CVE-2026-65178 in NeMo
Summary
by MITRE • 09/22/2026
NVIDIA NeMo contains a vulnerability in its dataset-loading workflow where a maliciously crafted model_config.yaml can inject unsafe parameters. A successful exploit of this vulnerability may lead to code execution, data tampering, denial of service, and information disclosure.
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Analysis
by VulDB Data Team • 09/22/2026
The NVIDIA NeMo framework serves as a comprehensive toolkit for building conversational AI models, handling tasks such as automatic speech recognition, natural language processing, and text-to-speech synthesis. Within this ecosystem, the dataset-loading workflow is critical for ingesting training data from various sources into memory or storage pipelines. A significant security flaw has been identified in how NeMo processes configuration files during this ingestion phase. Specifically, when a user provides a model_config.yaml file to define parameters for loading datasets, the framework fails to adequately sanitize or validate certain input fields before passing them to underlying system commands or serialization mechanisms. This lack of rigorous validation allows an attacker who controls the content of the configuration file to inject arbitrary and unsafe parameters into the execution context.
The technical nature of this vulnerability centers on improper neutralization of special elements used in a command, often categorized under CWE-78 Improper Neutralization of Special Elements used in an OS Command or CWE-502 Deserialization of Untrusted Data depending on the specific implementation path exploited within NeMo's Python-based architecture. When the framework parses the YAML configuration, it interprets certain values as executable instructions rather than static data strings. This behavior enables a malicious actor to craft a model_config.yaml file containing payloads that leverage built-in Python functions or external libraries invoked by the dataset loader. By manipulating these parameters, an attacker can bypass intended security boundaries and execute arbitrary code within the environment running NeMo with the privileges of the user executing the script.
The operational impact of this vulnerability is severe due to its potential for remote code execution if the configuration file originates from a trusted but compromised source or a malicious third-party model repository. A successful exploit allows an attacker to achieve full control over the affected system, leading to unauthorized data tampering where training datasets or model weights can be altered to introduce backdoors or bias into AI models. Furthermore, the vulnerability facilitates information disclosure by allowing attackers to read sensitive files from the host system that are accessible to the process user. In scenarios involving resource-intensive dataset processing, an attacker could also trigger a denial of service condition by exhausting system resources through infinite loops or memory allocation attacks embedded within the crafted configuration parameters.
Mitigation strategies must focus on strict input validation and secure coding practices within the data ingestion pipeline. Developers should implement allow-listing for all configurable parameters in model_config.yaml to ensure that only expected values are processed, thereby preventing the injection of arbitrary code snippets. Utilizing safe deserialization libraries instead of generic parsers can also reduce the risk associated with untrusted YAML inputs. For end-users, it is imperative to treat configuration files from unknown or unverified sources as potentially malicious and avoid loading them in production environments without thorough inspection. NVIDIA has released patches that address these validation gaps, so applying the latest updates to NeMo is essential for maintaining a secure AI development infrastructure aligned with industry standards such as OWASP Top 10 risks related to injection and broken access control.