Vulnerability-Lookup

CNVD-2020-62802

Vulnerability from cnvd - Published: 2020-11-12

Title

Google TensorFlow缓冲区溢出漏洞（CNVD-2020-62802）

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，攻击者可利用该漏洞可以将一个无效的axis值传递给tf.quantization。数字转换和dequantize。这将导致在c++内核实现中访问一个超出输入张量秩的维度。但是dim size只执行DCHECK来验证参数，然后使用它来访问数组中相应的元素。因为在正常的构建中，DCHECK 类宏是无操作的，这会导致segfault和访问数组边界之外。

Severity

低

Patch Name

Google TensorFlow缓冲区溢出漏洞（CNVD-2020-62802）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-rrfp-j2mp-hq9c

Reference

https://nvd.nist.gov/vuln/detail/CVE-2020-15265

Impacted products

Name
Google Google TensorFlow <2.4.0

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JSON

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}

CVE-2020-15265 (GCVE-0-2020-15265)

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

Title

Segfault in Tensorflow

Summary

In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. 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

5.9 (Medium)


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

CWE

CWE-125 - Out-of-bounds Read

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/42105	x_refsource_MISC
https://github.com/tensorflow/tensorflow/commit/e…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.4.0

Show details on NVD website

JSON

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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

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