Vulnerability-Lookup

CNVD-2020-04913

Vulnerability from cnvd - Published: 2020-02-13

Title

Google TensorFlow代码问题漏洞

Description

TensorFlow是一套用于机器学习的端到端开源平台。 Google TensorFlow中存在代码问题漏洞。该漏洞源于网络系统或产品未对输入的数据进行正确的验证。目前没有详细的漏洞细节提供。

Severity

高

Patch Name

Google TensorFlow代码问题漏洞的补丁

Patch Description

TensorFlow是一套用于机器学习的端到端开源平台。 Google TensorFlow中存在代码问题漏洞。该漏洞源于网络系统或产品未对输入的数据进行正确的验证。目前没有详细的漏洞细节提供。目前，供应商发布了安全公告及相关补丁信息，修复了此漏洞。

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-977j-xj7q-2jr9

Reference

https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf

Impacted products

Name
Google Google TensorFlow

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-5215",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-5215"
    }
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  "description": "TensorFlow\u662f\u4e00\u5957\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7aef\u5230\u7aef\u5f00\u6e90\u5e73\u53f0\u3002\n\nGoogle TensorFlow\u4e2d\u5b58\u5728\u4ee3\u7801\u95ee\u9898\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7f51\u7edc\u7cfb\u7edf\u6216\u4ea7\u54c1\u672a\u5bf9\u8f93\u5165\u7684\u6570\u636e\u8fdb\u884c\u6b63\u786e\u7684\u9a8c\u8bc1\u3002\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u6f0f\u6d1e\u7ec6\u8282\u63d0\u4f9b\u3002",
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  "number": "CNVD-2020-04913",
  "openTime": "2020-02-13",
  "patchDescription": "TensorFlow\u662f\u4e00\u5957\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7aef\u5230\u7aef\u5f00\u6e90\u5e73\u53f0\u3002\r\n\r\nGoogle TensorFlow\u4e2d\u5b58\u5728\u4ee3\u7801\u95ee\u9898\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7f51\u7edc\u7cfb\u7edf\u6216\u4ea7\u54c1\u672a\u5bf9\u8f93\u5165\u7684\u6570\u636e\u8fdb\u884c\u6b63\u786e\u7684\u9a8c\u8bc1\u3002\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u6f0f\u6d1e\u7ec6\u8282\u63d0\u4f9b\u3002\u76ee\u524d\uff0c\u4f9b\u5e94\u5546\u53d1\u5e03\u4e86\u5b89\u5168\u516c\u544a\u53ca\u76f8\u5173\u8865\u4e01\u4fe1\u606f\uff0c\u4fee\u590d\u4e86\u6b64\u6f0f\u6d1e\u3002",
  "patchName": "Google TensorFlow\u4ee3\u7801\u95ee\u9898\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Google Google TensorFlow"
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  "serverity": "\u9ad8",
  "submitTime": "2020-02-12",
  "title": "Google TensorFlow\u4ee3\u7801\u95ee\u9898\u6f0f\u6d1e"
}

CVE-2020-5215 (GCVE-0-2020-5215)

Vulnerability from cvelistv5 – Published: 2020-01-28 21:20 – Updated: 2024-08-04 08:22

Title

Segmentation faultin TensorFlow when converting a Python string to tf.float16

Summary

In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.

Severity

5 (Medium)


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

CWE

CWE-754 - Improper Check for Unusual or Exceptional Conditions

Assigner

GitHub_M mitre root

References

4 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/5…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
TensorFlow	TensorFlow	Affected: < 1.15.2 Affected: = 2.0.0

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

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.