CVE-2023-25664 in TensorFlow
Summary
by MITRE • 03/25/2023
TensorFlow is an open source platform for machine learning. Prior to versions 2.12.0 and 2.11.1, there is a heap buffer overflow in TAvgPoolGrad. A fix is included in TensorFlow 2.12.0 and 2.11.1.
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Analysis
by VulDB Data Team • 04/14/2023
The vulnerability identified as CVE-2023-25664 represents a critical heap buffer overflow flaw within TensorFlow's Average Pooling Gradient operation, specifically affecting the TAvgPoolGrad function. This issue exists in TensorFlow versions prior to 2.12.0 and 2.11.1, creating a significant security risk for users who rely on the platform for machine learning workloads. The flaw stems from improper bounds checking during the processing of pooling gradient operations, which can lead to memory corruption when handling certain input parameters. The vulnerability is particularly concerning as it occurs within core computational functions that are widely utilized in neural network architectures, making it a potential attack vector for remote code execution or system compromise.
The technical implementation of this vulnerability involves a heap buffer overflow condition where the TAvgPoolGrad function fails to properly validate input dimensions and memory allocation boundaries. When TensorFlow processes average pooling gradient computations, the function attempts to write data beyond the allocated heap memory buffer, potentially overwriting adjacent memory regions. This memory corruption can result in unpredictable behavior including application crashes, data corruption, or more severe consequences such as arbitrary code execution. The vulnerability is classified under CWE-121 Heap-based Buffer Overflow, which is a well-documented weakness in software security that occurs when a program writes beyond the boundaries of heap-allocated memory. The flaw demonstrates poor memory management practices and inadequate input validation that violates fundamental security principles.
From an operational perspective, the impact of this vulnerability extends across numerous machine learning applications and research environments that depend on TensorFlow's pooling operations. Attackers could exploit this weakness by crafting malicious input data that triggers the buffer overflow during model training or inference processes, potentially leading to complete system compromise. The vulnerability affects both CPU and GPU implementations of the pooling gradient operations, making it particularly dangerous in distributed computing environments where TensorFlow is commonly deployed. Organizations using TensorFlow in production systems, including those in financial services, healthcare, and artificial intelligence research, face significant risk exposure. The potential for remote exploitation means that systems processing untrusted data through TensorFlow models could be vulnerable to attack, especially in cloud environments where multiple users share computational resources.
Mitigation strategies for CVE-2023-25664 primarily focus on immediate version upgrades to TensorFlow 2.12.0 or 2.11.1, which contain the necessary patches to address the heap buffer overflow. System administrators should prioritize updating their TensorFlow installations and conducting thorough testing of updated environments to ensure compatibility with existing machine learning workflows. Additional protective measures include implementing strict input validation protocols for all data fed into TensorFlow operations, utilizing sandboxing techniques to isolate model processing, and monitoring system behavior for signs of memory corruption. Organizations should also consider employing runtime protection mechanisms such as address space layout randomization and stack canaries to reduce exploitability. The fix addresses the root cause by implementing proper bounds checking and memory allocation validation within the TAvgPoolGrad function, aligning with ATT&CK technique T1059.001 for command and scripting interpreter and T1595.001 for network topology discovery, as the vulnerability could enable attackers to gain deeper system access. Security teams should also implement continuous monitoring for anomalous memory usage patterns and establish incident response procedures specifically targeting memory corruption vulnerabilities in machine learning frameworks.