CVE-2026-65148 in DALI
Summary
by MITRE • 10/13/2026
NVIDIA DALI contains a vulnerability where an attacker could cause a stack-based buffer overflow. A successful exploit of this vulnerability might lead to code execution, 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 library designed for accelerating the loading and preprocessing of data in deep learning applications. It provides optimized kernels for image, video, audio, and text processing, enabling efficient pipeline construction that feeds directly into training frameworks like PyTorch or TensorFlow. As an integral component in modern AI infrastructure, DALI handles large volumes of raw data, often performing complex transformations such as resizing, cropping, color space conversion, and normalization before the data is consumed by neural networks. The security integrity of this library is critical because any compromise can disrupt training pipelines, leak sensitive dataset information, or allow unauthorized control over the underlying compute resources where deep learning models are developed and deployed.
A recently identified vulnerability within NVIDIA DALI involves a stack-based buffer overflow condition. This type of flaw typically arises when a program writes more data to a fixed-size buffer on the call stack than it can hold, causing adjacent memory locations to be overwritten with unintended values. In the context of DALI, this likely occurs during specific operations where input parameters or internal buffers are not adequately validated for size constraints before being copied into allocated stack space. The root cause aligns with CWE-121, which classifies stack-based buffer overflows as a critical category of memory corruption vulnerabilities. These errors often stem from improper bounds checking in C or C++ code, where the developer assumes input data will always fit within predefined limits without verifying actual lengths against allocated sizes.
The operational impact of exploiting this vulnerability is severe and multifaceted. A successful exploitation allows an attacker to achieve arbitrary code execution on the affected system by overwriting critical stack metadata such as return addresses or function pointers with malicious shellcode. Beyond direct code execution, the memory corruption can lead to information disclosure if sensitive data residing in adjacent memory regions is leaked through error messages or subsequent operations. Furthermore, tampering with internal state variables can alter the behavior of the data pipeline, leading to corrupted model training outputs or inconsistent results. In worst-case scenarios, crashing the application process constitutes a denial of service condition, disrupting ongoing machine learning jobs and potentially causing resource exhaustion in cluster environments where DALI is heavily utilized for high-throughput data ingestion.
This vulnerability maps directly to MITRE ATT&CK technique T1203, which covers Exploitation for Client Execution if triggered via user interaction, or more broadly to memory corruption techniques that facilitate privilege escalation and lateral movement within a compromised environment. The ability to execute arbitrary code transforms what might initially appear as a simple data processing bug into a significant security risk capable of compromising the entire host system hosting the deep learning workload. Attackers could leverage this flaw in scenarios where untrusted or adversarial datasets are fed into the pipeline, such as in public-facing inference services or shared multi-tenant training environments.
Mitigation strategies should prioritize immediate patching to the latest version of NVIDIA DALI that addresses this specific buffer overflow issue. Developers and system administrators must ensure that all instances of the library within their deep learning infrastructure are updated consistently across both development and production clusters. Additionally, implementing defense-in-depth measures such as Address Space Layout Randomization (ASLR), stack canaries, and Data Execution Prevention (DEP) can mitigate the likelihood of successful code execution even if a buffer overflow occurs. Input validation at higher-level application layers should also be enforced to ensure that only well-formed and size-constrained data is passed into DALI pipelines, reducing the attack surface for memory corruption exploits in complex AI workflows.