CVE-2022-23592 in Tensorflow
Summary
by MITRE • 02/05/2022
Tensorflow is an Open Source Machine Learning Framework. TensorFlow's type inference can cause a heap out of bounds read as the bounds checking is done in a `DCHECK` (which is a no-op during production). An attacker can control the `input_idx` variable such that `ix` would be larger than the number of values in `node_t.args`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.
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Analysis
by VulDB Data Team • 02/07/2022
TensorFlow represents one of the most widely adopted open source machine learning frameworks, serving as the foundation for numerous artificial intelligence applications across enterprise and research environments. The vulnerability identified as CVE-2022-23592 resides within TensorFlow's type inference mechanism, specifically targeting the bounds checking implementation that occurs within debug conditional statements. This flaw demonstrates a critical design oversight where essential security validations are relegated to debugging macros that are effectively disabled in production environments, creating a dangerous gap between development and deployment security postures.
The technical flaw manifests through improper bounds validation in TensorFlow's internal type inference system, where the input_idx variable controls access to node_t.args array elements. When an attacker can manipulate this input parameter, the resulting index calculation produces an ix value that exceeds the legitimate array bounds, leading to a heap out of bounds read condition. This vulnerability type maps directly to CWE-129, which addresses improper bounds checking in software systems, and specifically aligns with CWE-787, which details out-of-bounds read conditions. The fundamental issue occurs because the bounds checking is implemented using DCHECK macros, which are designed for debugging purposes and are automatically disabled in release builds, leaving production systems vulnerable to memory access violations.
The operational impact of this vulnerability extends beyond simple memory corruption, as it provides attackers with a potential pathway for information disclosure and system instability. In production environments where TensorFlow processes untrusted input data, an attacker could craft malicious inputs that trigger the out of bounds read, potentially exposing sensitive memory contents or causing application crashes that could be exploited for further attacks. This vulnerability particularly affects systems processing machine learning models that rely on TensorFlow's automatic type inference, making it relevant to data science platforms, AI-powered applications, and automated machine learning pipelines that handle diverse input sources.
Organizations utilizing TensorFlow versions prior to 2.8.0 face significant security risks as this vulnerability remains unpatched in the affected release. The fix implemented in TensorFlow 2.8.0 addresses the core issue by ensuring proper bounds checking occurs regardless of debug build configurations, moving the validation outside of DCHECK macros to ensure production safety. Security practitioners should prioritize immediate patching of affected systems, as this vulnerability demonstrates the importance of maintaining robust security controls throughout all software development lifecycle phases. The ATT&CK framework categorizes this as a memory corruption technique, potentially enabling subsequent attack phases such as privilege escalation or information gathering. Additionally, this vulnerability highlights the broader security principle that defensive measures should not rely on debug-only code paths, as emphasized in the software security best practices of the OWASP Top Ten and NIST Cybersecurity Framework, which stress the importance of defense in depth and secure coding practices that maintain security controls across all operational environments.