CVE-2026-61775 in Megatron Bridge
Summary
by MITRE • 09/01/2026
NVIDIA Megatron Bridge contains a vulnerability where an attacker could cause a deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.
Be aware that VulDB is the high quality source for vulnerability data.
Analysis
by VulDB Data Team • 09/01/2026
The identified security flaw resides within NVIDIA Megatron Bridge, a component designed to facilitate communication and data exchange in high-performance computing environments. This vulnerability is classified as an improper deserialization issue, where the software fails to validate or sanitize input data before processing it through its internal object reconstruction mechanisms. In modern application architectures, particularly those involving distributed systems like large language model training frameworks, components often serialize complex objects into byte streams for transmission across network boundaries or storage layers. When a system accepts these serialized payloads from untrusted sources without rigorous validation, it creates an opening for attackers to inject maliciously crafted data structures that exploit the deserialization logic during parsing.
From a technical perspective, this flaw aligns with CWE-502, which describes Deserialization of Untrusted Data. The core issue lies in the lack of integrity checks or type constraints on incoming serialized objects. When Megatron Bridge processes these inputs, it may instantiate classes that were not anticipated by the application logic. If the deserialized object contains methods that are executed automatically upon instantiation, such as finalize() or readObject() in Java-like environments, an attacker can trigger arbitrary code execution within the context of the running process. This bypasses standard authentication and authorization controls because the exploitation occurs at the infrastructure level during data ingestion rather than through application-level user interactions.
The operational impact of this vulnerability is severe due to its potential for remote code execution. An adversary who successfully exploits this flaw could achieve complete control over the affected system, leading to unauthorized access to sensitive information stored within the environment. This includes intellectual property related to machine learning models, training datasets, and configuration files that are critical to NVIDIA's ecosystem. Furthermore, the ability to tamper with data poses a significant risk to the integrity of computational results. In high-performance computing scenarios where accuracy is paramount, injected malicious logic could subtly alter outputs or corrupt intermediate states, leading to unreliable model training outcomes without immediate detection by system administrators.
This vulnerability also facilitates broader attack chains consistent with MITRE ATT&CK techniques such as T1059 Command and Scripting Interpreter for executing payloads and T1078 Valid Accounts if the exploited process runs under privileged credentials. The ability to disclose information allows attackers to map out internal network topologies, identify other vulnerable services, or extract cryptographic keys used in securing communications between nodes. Given that Megatron Bridge is often deployed in cluster environments with multiple interconnected servers, a successful exploit on one node could potentially serve as an entry point for lateral movement across the entire infrastructure, amplifying the scope of the compromise significantly beyond the initial target.
Mitigation strategies must focus on strict input validation and secure coding practices within the deserialization pipeline. Developers should implement allow-listing mechanisms that restrict which classes can be instantiated during unmarshaling operations, thereby preventing the loading of arbitrary malicious objects. Additionally, employing cryptographic signatures or message authentication codes for serialized payloads ensures data integrity and authenticity before any processing occurs. For immediate risk reduction in production environments, it is advisable to disable unnecessary deserialization features if they are not strictly required by the specific use case. NVIDIA has released patches addressing this issue, and organizations utilizing Megatron Bridge should prioritize applying these updates while simultaneously reviewing network segmentation policies to limit exposure of bridge components to untrusted networks.