CVE-2026-65150 in DALI
Summary
by MITRE • 10/13/2026
NVIDIA DALI contains a vulnerability where an attacker could cause an uncontrolled out-of-bounds read. A successful exploit of this vulnerability mighty lead to information disclosure, data tampering, and denial of service.
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Analysis
by VulDB Data Team • 10/13/2026
The NVIDIA Data Loading Library (DALI) is a high-performance software library designed for accelerating data loading pipelines in deep learning applications. It provides optimized operations for reading, decoding, augmenting, and preprocessing images and videos, serving as a critical component in the training infrastructure of many machine learning models. The identified vulnerability within this library represents a significant security risk because it allows an attacker to trigger an uncontrolled out-of-bounds read operation. This type of flaw typically arises when software accesses memory locations beyond the intended boundaries of a buffer or data structure, often due to insufficient validation of input parameters such as array indices, string lengths, or file offsets before performing memory access operations.
From a technical perspective, this vulnerability is classified under CWE-125, which denotes Out-of-bounds Read. The root cause likely involves a failure in boundary checking logic within the data processing routines of DALI. When an attacker supplies crafted input that exceeds expected limits, the application may read from adjacent memory regions that were not intended to be accessed by the current execution context. This behavior violates the principle of least privilege and proper memory isolation, allowing the process to retrieve sensitive information stored in neighboring memory blocks or causing undefined behavior depending on how the operating system handles such illegal access attempts.
The operational impact of exploiting this vulnerability is multifaceted and severe. Primarily, it leads to information disclosure, where an attacker can potentially extract confidential data from the application's memory space. This could include proprietary model weights, training datasets containing personally identifiable information, or internal state variables that reveal system architecture details. Furthermore, if the out-of-bounds read interacts with critical control structures or triggers subsequent write operations through related bugs, it may lead to data tampering, compromising the integrity of the machine learning pipeline. In many cases, such memory corruption issues also result in application crashes, leading to a denial of service that disrupts training jobs and wastes computational resources allocated by organizations relying on stable deep learning environments.
This vulnerability aligns with several tactics defined in the MITRE ATT&CK framework. Specifically, it facilitates Initial Access or Execution phases where an attacker might leverage memory corruption for reconnaissance purposes, mapping out the internal state of the system before launching more destructive attacks. It also relates to Defense Evasion techniques if the crash is used to bypass security controls that rely on application stability. The lack of proper input validation places this flaw squarely in the category of improper restriction of operations within the bounds of a memory buffer, highlighting the need for rigorous static and dynamic analysis during software development.
To mitigate this risk, immediate action should be taken by updating NVIDIA DALI to the latest patched version that addresses these boundary checking issues. Developers integrating DALI into their pipelines must ensure they are using supported releases where such vulnerabilities have been resolved. Additionally, implementing defense-in-depth strategies is crucial. This includes deploying runtime application self-protection tools or memory safety analyzers in production environments to detect abnormal memory access patterns. For organizations building custom data loaders based on DALI, it is imperative to enforce strict input validation and sanitization protocols at the entry points of all data processing functions. Regular security audits focusing on C++ codebases for common pitfalls like buffer overflows will further reduce the attack surface associated with high-performance computing libraries.