CVE-2026-24156: NVIDIA DALI RCE Vulnerability

CVE-2026-24156 Overview

CVE-2026-24156 is a high-severity insecure deserialization vulnerability in NVIDIA DALI (Data Loading Library). The vulnerability allows an attacker to cause deserialization of untrusted data, which could lead to arbitrary code execution on affected systems. NVIDIA DALI is a GPU-accelerated library for data loading and augmentation commonly used in deep learning pipelines.

Critical Impact

Successful exploitation of this deserialization vulnerability could allow attackers to execute arbitrary code with the privileges of the application using NVIDIA DALI, potentially leading to complete system compromise.

Affected Products

• NVIDIA DALI (Data Loading Library)

Discovery Timeline

• 2026-04-07 - CVE-2026-24156 published to NVD
• 2026-04-08 - Last updated in NVD database

Technical Details for CVE-2026-24156

Vulnerability Analysis

This vulnerability falls under CWE-502 (Deserialization of Untrusted Data), a class of vulnerabilities where an application deserializes data from untrusted sources without proper validation. In the context of NVIDIA DALI, the library processes serialized data during data loading operations for machine learning workflows. When untrusted or maliciously crafted serialized data is processed, the deserialization mechanism can be manipulated to instantiate arbitrary objects or execute code during the deserialization process.

The local attack vector means an attacker would need some level of access to the system or the ability to supply malicious input data to the DALI pipeline. This could occur through poisoned training datasets, malicious model files, or other data inputs processed by DALI. The requirement for user interaction (opening a malicious file or dataset) provides some mitigation but does not eliminate the risk in automated pipeline environments.

Root Cause

The root cause of this vulnerability lies in NVIDIA DALI's handling of serialized data without adequate validation. When deserializing objects, the library fails to properly restrict the types of objects that can be instantiated, allowing an attacker to inject malicious payloads that get executed during the deserialization process. This is a common pattern in insecure deserialization vulnerabilities where the application trusts the incoming serialized data implicitly.

Attack Vector

The attack requires local access to the system with low privileges and some user interaction. An attacker could craft a malicious serialized payload and deliver it through:

1. A poisoned training dataset containing malicious serialized objects
2. A compromised model checkpoint or pipeline configuration file
3. Input data processed through DALI's data loading functions

When DALI deserializes this malicious payload, the attacker-controlled code is executed, potentially leading to arbitrary code execution with the privileges of the process running the DALI library—often with access to sensitive machine learning data and compute resources.

The deserialization vulnerability in NVIDIA DALI allows arbitrary object instantiation during data loading operations. When the library processes serialized data, it fails to validate object types before instantiation, enabling attackers to inject malicious payloads. See the NVIDIA Support Article for detailed technical information about the affected components and exploitation conditions.

Detection Methods for CVE-2026-24156

Indicators of Compromise

• Unexpected process spawning from applications using NVIDIA DALI
• Anomalous network connections initiated by machine learning pipeline processes
• Unusual file system activity in directories containing training data or model files
• Process memory anomalies or injection signatures in DALI-utilizing applications

Detection Strategies

• Monitor for suspicious deserialization patterns in application logs related to DALI operations
• Implement file integrity monitoring on training datasets and model checkpoint directories
• Deploy endpoint detection rules for known deserialization exploitation techniques
• Analyze DALI pipeline configurations for references to untrusted data sources

Monitoring Recommendations

• Enable verbose logging for DALI operations to capture deserialization events
• Implement runtime application self-protection (RASP) for Python applications using DALI
• Monitor for unusual resource consumption patterns in machine learning workloads
• Set up alerts for code execution attempts from data processing contexts

How to Mitigate CVE-2026-24156

Immediate Actions Required

• Review and restrict access to systems running NVIDIA DALI to authorized users only
• Audit all data sources processed by DALI pipelines to ensure they are from trusted origins
• Implement input validation for any serialized data before processing with DALI
• Isolate machine learning workloads using DALI in sandboxed or containerized environments

Patch Information

NVIDIA has released information regarding this vulnerability. Organizations should consult the NVIDIA Support Article for official patch information and upgrade instructions. Upgrade to the latest patched version of NVIDIA DALI as soon as it becomes available in your environment.

Workarounds

• Avoid processing serialized data from untrusted or unverified sources with DALI
• Implement strict access controls on training data and model file directories
• Run DALI pipelines in isolated environments with minimal privileges using containers or VMs
• Apply application-level input validation to filter potentially malicious serialized content

If immediate patching is not possible, consider implementing network-level isolation for systems running DALI workloads and restricting which users and processes can supply data to the DALI pipeline. Ensure all training data and model files are sourced from verified, trusted repositories only.