CVE-2026-65142 in Model-Optimizer
Summary
by MITRE • 10/07/2026
NVIDIA Model-Optimizer contains a vulnerability where an attacker may cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, data tampering, denial of service, and information disclosure.
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Analysis
by VulDB Data Team • 10/07/2026
The NVIDIA Model Optimizer is a critical component within the AI development lifecycle, designed to convert trained machine learning models into optimized formats suitable for deployment on various hardware accelerators including GPUs, TPUs, and FPGAs. This toolchain facilitates performance tuning by applying techniques such as quantization, pruning, and graph optimization. However, recent security assessments have identified a critical vulnerability within this software that centers around the insecure handling of serialized data structures. Specifically, the application fails to adequately validate or sanitize input before processing it through deserialization routines. In many modern frameworks, serialization is used extensively for saving model states, configuration parameters, and intermediate computation graphs. When an attacker can influence these inputs, they may exploit weaknesses in how the underlying runtime interprets these serialized objects, leading to a classic insecure deserialization flaw that allows arbitrary code execution under specific conditions.
From a technical perspective, this vulnerability stems from the application's reliance on trusted data sources without sufficient verification of integrity or authenticity. Deserialization processes often reconstruct complex object graphs in memory, and if the input stream contains maliciously crafted payloads, it can trigger unintended side effects during reconstruction. These side effects may include invoking arbitrary methods, instantiating dangerous classes, or manipulating internal state variables that control execution flow. The root cause is typically a lack of strict type checking or an over-reliance on default deserialization mechanisms that do not restrict which classes are permitted to be instantiated from the input stream. This aligns with CWE-502, Deserialization of Untrusted Data, where software uses input data to construct objects without validating whether those objects pose a security risk. The flaw exists because the optimizer assumes all incoming model artifacts or configuration files originate from trusted developers rather than potentially hostile actors who might have compromised upstream repositories or intercepted distribution channels.
The operational impact of exploiting this vulnerability is severe and multifaceted, encompassing remote code execution, data tampering, denial of service, and information disclosure. If an attacker successfully crafts a malicious model file or configuration payload that triggers the deserialization flaw, they can execute arbitrary commands on the host system with the privileges of the user running the Model Optimizer. This could lead to full compromise of the development environment, allowing lateral movement within corporate networks where AI models are trained and optimized. Furthermore, data tampering is possible if the attacker modifies serialized parameters before or during processing, subtly altering model behavior in ways that degrade performance or introduce backdoors into deployed AI systems without detection. Denial of service can be achieved by crafting payloads that cause excessive memory consumption or infinite loops during deserialization, effectively crashing the optimization process and disrupting development workflows. Additionally, information disclosure may occur if the exploitation vector allows reading sensitive files from the host system through side-channel effects or error messages exposed during the crash state.
This vulnerability maps directly to several entries in the MITRE ATT&CK framework, particularly T1059 Command and Scripting Interpreter for code execution and T1203 Exploitation for Client Execution if delivered via phishing or compromised model repositories. It also relates to TA0040 Impact as it enables data manipulation and service disruption. The risk is amplified in enterprise environments where Model Optimizer is integrated into continuous integration and deployment pipelines, meaning that a single malicious commit containing a poisoned artifact could propagate through automated systems before human review catches the anomaly. To mitigate this risk, organizations must implement strict input validation policies for all model files entering the optimization pipeline. This includes verifying digital signatures of uploaded models against trusted keys to ensure integrity and authenticity. Developers should also employ allow-listing strategies where only specific, known-safe classes are permitted during deserialization processes rather than relying on default behaviors that accept any serializable object.
Security teams should further enhance their defenses by isolating the Model Optimizer execution environment using containerization or sandboxing techniques to limit the blast radius of a potential compromise. Regular updates to the NVIDIA software stack are essential as patches may address these underlying deserialization weaknesses in future releases. Additionally, implementing network segmentation can prevent compromised workstations from communicating with external repositories that might distribute malicious artifacts. Monitoring tools should be configured to detect anomalous processes spawned by Model Optimizer executions, particularly those involving shell commands or unexpected file access patterns indicative of exploitation attempts. By combining technical controls like input sanitization and class whitelisting with operational practices such as supply chain security audits and environment isolation, organizations can significantly reduce the attack surface associated with this vulnerability while maintaining efficient AI model development workflows.