Vulnerability-Lookup

CNVD-2021-48869

Vulnerability from cnvd - Published: 2021-07-08

Title

Google TensorFlow缓冲区溢出漏洞（CNVD-2021-48869）

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。 Google TensorFlow 2.4.2，2.3.3 存在缓冲区溢出漏洞，该漏洞源于RaggedBincount的split参数未指定一个有效的SparseTensor ，攻击者可利用该漏洞导致堆缓冲区溢出。

Severity

中

Patch Name

Google TensorFlow缓冲区溢出漏洞（CNVD-2021-48869）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/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5

Reference

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4278-2v5v-65r4, https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5

Impacted products

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

Show details on source website

JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2021-29512",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2021-29512"
    }
  },
  "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\nGoogle TensorFlow 2.4.2\uff0c2.3.3 \u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8eRaggedBincount\u7684split\u53c2\u6570\u672a\u6307\u5b9a\u4e00\u4e2a\u6709\u6548\u7684SparseTensor \uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\u3002",
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  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2021-48869",
  "openTime": "2021-07-08",
  "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\nGoogle TensorFlow 2.4.2\uff0c2.3.3 \u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8eRaggedBincount\u7684split\u53c2\u6570\u672a\u6307\u5b9a\u4e00\u4e2a\u6709\u6548\u7684SparseTensor \uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\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",
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  "products": {
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      "Google TensorFlow 2.3.3"
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  "serverity": "\u4e2d",
  "submitTime": "2021-05-28",
  "title": "Google TensorFlow\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2021-48869\uff09"
}

CVE-2021-29512 (GCVE-0-2021-29512)

Vulnerability from cvelistv5 – Published: 2021-05-14 18:55 – Updated: 2024-08-03 22:11

Title

Heap buffer overflow in `RaggedBinCount`

Summary

TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.

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-120 - Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

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/e…	x_refsource_MISC

Impacted products

1 product

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

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.