CVE-2024-0116 in Triton Inference Server
Summary
by MITRE • 10/01/2024
NVIDIA Triton Inference Server contains a vulnerability where a user may cause an out-of-bounds read issue by releasing a shared memory region while it is in use. A successful exploit of this vulnerability may lead to denial of service.
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Analysis
by VulDB Data Team • 09/29/2025
The vulnerability identified as CVE-2024-0116 affects NVIDIA Triton Inference Server, a widely used platform for deploying machine learning models in production environments. This server architecture employs shared memory mechanisms to optimize performance and reduce memory overhead when handling inference requests across multiple model instances. The flaw manifests in the memory management subsystem where the software fails to properly synchronize access to shared memory regions during deallocation operations. When a user releases a shared memory segment while it remains actively referenced by ongoing inference operations, the system attempts to access memory locations that may have already been freed or are in an undefined state.
This out-of-bounds read condition represents a critical memory safety issue that falls under the CWE-125 weakness category, specifically addressing out-of-bounds read vulnerabilities in memory management operations. The vulnerability is particularly concerning in production environments where Triton Server handles concurrent inference requests from multiple clients, as the shared memory mechanism is designed to improve throughput by allowing multiple processes or threads to access the same memory region simultaneously. The improper synchronization between memory release operations and active memory usage creates a race condition that can result in unpredictable behavior when the system attempts to read from memory locations that have been prematurely deallocated.
The operational impact of this vulnerability extends beyond simple denial of service conditions, as it can compromise the stability and reliability of machine learning inference pipelines that depend on Triton Server. When the out-of-bounds read occurs, the system may experience crashes, segmentation faults, or corrupted memory states that can affect multiple concurrent inference requests. This vulnerability is particularly dangerous in high-throughput environments where shared memory regions are frequently allocated and deallocated, as the likelihood of triggering the race condition increases with system load and concurrent access patterns. The exploitability of this vulnerability is enhanced when attackers can control the timing of memory release operations, potentially leading to sustained denial of service attacks that can render the entire inference server unavailable to legitimate users.
Mitigation strategies for CVE-2024-0116 should focus on implementing proper memory synchronization mechanisms and access controls within the shared memory management subsystem. Organizations should ensure that all shared memory regions are properly reference-counted and only released when no active consumers remain, implementing robust locking mechanisms to prevent concurrent access during deallocation operations. The recommended approach involves adding proper synchronization primitives such as mutexes or semaphores around memory release operations to ensure that no active threads are accessing shared memory regions before deallocation occurs. Additionally, implementing memory safety checks and bounds verification within the shared memory management code can help detect and prevent out-of-bounds access attempts. System administrators should also consider applying the latest security patches from NVIDIA as soon as they become available, while monitoring for unusual system behavior or performance degradation that might indicate exploitation attempts. The ATT&CK framework categorizes this vulnerability under the T1499.004 technique for network denial of service, as the successful exploitation can render the inference server unavailable to legitimate users, disrupting machine learning workflows and potentially affecting business operations that depend on automated inference capabilities.