pysec-2020-129
Vulnerability from pysec
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's SavedModel
protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using tensorflow-serving
or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, 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": "adf095206f25471e864a8e63a0f1caef53a0e3a6" } ], "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-2020-15206", "GHSA-w5gh-2wr2-pm6g" ], "details": "In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow\u0027s `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.", "id": "PYSEC-2020-129", "modified": "2020-10-29T16:15:00Z", "published": "2020-09-25T19:15:00Z", "references": [ { "type": "ADVISORY", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w5gh-2wr2-pm6g" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1" }, { "type": "FIX", "url": "https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6" }, { "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.