GHSA-MXJJ-953W-2C2V
Vulnerability from github – Published: 2020-09-25 18:28 – Updated: 2024-10-30 21:17
VLAI?
Summary
Data corruption in tensorflow-lite
Details
Impact
When determining the common dimension size of two tensors, TFLite uses a DCHECK which is no-op outside of debug compilation modes:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442
Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors.
Patches
We have patched the issue in 8ee24e7949a20 and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Severity ?
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"cwe_ids": [
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"github_reviewed": true,
"github_reviewed_at": "2020-09-25T17:59:45Z",
"nvd_published_at": "2020-09-25T19:15:00Z",
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"details": "### Impact\nWhen determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442\n\nSince the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors.\n\n### Patches\nWe have patched the issue in 8ee24e7949a20 and will release patch releases for all versions between 1.15 and 2.3.\n\nWe recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n### Attribution\nThis vulnerability has been reported by members of the Aivul Team from Qihoo 360.",
"id": "GHSA-mxjj-953w-2c2v",
"modified": "2024-10-30T21:17:24Z",
"published": "2020-09-25T18:28:44Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2v"
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"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-15208"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d"
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{
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"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-288.yaml"
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"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-131.yaml"
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"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
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"url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
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"schema_version": "1.4.0",
"severity": [
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"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N",
"type": "CVSS_V3"
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"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Data corruption in tensorflow-lite"
}
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Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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