CVE-2019-5247 in Atlas 300
Summary
by MITRE
Huawei Atlas 300, Atlas 500 have a buffer overflow vulnerability. A local, authenticated attacker may craft specific parameter and send to the process to exploit this vulnerability. Successfully exploit may cause service crash.
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Analysis
by VulDB Data Team • 03/05/2024
The vulnerability identified as CVE-2019-5247 affects Huawei Atlas 300 and Atlas 500 hardware platforms, which are designed for artificial intelligence computing and machine learning workloads. These devices operate as specialized hardware accelerators that process large-scale data analytics and deep learning tasks. The vulnerability manifests as a buffer overflow condition that exists within the system's processing mechanisms, specifically within the software components that manage parameter handling for computational tasks.
This buffer overflow vulnerability resides in the local authenticated attack surface, meaning that an attacker must first establish legitimate credentials or access to the system before attempting exploitation. The flaw occurs when specific parameter values are crafted and submitted to the affected process, which then fails to properly validate or sanitize input data before processing. The technical implementation of this vulnerability aligns with CWE-121, which describes stack-based buffer overflow conditions where insufficient bounds checking allows attackers to overwrite adjacent memory locations.
The operational impact of successfully exploiting this vulnerability can result in service disruption and system instability. When an attacker crafts malicious parameters and submits them to the vulnerable process, the buffer overflow can cause the targeted application or service to crash, potentially leading to denial of service conditions that affect AI computing capabilities. This disruption can be particularly damaging in production environments where these devices are critical for maintaining continuous machine learning operations and data processing workflows. The vulnerability represents a significant concern for organizations relying on these platforms for enterprise-level artificial intelligence workloads.
Mitigation strategies for this vulnerability should focus on implementing proper input validation and bounds checking mechanisms within the affected software components. System administrators should ensure that all parameter inputs are rigorously validated before processing, with appropriate buffer size checks and memory management practices. The implementation of address space layout randomization and stack canaries can provide additional protection layers against exploitation attempts. Organizations should also consider applying firmware updates provided by Huawei to address the specific buffer overflow conditions. From an operational security perspective, this vulnerability aligns with ATT&CK technique T1059, which involves the execution of malicious code through command and scripting interfaces, and may require monitoring for unusual parameter patterns that could indicate exploitation attempts. Regular security assessments and penetration testing of these AI computing platforms should be conducted to identify potential similar vulnerabilities in the system's software stack.