pysec-2021-159
Vulnerability from pysec
TensorFlow is an end-to-end open source platform for machine learning. The tf.raw_ops.Conv3DBackprop*
operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow", "purl": "pkg:pypi/tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "311403edbc9816df80274bd1ea8b3c0c0f22c3fa" } ], "repo": "https://github.com/tensorflow/tensorflow", "type": "GIT" }, { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" }, { "introduced": "2.2.0" }, { "fixed": "2.2.3" }, { "introduced": "2.3.0" }, { "fixed": "2.3.3" }, { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ], "versions": [ "0.12.0", "0.12.0rc0", "0.12.0rc1", "0.12.1", "1.0.0", "1.0.1", "1.1.0", "1.1.0rc0", "1.1.0rc1", "1.1.0rc2", "1.10.0", "1.10.0rc0", "1.10.0rc1", "1.10.1", "1.11.0", "1.11.0rc0", "1.11.0rc1", "1.11.0rc2", "1.12.0", "1.12.0rc0", "1.12.0rc1", "1.12.0rc2", "1.12.2", "1.12.3", "1.13.0rc0", "1.13.0rc1", "1.13.0rc2", "1.13.1", "1.13.2", "1.14.0", "1.14.0rc0", "1.14.0rc1", "1.15.0", "1.15.0rc0", "1.15.0rc1", "1.15.0rc2", "1.15.0rc3", "1.15.2", "1.15.3", "1.15.4", "1.15.5", "1.2.0", "1.2.0rc0", "1.2.0rc1", "1.2.0rc2", "1.2.1", "1.3.0", "1.3.0rc0", "1.3.0rc1", "1.3.0rc2", "1.4.0", "1.4.0rc0", "1.4.0rc1", "1.4.1", "1.5.0", "1.5.0rc0", "1.5.0rc1", "1.5.1", "1.6.0", "1.6.0rc0", "1.6.0rc1", "1.7.0", "1.7.0rc0", "1.7.0rc1", "1.7.1", "1.8.0", "1.8.0rc0", "1.8.0rc1", "1.9.0", "1.9.0rc0", "1.9.0rc1", "1.9.0rc2", "2.0.0", "2.0.0a0", "2.0.0b0", "2.0.0b1", "2.0.0rc0", "2.0.0rc1", "2.0.0rc2", "2.0.1", "2.0.2", "2.0.3", "2.0.4", "2.1.0", "2.1.0rc0", "2.1.0rc1", "2.1.0rc2", "2.1.1", "2.1.2", "2.1.3", "2.2.0", "2.2.1", "2.2.2", "2.3.0", "2.3.1", "2.3.2", "2.4.0", "2.4.1" ] } ], "aliases": [ "CVE-2021-29522", "GHSA-c968-pq7h-7fxv" ], "details": "TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.", "id": "PYSEC-2021-159", "modified": "2021-08-27T03:22:25.206676Z", "published": "2021-05-14T20:15:00Z", "references": [ { "type": "ADVISORY", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv" }, { "type": "FIX", "url": "https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa" } ] }
Sightings
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Nomenclature
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- Confirmed: The vulnerability is confirmed from an analyst perspective.
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- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.