Heap out of bounds access in sparse reduction operations in TensorFlow
- Published
- Last modified
- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean? →
- published
- Whether CISA lists it in its Known Exploited Vulnerabilities catalog: proof that attackers are using it.What do these mean? →
- Not listed in CISA KEV
- Fix
- Fixed in 2.5.1, 2.4.3, 2.3.4
- Whether working attack code is public (Exploit-DB, Metasploit, Nuclei or GitHub). Public code makes attacks easier.What do these mean? →
- None on Exploit-DB, Metasploit or GitHub
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The implementation fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Timeline
- CVE published
- Record last updated
Affected versions
- tensorflow tensorflow
From 2.5.0 · fixed in 2.5.1; From 2.4.0 · fixed in 2.4.3; fixed in 2.3.4
- tensorflowPyPI
fixed in 2.3.4
- tensorflowPyPI
From 2.4.0 · fixed in 2.4.3
- tensorflowPyPI
From 2.5.0 · fixed in 2.5.1
- tensorflow-cpuPyPI
fixed in 2.3.4
- tensorflow-cpuPyPI
From 2.4.0 · fixed in 2.4.3
- tensorflow-cpuPyPI
From 2.5.0 · fixed in 2.5.1
- tensorflow-gpuPyPI
fixed in 2.3.4
- tensorflow-gpuPyPI
From 2.4.0 · fixed in 2.4.3
- tensorflow-gpuPyPI
From 2.5.0 · fixed in 2.5.1
- tensorflow-gpuPyPI
From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3
- tensorflowPyPI
From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3
- tensorflow-cpuPyPI
From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3