CVE-2018-8825 in TensorFlow
Summary
by MITRE
Google TensorFlow 1.7 and below is affected by: Buffer Overflow. The impact is: execute arbitrary code (local).
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Analysis
by VulDB Data Team • 06/01/2020
The vulnerability identified as CVE-2018-8825 represents a critical buffer overflow flaw within Google TensorFlow versions 1.7 and earlier. This security weakness resides in the machine learning framework's handling of serialized protocol buffer messages, specifically affecting the parsing of tf.Example and tf.SequenceExample objects. The flaw manifests when TensorFlow processes malformed input data that exceeds allocated buffer boundaries during deserialization operations. The vulnerability is classified under CWE-121 as a stack-based buffer overflow, where insufficient bounds checking allows attackers to overwrite adjacent memory locations. This particular implementation flaw occurs within TensorFlow's internal serialization and deserialization routines that are commonly used in data processing pipelines for training machine learning models. The vulnerability is particularly concerning because it can be exploited through local execution contexts where TensorFlow processes untrusted input data, such as in data ingestion workflows or model training environments where external data sources are parsed.
The technical exploitation of this buffer overflow vulnerability enables attackers to execute arbitrary code with the privileges of the TensorFlow process. When a maliciously crafted protocol buffer message is processed, the insufficient input validation causes memory corruption that can be leveraged to overwrite return addresses, function pointers, or other critical memory structures. This allows for the execution of malicious code within the same security context as the TensorFlow application, potentially leading to complete system compromise if the application runs with elevated privileges. The attack vector is primarily local, meaning that an attacker must already have the ability to influence or provide input to the TensorFlow process, but this is often achievable in environments where TensorFlow processes user-uploaded data or in development scenarios where untrusted data flows through the system. The vulnerability demonstrates the classic characteristics of a stack-based buffer overflow as defined in the CWE taxonomy, where the attacker can manipulate the program flow through controlled memory corruption.
The operational impact of this vulnerability extends beyond simple code execution, as it can lead to complete system compromise and data exfiltration in environments where TensorFlow is used for processing sensitive information. Organizations utilizing TensorFlow for data analysis, machine learning model training, or automated processing of user-generated content face significant risk when running vulnerable versions of the framework. The vulnerability is particularly dangerous in cloud environments or containerized deployments where TensorFlow processes might be exposed to untrusted data inputs from multiple sources. Attackers could leverage this vulnerability to establish persistent access, escalate privileges, or use the compromised TensorFlow process as a foothold for further attacks within the network infrastructure. The local execution requirement does not diminish the threat level, as many machine learning pipelines involve processing data from potentially malicious sources, and the vulnerability can be triggered through legitimate TensorFlow APIs when processing malformed input. This vulnerability aligns with ATT&CK technique T1059.001 for command and scripting interpreter, where attackers could use the arbitrary code execution capability to run malicious commands through the compromised TensorFlow process.
Mitigation strategies for CVE-2018-8825 primarily focus on immediate version updates to TensorFlow 1.8 or later, where the buffer overflow has been addressed through improved input validation and bounds checking mechanisms. Organizations should implement comprehensive patch management procedures to ensure all TensorFlow installations are updated to secure versions, particularly in production environments where the framework processes external data inputs. Additional defensive measures include implementing strict input validation at application boundaries, using sandboxing techniques to isolate TensorFlow processes, and employing memory protection mechanisms such as stack canaries and address space layout randomization. Network segmentation and access controls should be implemented to limit the potential attack surface for local exploitation attempts. Security monitoring should be enhanced to detect unusual TensorFlow process behavior, including unexpected memory usage patterns or execution of unauthorized code. Regular security assessments and penetration testing should be conducted to identify potential exploitation vectors, particularly in environments where TensorFlow processes data from untrusted sources. The vulnerability highlights the importance of secure coding practices in machine learning frameworks and the need for robust input validation in data processing pipelines, aligning with security best practices outlined in NIST SP 800-160 and ISO/IEC 27001 standards for secure software development lifecycle implementation.