escalates further. The availability impact is consistent with CWE-400: Uncontrolled Resource Consumption, as the exploitation can exhaust system resources or crash the graphics subsystem entirely.
The scope of this vulnerability affects multiple generations of Arm GPU drivers, indicating a systemic issue in how these specific driver versions handle concurrent memory operations. Affected components include the Bifst GPU Kernel Driver across revisions r12p0 through r49p5 and r50p0 through r51p0, as well as ranges extending to r54p2; the Valhall GPU Kernel Driver from r19p0 through r49p5 and r50p0 through r55p0; and the Arm 5th Gen GPU Architecture Kernel Driver from r41p0 through r49p5 and r50p0 through r55p0. This widespread impact suggests that the underlying architectural approach to memory management in these driver versions requires significant revision rather than simple patching of isolated code paths. The presence of this vulnerability in such a broad range of revisions highlights the complexity of maintaining synchronization correctness across evolving hardware architectures and software stacks.
Mitigation strategies must focus on both immediate remediation and long-term architectural improvements. For system administrators, applying vendor-provided patches that update the GPU kernel drivers to versions outside the affected ranges is the primary defense. These updates typically introduce stricter locking mechanisms or use atomic operations to ensure that memory state checks and modifications occur without interruption from other threads or processes. Additionally, implementing strict sandboxing policies for untrusted applications can limit the potential impact by restricting their ability to interact directly with GPU memory pools. From a development perspective, adopting formal verification methods for concurrency logic and integrating static analysis tools that detect race conditions during the software development lifecycle is essential. Furthermore, enforcing least-privilege principles ensures that even if an exploit succeeds in triggering the race condition, the attacker’s access remains confined within user-space boundaries unless additional escalation vectors are present. Continuous monitoring of system logs for unusual GPU driver activity can also aid in detecting attempted exploitation attempts before they result in significant damage.