CVE-2025-23307 in NeMo Curator
Summary
by MITRE • 08/26/2025
NVIDIA NeMo Curator for all platforms contains a vulnerability where a malicious file created by an attacker could allow code injection. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.
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Analysis
by VulDB Data Team • 08/29/2025
The vulnerability identified as CVE-2025-23307 resides within NVIDIA NeMo Curator, a tool designed for data curation and processing within machine learning workflows. This security flaw represents a critical code injection vulnerability that could be exploited by malicious actors to gain unauthorized access to systems running the affected software. The vulnerability affects all platforms where NeMo Curator is deployed, making it particularly concerning given the widespread adoption of NVIDIA's machine learning frameworks across enterprise environments and research institutions. The flaw stems from insufficient input validation mechanisms within the file processing pipeline, creating opportunities for attackers to craft malicious files that can trigger unintended code execution when processed by the application.
The technical implementation of this vulnerability involves a failure in proper sanitization and validation of file inputs within the NeMo Curator framework. When the application processes user-supplied files, it does not adequately verify the integrity or content of these inputs before executing any associated code or commands. This weakness aligns with CWE-94, which describes "Improper Control of Generation of Code ('Code Injection')" and represents a classic injection attack vector. The vulnerability could be exploited through various file formats that NeMo Curator supports, potentially allowing attackers to inject malicious code that executes with the privileges of the running application. This code injection could occur during file parsing, data transformation, or metadata processing phases where the application does not properly isolate or validate external inputs.
The operational impact of this vulnerability extends beyond simple code execution, encompassing a broad range of security implications that could severely compromise affected systems. Successful exploitation could enable attackers to escalate privileges from regular user accounts to system-level access, depending on how the application is configured and deployed. The vulnerability also creates opportunities for information disclosure, allowing attackers to extract sensitive data from the system or from files processed by the application. Data tampering capabilities could be leveraged to modify training datasets or configuration files, potentially corrupting machine learning models or introducing backdoors into the training pipeline. From an attacker's perspective, this vulnerability aligns with ATT&CK technique T1059.001 for command and scripting interpreter, and T1566 for spearphishing with a malicious attachment, as it provides a mechanism for initial compromise through crafted file delivery.
Mitigation strategies for CVE-2025-23307 should prioritize immediate patching of affected systems, as NVIDIA is expected to release security updates addressing this vulnerability. Organizations should implement strict file validation policies that verify the integrity of all inputs before processing, including the use of checksums and digital signatures for critical files. Network segmentation and access controls can help limit the potential impact of exploitation by restricting access to systems running NeMo Curator. Security monitoring should be enhanced to detect unusual file processing activities or unexpected code execution patterns. Additionally, implementing application whitelisting and sandboxing mechanisms can provide defense-in-depth measures that limit the damage from successful exploitation attempts. The vulnerability underscores the importance of secure coding practices and input validation, particularly in machine learning and data processing frameworks where complex data flows create multiple potential attack vectors. Organizations should also conduct thorough security assessments of their machine learning pipelines to identify similar vulnerabilities in other components of their AI infrastructure.