Vulnerability-Lookup

CNVD-2020-62809

Vulnerability from cnvd - Published: 2020-11-12

Title

Google TensorFlow缓冲区溢出漏洞（CNVD-2020-62809）

Description

Google TensorFlow是美国谷歌（Google）公司的一套用于机器学习的端到端开源平台。 Tensorflow 2.4.0之前版本存在安全漏洞，该漏洞源于当tf.image.crop_and_resize 的 boxes的参数有一个非常大的值，CPU内核实现接收它作为一个c++ nan 浮点值。试图对其进行操作是一种未定义的行为，稍后会产生分割错误。攻击者可以利用此漏洞执行恶意代码。

Severity

中

Patch Name

Google TensorFlow缓冲区溢出漏洞（CNVD-2020-62809）的补丁

Patch Description

Formal description

目前厂商已发布升级补丁以修复漏洞，补丁获取链接： https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc

Reference

https://nvd.nist.gov/vuln/detail/CVE-2020-15266

Impacted products

Name
Google TensorFlow <2.4.0

Show details on source website

JSON

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    "cve": {
      "cveNumber": "CVE-2020-15266",
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  "number": "CNVD-2020-62809",
  "openTime": "2020-11-12",
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  "products": {
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}

CVE-2020-15266 (GCVE-0-2020-15266)

Vulnerability from cvelistv5 – Published: 2020-10-21 20:30 – Updated: 2024-08-04 13:15

Title

Undefined behavior in Tensorflow

Summary

In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Severity

3.7 (Low)


                        
                          CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L

CWE

CWE-119 - Improper Restriction of Operations within the Bounds of a Memory Buffer

Assigner

GitHub_M mitre root

References

3 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/issues/42129	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/pull/421…	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.4.0

Show details on NVD website

JSON

To clipboard

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Sightings

Author	Source	Type	Date	Other

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.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.