CVE-2018-10055 in TensorFlow
Summary
by MITRE
Invalid memory access and/or a heap buffer overflow in the TensorFlow XLA compiler in Google TensorFlow before 1.7.1 could cause a crash or read from other parts of process memory via a crafted configuration file.
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Analysis
by VulDB Data Team • 06/01/2020
The vulnerability identified as CVE-2018-10055 represents a critical memory safety issue within the TensorFlow XLA compiler component that affects Google TensorFlow versions prior to 1.7.1. This flaw manifests as invalid memory access and heap buffer overflow conditions that can be exploited through carefully crafted configuration files. The XLA compiler serves as a domain-specific compiler for machine learning workloads and is designed to optimize TensorFlow operations by generating efficient machine code. When processing malformed input configurations, the compiler fails to properly validate memory boundaries, creating opportunities for unauthorized memory access patterns that could lead to system instability or information disclosure.
The technical implementation of this vulnerability stems from insufficient input validation mechanisms within the XLA compiler's configuration parsing logic. When the compiler encounters malformed or maliciously constructed configuration files, it fails to perform adequate bounds checking on heap-allocated buffers, leading to memory corruption scenarios. This type of flaw falls under the CWE-121 category of stack-based buffer overflow, though the specific implementation involves heap memory management issues that can result in arbitrary memory reads. The vulnerability is particularly concerning because it operates at the compiler level where configuration files are processed before execution, meaning that any attacker who can influence the configuration input can potentially trigger the memory corruption conditions.
The operational impact of CVE-2018-10055 extends beyond simple crash conditions to encompass potential information disclosure and system stability threats. A successful exploitation could cause the TensorFlow process to crash and terminate unexpectedly, disrupting machine learning workflows and potentially leading to data loss. More critically, the heap buffer overflow conditions may enable attackers to read from other parts of the process memory space, potentially exposing sensitive information such as model parameters, training data, or system credentials. This vulnerability particularly affects environments where TensorFlow processes are exposed to untrusted input configurations, including cloud-based machine learning platforms or collaborative development environments where multiple parties contribute configuration files.
Mitigation strategies for CVE-2018-10055 primarily focus on upgrading to TensorFlow version 1.7.1 or later, which includes patches addressing the memory validation issues within the XLA compiler. Organizations should implement comprehensive input validation controls for any configuration files processed by TensorFlow components, particularly those that may originate from untrusted sources. The ATT&CK framework categorizes this vulnerability under the T1059.001 technique for command and scripting interpreter, as exploitation often involves crafting malicious inputs that trigger the compiler's memory handling flaws. Additional defensive measures include implementing sandboxing mechanisms for configuration file processing, employing memory protection techniques such as stack canaries and address space layout randomization, and establishing strict access controls for systems running TensorFlow with XLA compilation capabilities. Security monitoring should focus on detecting unusual process termination patterns or memory access anomalies that may indicate exploitation attempts.