CVE-2024-36284 in Neural Compressor Software
Summary
by MITRE • 11/13/2024
Improper input validation in some Intel(R) Neural Compressor software before version v3.0 may allow an authenticated user to potentially enable escalation of privilege via adjacent access.
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Analysis
by VulDB Data Team • 11/13/2024
The vulnerability identified as CVE-2024-36284 resides within Intel's Neural Compressor software, a tool designed for optimizing machine learning models for deployment across various hardware platforms. This issue represents a critical security flaw that affects versions prior to v3.0, specifically targeting the software's input validation mechanisms. The vulnerability stems from inadequate sanitization of user-provided data during the optimization process, creating a pathway for malicious actors to manipulate the system's behavior. The affected software serves as a bridge between machine learning frameworks and optimized inference engines, making it a prime target for attackers seeking to compromise the underlying computational infrastructure.
The technical exploitation of this vulnerability occurs through improper input validation that fails to adequately check or sanitize data provided by authenticated users. When users interact with the Neural Compressor software, particularly through its command-line interfaces or configuration file processing capabilities, the system does not sufficiently validate the integrity and legitimacy of incoming data. This weakness allows an authenticated user with adjacent access to craft malicious inputs that can manipulate the software's internal operations. The flaw operates at the boundary between user input and system processing, where insufficient validation enables attackers to inject crafted parameters that can alter the software's execution flow or modify critical system components. According to CWE classification, this vulnerability aligns with CWE-20, which describes "Improper Input Validation" as a fundamental weakness that occurs when software does not properly validate inputs received from external sources.
The operational impact of CVE-2024-36284 extends beyond simple privilege escalation, as it fundamentally compromises the integrity of the machine learning optimization pipeline. An attacker who successfully exploits this vulnerability can potentially gain elevated privileges within the system, allowing them to execute arbitrary code with higher privileges than initially granted. This escalation of privilege capability becomes particularly dangerous in environments where the Neural Compressor software is used for production model optimization, as it could enable attackers to modify optimization parameters, inject malicious code into optimized models, or gain unauthorized access to sensitive data. The adjacent access requirement means that attackers must be physically present or have network access to the system, but once inside, they can leverage this vulnerability to significantly compromise system integrity. The vulnerability's impact is further amplified when considering that Neural Compressor is often used in enterprise environments where model optimization is a critical component of machine learning workflows, making the attack surface particularly valuable for adversaries seeking persistent access to computational resources.
Mitigation strategies for CVE-2024-36284 primarily focus on immediate software updates and access control measures. Organizations should prioritize upgrading to Intel Neural Compressor version v3.0 or later, which contains the necessary patches to address the input validation deficiencies. Additionally, implementing strict access controls and network segmentation can help limit the attack surface by restricting adjacent access to systems running the vulnerable software. Security teams should also consider monitoring for unusual input patterns or configuration changes that might indicate exploitation attempts. The vulnerability's classification under ATT&CK technique T1068, "Exploitation for Privilege Escalation," emphasizes the need for comprehensive monitoring of privilege-related activities. Network administrators should implement least privilege principles for users who require access to Neural Compressor functionality, ensuring that only authorized personnel can interact with the software. Regular security assessments and vulnerability scanning should be conducted to identify any other potentially affected systems within the organization's infrastructure, as the issue may persist in other components of Intel's machine learning optimization toolchain.