CVE-2026-24181: NVIDIA DALI RCE Vulnerability

CVE-2026-24181 Overview

CVE-2026-24181 affects NVIDIA Data Loading Library (DALI), a GPU-accelerated library for data loading and preprocessing in deep learning workflows. The vulnerability results from improper index validation [CWE-129] within a DALI component. A local attacker with low privileges can supply crafted input that bypasses index bounds checks during processing. Successful exploitation may lead to code execution, data tampering, denial of service, and information disclosure. The flaw requires user interaction and operates over a local attack vector, limiting remote exposure. NVIDIA has published a support advisory addressing the issue.

Critical Impact

Local exploitation of CVE-2026-24181 can result in arbitrary code execution, data tampering, denial of service, and disclosure of sensitive information processed through DALI pipelines.

Affected Products

• NVIDIA DALI (Data Loading Library)
• Deep learning training and inference workflows that ingest data through DALI pipelines
• GPU-accelerated machine learning environments embedding the vulnerable DALI component

Discovery Timeline

• 2026-06-09 - CVE-2026-24181 published to the National Vulnerability Database
• 2026-06-09 - Last updated in the NVD database

Technical Details for CVE-2026-24181

Vulnerability Analysis

The vulnerability is classified under [CWE-129]: Improper Validation of Array Index. A DALI component accepts an index value used to access an array, buffer, or similar indexed structure without sufficient validation against the boundaries of the underlying allocation. When the index is attacker-influenced, memory regions outside the intended bounds become reachable for read or write operations.

The attack vector is local and requires the attacker to hold low privileges on the target host. User interaction is also required, meaning a victim must initiate a workflow that loads attacker-supplied data or configuration through the vulnerable DALI pathway. Despite these prerequisites, the consequences span confidentiality, integrity, and availability, which reflects the breadth of operations that DALI performs during data preprocessing.

Root Cause

The root cause is missing or insufficient bounds checking on an index value consumed by a DALI internal routine. Without verification that the index falls within the allocated range, the component performs operations on adjacent memory, enabling corruption of in-process structures, leakage of memory contents, or process termination.

Attack Vector

An attacker with local access and standard user privileges supplies crafted input — for example, a malicious dataset, configuration file, or pipeline parameter — to a workflow that uses DALI. When the user runs the workflow, DALI processes the input and reaches the vulnerable code path. The improperly validated index then drives out-of-bounds memory access, which can be steered toward code execution, manipulation of training data or model artifacts, process crashes, or extraction of sensitive in-memory information. See the NVIDIA Support Document for vendor technical details.

Detection Methods for CVE-2026-24181

Indicators of Compromise

• Unexpected crashes, segmentation faults, or aborts in processes that load the DALI library during training or inference
• DALI pipelines processing datasets or configuration files from untrusted or external sources
• Anomalous child process creation or shell activity originating from Python interpreters running DALI workloads
• Modifications to model checkpoints, training datasets, or preprocessing scripts that cannot be tied to authorized changes

Detection Strategies

• Inventory hosts and containers that ship the DALI library and correlate installed versions against the fixed release identified in the NVIDIA advisory
• Monitor for process execution and library load events involving DALI shared objects on GPU workstations and ML training nodes
• Apply file integrity monitoring to datasets, pipeline definitions, and DALI binaries used in production ML environments
• Review GPU workload telemetry for crashes or memory faults that coincide with new or modified input data

Monitoring Recommendations

• Centralize logs from ML training infrastructure, including container runtimes and Python application logs, to detect abnormal terminations
• Track command-line arguments and environment variables passed to DALI-backed workflows for unexpected file paths
• Alert on writes to model artifact directories from processes other than approved training jobs
• Baseline normal DALI memory and CPU usage so out-of-bounds behavior producing crashes or hangs surfaces quickly

How to Mitigate CVE-2026-24181

Immediate Actions Required

• Apply the NVIDIA-supplied update for DALI as described in the NVIDIA Support Document
• Restrict local access to systems running DALI workloads to trusted users and service accounts
• Treat all datasets, model configuration files, and pipeline definitions from external sources as untrusted until validated
• Rebuild container images and ML environments to incorporate the patched DALI version

Patch Information

NVIDIA has published guidance for CVE-2026-24181 in its security bulletin. Refer to the NVIDIA Support Document for the fixed DALI version and upgrade instructions. Additional metadata is available at the NIST CVE-2026-24181 record and the CVE.org Record for CVE-2026-24181.

Workarounds

• Run DALI workloads inside isolated containers or virtual machines with minimal host privileges to contain potential exploitation
• Validate dataset structure and metadata before feeding inputs into DALI pipelines, rejecting files with malformed or out-of-range index fields
• Disable or remove DALI from systems where it is not actively required until the patched version is deployed
• Enforce least privilege on accounts that can submit or modify ML pipelines, reducing the pool of potential local attackers

# Verify the installed DALI version and upgrade to the fixed release
pip show nvidia-dali-cuda120 | grep -i version
pip install --upgrade nvidia-dali-cuda120