Heap buffer overflow 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- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean?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 2.3.1
- 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
In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Timeline
- CVE published
- Record last updated
Affected versions
- tensorflow tensorflow
2.3.0
- tensorflowPyPI
fixed in 2.3.1
- tensorflow-gpuPyPI
fixed in 2.3.1
- tensorflowPyPI
From 2.3.0 · fixed in 2.3.1
- tensorflow-cpuPyPI
From 2.3.0 · fixed in 2.3.1
- tensorflow-gpuPyPI
From 2.3.0 · fixed in 2.3.1
- tensorflow-cpuPyPI
fixed in 2.3.1