Vulnerability · Published Aug 12, 2021 · Assigned by GitHub_M

CVE-2021-37679Heap OOB in nested `tf.map_fn` with `RaggedTensor`s in TensorFlow

High 7.1

In plain English

A high-severity out-of-bounds read in tensorflow from tensorflow. There are no confirmed reports of exploitation so far. The chance it is exploited in the next 30 days is estimated at 0.2% (higher than 8% of all known vulnerabilities). Fixed versions are available - upgrade the affected package.

Severity (CVSS 3.1)7.1High
Exploit odds (30 days)0.2%top 92.2% of all CVEs
Exploited in the wildNo reportsnot in CISA KEV
Attack toolingNoneno public tooling known

Official description

TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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.

Affected packages & fixed versions

EcosystemPackageFixed inAdvisory
PyPItensorflow2.3.4, 2.4.3PYSEC-2021-301
PyPItensorflow2.3.4GHSA-g8wg-cjwc-xhhp
PyPItensorflow2.4.3GHSA-g8wg-cjwc-xhhp
PyPItensorflow2.5.1GHSA-g8wg-cjwc-xhhp
PyPItensorflow-cpu2.4.3GHSA-g8wg-cjwc-xhhp
PyPItensorflow-cpu2.3.4, 2.4.3PYSEC-2021-592
PyPItensorflow-cpu2.3.4GHSA-g8wg-cjwc-xhhp
PyPItensorflow-cpu2.5.1GHSA-g8wg-cjwc-xhhp
PyPItensorflow-gpu2.3.4, 2.4.3PYSEC-2021-790
PyPItensorflow-gpu2.3.4GHSA-g8wg-cjwc-xhhp
PyPItensorflow-gpu2.4.3GHSA-g8wg-cjwc-xhhp
PyPItensorflow-gpu2.5.1GHSA-g8wg-cjwc-xhhp

Affected products (as reported)

VendorProductVersions
tensorflow tensorflow >= 2.5.0, < 2.5.1>= 2.4.0, < 2.4.3< 2.3.4

Weakness: CWE-125 · Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N

References