CVE-2024-0081 in NeMo
Summary
by MITRE • 04/05/2024
NVIDIA NeMo framework for Ubuntu contains a vulnerability in tools/asr_webapp where an attacker may cause an allocation of resources without limits or throttling. A successful exploit of this vulnerability may lead to a server-side denial of service.
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Analysis
by VulDB Data Team • 10/10/2025
The vulnerability identified as CVE-2024-0081 resides within the NVIDIA NeMo framework's asr_webapp tools component specifically designed for Ubuntu environments. This issue represents a critical resource exhaustion flaw that allows malicious actors to manipulate the application's memory allocation mechanisms without proper bounds or rate limiting controls. The NeMo framework serves as a comprehensive toolkit for developing and deploying speech recognition and natural language processing models, making it a valuable target for attackers seeking to disrupt services. The asr_webapp module specifically handles web-based applications for automatic speech recognition tasks, which are commonly deployed in enterprise environments for voice processing and transcription services.
The technical implementation of this vulnerability stems from inadequate resource management within the application's allocation routines. When processing incoming requests, the system fails to enforce proper limits on memory consumption or request processing rates, creating an environment where an attacker can continuously submit malformed or excessive requests to deplete available system resources. This flaw operates at the application level rather than at the operating system level, making it particularly challenging to detect through traditional network monitoring approaches. The lack of throttling mechanisms means that even a single malicious actor can effectively overwhelm the system's capacity to handle legitimate requests, leading to cascading failures in service availability.
The operational impact of this vulnerability extends beyond simple service disruption to potentially compromise the entire infrastructure supporting speech recognition services. Server-side denial of service attacks can result in significant business disruption for organizations relying on automated speech recognition systems for customer support, transcription services, or voice-controlled applications. The vulnerability affects systems where the NeMo framework is deployed in web application environments, particularly those handling high volumes of concurrent speech processing requests. Organizations using this framework for production workloads face substantial risk of service unavailability, which can translate into direct financial losses and damage to customer relationships. The attack vector is relatively simple to execute, requiring only the ability to send requests to the vulnerable web application endpoint without needing advanced exploitation techniques.
Mitigation strategies for CVE-2024-0081 should focus on implementing robust resource management controls within the application layer. System administrators should immediately deploy rate limiting mechanisms and memory allocation caps for the asr_webapp component to prevent unbounded resource consumption. The implementation should follow established security practices such as those outlined in the OWASP Top Ten and NIST cybersecurity frameworks. Organizations should also consider deploying network-level controls including firewalls and intrusion detection systems to monitor for unusual request patterns that may indicate exploitation attempts. Additionally, regular security updates and patches from NVIDIA should be applied promptly to address the root cause of the vulnerability. The ATT&CK framework categorizes this type of vulnerability under resource exhaustion techniques, specifically targeting the availability aspect of the CIA triad. Organizations should also implement comprehensive monitoring and alerting systems to detect anomalous resource consumption patterns that could indicate exploitation of this vulnerability. The CWE database classifies this issue as a weakness in resource management, specifically related to insufficient resource management controls and lack of proper allocation limits.