CVE-2020-28759 in Tengine
Summary
by MITRE • 12/27/2020
The serializer module in OAID Tengine lite-v1.0 has a Buffer Overflow and crash. NOTE: another person has stated "I don't think there is an proof of overflow so far.
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Analysis
by VulDB Data Team • 08/04/2024
The vulnerability identified as CVE-2020-28759 resides within the serializer module of OAID Tengine lite-v1.0, a lightweight deep learning inference framework designed for edge devices. This flaw manifests as a buffer overflow condition that can lead to system crashes and potentially more severe consequences. The serializer component is responsible for converting data structures into a format suitable for storage or transmission, making it a critical element in the framework's operation. When processing malformed input data, the module fails to properly validate buffer boundaries, creating an opportunity for attackers to exploit this weakness through carefully crafted inputs.
The technical nature of this vulnerability aligns with CWE-121, which describes buffer overflow conditions where data written to a buffer exceeds the allocated memory space. The flaw occurs during the serialization process when the system attempts to store data beyond the intended buffer limits. This type of vulnerability typically arises from insufficient input validation and boundary checking mechanisms within the code. The buffer overflow can cause memory corruption that results in program termination, system instability, or in more severe cases, arbitrary code execution if proper exploitation techniques are applied. The crash behavior indicates that the system lacks proper error handling and recovery mechanisms when encountering malformed serialized data.
From an operational perspective, this vulnerability presents significant risks to systems utilizing OAID Tengine lite-v1.0 for deep learning inference tasks, particularly in edge computing environments where these frameworks are commonly deployed. The impact extends beyond simple service disruption as the vulnerability could be exploited to cause denial of service attacks against inference systems. Attackers could potentially send maliciously formatted serialized data to trigger the buffer overflow, leading to system crashes that would compromise the availability of critical inference services. The vulnerability affects devices that rely on this lightweight framework for processing neural network models, including IoT devices, mobile applications, and embedded systems where resource constraints make proper input validation even more critical.
Mitigation strategies for CVE-2020-28759 should focus on implementing robust input validation and boundary checking mechanisms within the serializer module. Organizations should prioritize updating to patched versions of OAID Tengine lite-v1.0 if available, as this represents the most effective immediate solution. Additionally, implementing defensive programming practices such as using safe string handling functions, implementing proper buffer size checks, and employing memory safety techniques can help prevent similar vulnerabilities from occurring. The implementation of runtime protections such as stack canaries, address space layout randomization, and data execution prevention can provide additional layers of defense. Security monitoring should include detection of abnormal serialization patterns that might indicate exploitation attempts, while network segmentation and access controls can limit the potential impact of successful attacks. The vulnerability also highlights the importance of following secure coding practices and conducting regular security assessments of third-party components used in production systems, as outlined in the ATT&CK framework's software supply chain attack categories.