CVE-2022-23585

Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling png::CommonInitDecode(..., &decode), the decode value contains allocated buffers which can only be freed by calling png::CommonFreeDecode(&decode). However, several error case in the function implementation invoke the OP_REQUIRES macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Weakness

The product does not sufficiently track and release allocated memory after it has been used, which slowly consumes remaining memory.

Affected Software

Name	Vendor	Start Version	End Version
Tensorflow	Google	*	2.5.2 (including)
Tensorflow	Google	2.6.0 (including)	2.6.2 (including)
Tensorflow	Google	2.7.0 (including)	2.7.0 (including)

Potential Mitigations

Choose a language or tool that provides automatic memory management, or makes manual memory management less error-prone.
For example, glibc in Linux provides protection against free of invalid pointers.
When using Xcode to target OS X or iOS, enable automatic reference counting (ARC) [REF-391].
To help correctly and consistently manage memory when programming in C++, consider using a smart pointer class such as std::auto_ptr (defined by ISO/IEC ISO/IEC 14882:2003), std::shared_ptr and std::unique_ptr (specified by an upcoming revision of the C++ standard, informally referred to as C++ 1x), or equivalent solutions such as Boost.

NVD	https://nvd.nist.gov/vuln/detail/CVE-2022-23585
CWE	https://cwe.mitre.org/data/definitions/401.html

Missing Release of Memory after Effective Lifetime

Weakness

Affected Software

Potential Mitigations

References