CVE-2026-65149 in DALI
Summary
by MITRE • 10/13/2026
NVIDIA DALI contains a vulnerability where an attacker could cause an uncontrolled out-of-bounds write. A successful exploit of this vulnerability might lead to denial of service, information disclosure, and data tampering.
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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 pipelines. It provides optimized kernels for image decoding, augmentation, and other transformations that are critical for training neural networks efficiently. The identified vulnerability resides within this core component, specifically involving memory management operations during the processing of input tensors or images. This flaw represents a classic instance of an uncontrolled out-of-bounds write, where the application writes data to a memory location outside the boundaries of the intended buffer. Such errors typically arise from insufficient validation of array indices or incorrect calculations regarding buffer sizes and offsets during runtime execution.
From a technical perspective, this vulnerability allows an attacker who can control the input data fed into DALI to manipulate the program's heap or stack memory arbitrarily. By crafting specific malformed inputs that trigger these boundary violations, an adversary can overwrite adjacent memory structures. This capability fundamentally breaks the isolation guarantees of the application environment. The immediate consequence is often a crash resulting in a denial of service, as the operating system detects invalid memory access and terminates the process to prevent further corruption. However, the implications extend far beyond simple availability loss.
The ability to perform an out-of-bounds write enables more severe attacks such as arbitrary code execution or privilege escalation if the vulnerable component runs with elevated privileges. Even without full remote code execution, the vulnerability facilitates information disclosure by allowing the reading of sensitive data stored in adjacent memory regions through subsequent read operations that leverage the corrupted state. Furthermore, it permits data tampering, where an attacker can alter critical variables within the application's runtime environment, potentially bypassing security checks or modifying model parameters during training to introduce backdoors or degrade performance integrity.
This vulnerability aligns with Common Weakness Enumeration (CWE) category CWE-787: Out-of-bounds Write, which describes writing data outside of the intended buffer boundaries. In terms of offensive cybersecurity frameworks, this flaw supports techniques associated with MITRE ATT&CK tactic TA0004: Privilege Escalation and technique T1203: Exploitation for Client Execution if leveraged in a client-side context, or general exploitation patterns under TA0005: Defense Evasion. The lack of proper bounds checking is a fundamental software defect that undermines the integrity of memory operations within high-performance computing libraries like DALI.
Mitigation strategies must focus on both immediate remediation and long-term defensive coding practices. NVIDIA has likely released patches or updated versions of the library that include rigorous input validation and enhanced boundary checks to prevent writes beyond allocated memory segments. Organizations utilizing NVIDIA DALI should immediately update their dependencies to the latest secure version. Additionally, developers integrating this library into larger applications should implement defense-in-depth measures such as enabling Address Sanitizer (ASan) during development and testing phases to detect out-of-bounds accesses early in the software lifecycle. Runtime protection mechanisms like Data Execution Prevention (DEP) and Stack Canaries can also help mitigate the impact of exploitation attempts by preventing code execution from corrupted memory regions, although they do not fix the underlying vulnerability itself.