CVE-2026-47621 in Dynamo
Summary
by MITRE • 08/04/2026
NVIDIA Dynamo for Linux contains a vulnerability where an attacker could cause a race condition in the LoRA manager singleton initialization. A successful exploit of this vulnerability might lead to denial of service and data tampering.
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Analysis
by VulDB Data Team • 08/04/2026
The vulnerability in NVIDIA Dynamo for Linux represents a critical race condition flaw within the LoRA manager singleton initialization process that exposes the system to significant security risks. This issue stems from improper synchronization mechanisms during the initialization phase of the LoRA (Low-Rank Adaptation) manager component, which is essential for dynamic model adaptation in machine learning workflows. The race condition occurs when multiple threads or processes attempt to initialize the singleton instance simultaneously, creating a window where the system state becomes inconsistent and unpredictable.
The technical implementation flaw manifests through inadequate mutex locking or semaphore controls during the singleton creation sequence. When concurrent access attempts occur, the initialization logic may execute partially or incompletely, leading to memory corruption or invalid pointer references within the LoRA manager structure. This vulnerability falls under CWE-362, which specifically addresses race conditions in software systems where multiple threads compete for shared resources without proper synchronization. The flaw is particularly dangerous because it affects a core component responsible for managing model parameters and adaptations, making it a prime target for exploitation.
From an operational impact perspective, this vulnerability creates multiple attack vectors that could result in system instability and data integrity compromise. An attacker exploiting this race condition could trigger denial of service conditions by causing the LoRA manager to crash or become unresponsive, thereby disrupting machine learning workflows and inference processes. Additionally, the inconsistent state management during initialization provides opportunities for data tampering where malicious actors might inject corrupted parameters or manipulate model configurations. The vulnerability's impact extends beyond immediate system availability to potentially compromise the integrity of trained models and their associated parameters.
The security implications align with ATT&CK technique T1499.004, which covers "Utilities: Data Destruction" through service disruption mechanisms, as well as T1566.002 for "Phishing: Spearphishing Attachment" where the race condition could be exploited through manipulated model files or training datasets. Mitigation strategies should focus on implementing robust synchronization primitives such as proper mutex acquisition before singleton initialization, adding validation checks during the initialization sequence, and ensuring thread-safe access patterns throughout the LoRA manager lifecycle. Organizations should also consider implementing monitoring solutions to detect anomalous behavior patterns that might indicate exploitation attempts.
Recommended defensive measures include updating to patched versions of NVIDIA Dynamo where proper race condition handling has been implemented, deploying input validation for all LoRA-related operations, and establishing comprehensive logging mechanisms to track singleton initialization events. Security teams should also conduct regular vulnerability assessments targeting shared resource access patterns and implement code review processes specifically focused on concurrent programming practices. The fix should ensure that the singleton initialization follows the double-checked locking pattern or similar thread-safe initialization techniques to prevent multiple simultaneous initialization attempts from corrupting system state while maintaining performance characteristics essential for machine learning workloads.