pysec-2020-125
Vulnerability from pysec
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard
API in TensorFlow expects the last argument to be a function taking two int64
(i.e., long long
) arguments. However, there are several places in TensorFlow where a lambda taking int
or int32
arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow", "purl": "pkg:pypi/tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "27b417360cbd671ef55915e4bb6bb06af8b8a832" }, { "fixed": "ca8c013b5e97b1373b3bb1c97ea655e69f31a575" } ], "repo": "https://github.com/tensorflow/tensorflow", "type": "GIT" }, { "events": [ { "introduced": "0" }, { "fixed": "1.15.4" }, { "introduced": "2.0.0" }, { "fixed": "2.0.3" }, { "introduced": "2.1.0" }, { "fixed": "2.1.2" }, { "introduced": "2.2.0" }, { "fixed": "2.2.1" }, { "introduced": "2.3.0" }, { "fixed": "2.3.1" } ], "type": "ECOSYSTEM" } ], "versions": [ "0.12.0rc0", "0.12.0rc1", "0.12.0", "0.12.1", "1.0.0", "1.0.1", "1.1.0rc0", "1.1.0rc1", "1.1.0rc2", "1.1.0", "1.2.0rc0", "1.2.0rc1", "1.2.0rc2", "1.2.0", "1.2.1", "1.3.0rc0", "1.3.0rc1", "1.3.0rc2", "1.3.0", "1.4.0rc0", "1.4.0rc1", "1.4.0", "1.4.1", "1.5.0rc0", "1.5.0rc1", "1.5.0", "1.5.1", "1.6.0rc0", "1.6.0rc1", "1.6.0", "1.7.0rc0", "1.7.0rc1", "1.7.0", "1.7.1", "1.8.0rc0", "1.8.0rc1", "1.8.0", "1.9.0rc0", "1.9.0rc1", "1.9.0rc2", "1.9.0", "1.10.0rc0", "1.10.0rc1", "1.10.0", "1.10.1", "1.11.0rc0", "1.11.0rc1", "1.11.0rc2", "1.11.0", "1.12.0rc0", "1.12.0rc1", "1.12.0rc2", "1.12.0", "1.12.2", "1.12.3", "1.13.0rc0", "1.13.0rc1", "1.13.0rc2", "1.13.1", "1.13.2", "1.14.0rc0", "1.14.0rc1", "1.14.0", "1.15.0rc0", "1.15.0rc1", "1.15.0rc2", "1.15.0rc3", "1.15.0", "1.15.2", "1.15.3", "2.0.0", "2.0.1", "2.0.2", "2.1.0", "2.1.1", "2.2.0", "2.3.0" ] } ], "aliases": [ "CVE-2020-15202", "GHSA-h6fg-mjxg-hqq4" ], "details": "In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.", "id": "PYSEC-2020-125", "modified": "2020-10-29T16:15:00Z", "published": "2020-09-25T19:15:00Z", "references": [ { "type": "FIX", "url": "https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832" }, { "type": "ADVISORY", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1" }, { "type": "FIX", "url": "https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575" }, { "type": "WEB", "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html" } ] }
Sightings
Author | Source | Type | Date |
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Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- 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.