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- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean?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 before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. 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
- 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
- tensorflowPyPI
fixed in 2.3.1
- tensorflow-cpuPyPI
fixed in 2.3.1