CVE-2024-32048 in Distribution of OpenVINO Model Server Software
Summary
by MITRE • 11/13/2024
Improper input validation in the Intel(R) Distribution of OpenVINO(TM) Model Server software before version 2024.0 may allow an unauthenticated user to potentially enable denial of service via adjacent access.
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Analysis
by VulDB Data Team • 11/13/2024
The vulnerability identified as CVE-2024-32048 resides within the Intel(R) Distribution of OpenVINO(TM) Model Server software, a critical component for deploying and serving machine learning models in edge and cloud environments. This software serves as a bridge between machine learning frameworks and application services, enabling organizations to efficiently manage model inference workloads. The flaw manifests in the software's input validation mechanisms, which fail to properly sanitize or validate incoming data requests. Such deficiencies create a pathway for malicious actors to exploit the system's processing logic, potentially leading to system instability or complete service unavailability. The vulnerability affects versions prior to 2024.0, indicating that organizations running older iterations of the software remain at risk of exploitation. The security implications extend beyond simple service disruption, as this weakness could enable attackers to consume excessive system resources or trigger unexpected behavior patterns that compromise the overall integrity of the model serving infrastructure.
The technical nature of this vulnerability stems from inadequate input validation procedures that should normally filter and verify all external data inputs before processing. When an attacker submits malformed or unexpected input to the model server, the system fails to properly handle these edge cases, potentially causing the software to enter an unstable state or consume excessive computational resources. This type of flaw aligns with CWE-20, which describes "Improper Input Validation" as a fundamental weakness in software design that occurs when applications fail to validate or incorrectly validate input data. The vulnerability specifically leverages adjacent network access, meaning that an attacker must be physically present on the network segment where the model server operates, though this proximity requirement does not significantly reduce the threat level given the potential for service disruption. The lack of authentication requirements for exploitation indicates that this vulnerability could be readily exploited by any individual with network access to the target system, making it particularly dangerous in shared or accessible environments.
The operational impact of CVE-2024-32048 extends beyond immediate denial of service conditions, potentially affecting business continuity and operational efficiency for organizations relying on OpenVINO Model Server for critical inference workloads. When exploited successfully, this vulnerability can cause the model server to become unresponsive, leading to cascading failures in applications that depend on these inference services. The attack surface is particularly concerning for organizations using OpenVINO in production environments where model serving reliability is paramount. The vulnerability's characteristics suggest that it could be leveraged for resource exhaustion attacks, where an attacker systematically consumes CPU, memory, or network resources until the system becomes unavailable to legitimate users. This type of attack pattern is consistent with ATT&CK technique T1499.004, which covers "Endpoint Denial of Service" and represents a common attack vector for disrupting service availability. Organizations may experience significant downtime, degraded performance, and potential data loss if the model server becomes compromised, especially in scenarios where the software operates as a critical component in automated decision-making processes.
Mitigation strategies for CVE-2024-32048 should prioritize immediate software updates to version 2024.0 or later, which contain the necessary patches to address the input validation weaknesses. Organizations should also implement network segmentation and access controls to limit adjacent network access to model server instances, reducing the attack surface available to potential threat actors. Additional defensive measures include implementing monitoring systems to detect unusual resource consumption patterns or unexpected behavior in model server processes, which could indicate exploitation attempts. Network-level firewalls and intrusion detection systems should be configured to monitor for suspicious traffic patterns that might indicate attempts to exploit this vulnerability. Security teams should also conduct thorough vulnerability assessments to identify all instances of the affected software within their environments and prioritize remediation efforts based on risk exposure. The implementation of automated patch management systems can help ensure that all affected systems receive updates in a timely manner, preventing exploitation windows from remaining open. Organizations should also review their incident response procedures to ensure they can effectively respond to potential exploitation attempts, including establishing clear protocols for identifying, containing, and remediating affected systems. Regular security testing and code reviews should be implemented to prevent similar input validation issues from emerging in future software deployments, reinforcing the importance of robust input sanitization practices in software development lifecycle processes.