Vulnerability-Lookup

CNVD-2020-54784

Vulnerability from cnvd - Published: 2020-09-30

Title

TensorFlow Lite输入验证错误漏洞

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 Lite 2.2.1之前版本，2.3.1版本中存在安全漏洞，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。攻击者可利用该漏洞通过一定手段导致分段错误，它也可以用于进一步破坏内存，并且可以与其他漏洞链接在一起以创建更高级的漏洞利用。

Severity

中

Patch Name

TensorFlow Lite输入验证错误漏洞的补丁

Patch Description

Formal description

厂商已发布了漏洞修复程序，请及时关注更新： https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1

Reference

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq

Impacted products

Name
['Google TensorFlow Lite <2.2.1', 'Google TensorFlow Lite 2.3.1']

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JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-15212",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-15212"
    }
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  "description": "Google TensorFlow\u662f\u7f8e\u56fd\u8c37\u6b4c\uff08Google\uff09\u516c\u53f8\u7684\u4e00\u5957\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7aef\u5230\u7aef\u5f00\u6e90\u5e73\u53f0\u3002\n\nTensorFlow Lite 2.2.1\u4e4b\u524d\u7248\u672c\uff0c2.3.1\u7248\u672c\u4e2d\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\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\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u901a\u8fc7\u4e00\u5b9a\u624b\u6bb5\u5bfc\u81f4\u5206\u6bb5\u9519\u8bef\uff0c\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8fdb\u4e00\u6b65\u7834\u574f\u5185\u5b58\uff0c\u5e76\u4e14\u53ef\u4ee5\u4e0e\u5176\u4ed6\u6f0f\u6d1e\u94fe\u63a5\u5728\u4e00\u8d77\u4ee5\u521b\u5efa\u66f4\u9ad8\u7ea7\u7684\u6f0f\u6d1e\u5229\u7528\u3002",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u4e86\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://github.com/tensorflow/tensorflow/releases/tag/v2.3.1",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2020-54784",
  "openTime": "2020-09-30",
  "patchDescription": "Google TensorFlow\u662f\u7f8e\u56fd\u8c37\u6b4c\uff08Google\uff09\u516c\u53f8\u7684\u4e00\u5957\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7aef\u5230\u7aef\u5f00\u6e90\u5e73\u53f0\u3002\r\n\r\nTensorFlow Lite 2.2.1\u4e4b\u524d\u7248\u672c\uff0c2.3.1\u7248\u672c\u4e2d\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\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\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u901a\u8fc7\u4e00\u5b9a\u624b\u6bb5\u5bfc\u81f4\u5206\u6bb5\u9519\u8bef\uff0c\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8fdb\u4e00\u6b65\u7834\u574f\u5185\u5b58\uff0c\u5e76\u4e14\u53ef\u4ee5\u4e0e\u5176\u4ed6\u6f0f\u6d1e\u94fe\u63a5\u5728\u4e00\u8d77\u4ee5\u521b\u5efa\u66f4\u9ad8\u7ea7\u7684\u6f0f\u6d1e\u5229\u7528\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": "TensorFlow Lite\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
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      "Google TensorFlow Lite \u003c2.2.1",
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  "serverity": "\u4e2d",
  "submitTime": "2020-09-28",
  "title": "TensorFlow Lite\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e"
}

CVE-2020-15212 (GCVE-0-2020-15212)

Vulnerability from cvelistv5 – Published: 2020-09-25 18:50 – Updated: 2024-08-04 13:08

Title

Out of bounds access in tensorflow-lite

Summary

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Severity

8.1 (High)


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

CWE

CWE-787 - {"CWE-787":"Out-of-bounds Write"}

Assigner

GitHub_M mitre root

References

3 references

URL	Tags
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/commit/2…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: >= 2.2.0, < 2.2.1 Affected: >= 2.3.0, < 2.3.1

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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.

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CNVD-2020-54784

CVE-2020-15212 (GCVE-0-2020-15212)

Tags

Sightings

Nomenclature

Related by attack behaviour