Vulnerability-Lookup

CNVD-2021-37640

Vulnerability from cnvd - Published: 2021-05-28

Title

Google TensorFlow SparseDenseCwiseMul堆越界访问漏洞

Description

Google TensorFlow是一个端到端开源机器学习平台。 Google TensorFlow SparseDenseCwiseMul存在堆越界访问漏洞。攻击者可利用漏洞通过向“tf.raw\u ops.backpropinput”传递无效参数，在堆分配数组的边界之外进行写操作。

Severity

中

Patch Name

Google TensorFlow SparseDenseCwiseMul堆越界访问漏洞的补丁

Patch Description

Google TensorFlow是一个端到端开源机器学习平台。 Google TensorFlow SparseDenseCwiseMul存在堆越界访问漏洞。攻击者可利用漏洞通过向“tf.raw\u ops.backpropinput”传递无效参数，在堆分配数组的边界之外进行写操作。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

厂商已发布了漏洞修复程序，请及时关注更新： https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pvrc-hg3f-58r6

Reference

https://nvd.nist.gov/vuln/detail/CVE-2021-29566

Impacted products

Name
['Google TensorFlow 2.4.2', 'Google TensorFlow 2.3.3', 'Google TensorFlow 2.2.3', 'Google TensorFlow 2.1.4']

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JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2021-29566",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2021-29566"
    }
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  "description": "Google TensorFlow\u662f\u4e00\u4e2a\u7aef\u5230\u7aef\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u5e73\u53f0\u3002\n\nGoogle TensorFlow SparseDenseCwiseMul\u5b58\u5728\u5806\u8d8a\u754c\u8bbf\u95ee\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u6f0f\u6d1e\u901a\u8fc7\u5411\u201ctf.raw\\u ops.backpropinput\u201d\u4f20\u9012\u65e0\u6548\u53c2\u6570\uff0c\u5728\u5806\u5206\u914d\u6570\u7ec4\u7684\u8fb9\u754c\u4e4b\u5916\u8fdb\u884c\u5199\u64cd\u4f5c\u3002",
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  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2021-37640",
  "openTime": "2021-05-28",
  "patchDescription": "Google TensorFlow\u662f\u4e00\u4e2a\u7aef\u5230\u7aef\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u5e73\u53f0\u3002\r\n\r\nGoogle TensorFlow SparseDenseCwiseMul\u5b58\u5728\u5806\u8d8a\u754c\u8bbf\u95ee\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u6f0f\u6d1e\u901a\u8fc7\u5411\u201ctf.raw\\u ops.backpropinput\u201d\u4f20\u9012\u65e0\u6548\u53c2\u6570\uff0c\u5728\u5806\u5206\u914d\u6570\u7ec4\u7684\u8fb9\u754c\u4e4b\u5916\u8fdb\u884c\u5199\u64cd\u4f5c\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 SparseDenseCwiseMul\u5806\u8d8a\u754c\u8bbf\u95ee\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
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      "Google TensorFlow 2.3.3",
      "Google TensorFlow 2.2.3",
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    ]
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  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2021-29566",
  "serverity": "\u4e2d",
  "submitTime": "2021-05-18",
  "title": "Google TensorFlow SparseDenseCwiseMul\u5806\u8d8a\u754c\u8bbf\u95ee\u6f0f\u6d1e"
}

CVE-2021-29566 (GCVE-0-2021-29566)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:16 – Updated: 2024-08-03 22:11

Title

Heap OOB access in `Dilation2DBackpropInput`

Summary

TensorFlow is an end-to-end open source platform for machine learning. An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Severity

2.5 (Low)


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

CWE

CWE-787 - Out-of-bounds Write

Assigner

GitHub_M mitre root

References

2 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/3…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.1.4 Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2

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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.

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.