CVE-2022-35973— Segfault in `QuantizedMatMul` in TensorFlow
Quick assessment
Affected
tensorflow tensorflow
Exploitation
No confirmed in-the-wild exploitation; assess based on exposure
Recommended action
Check the vendor advisory and references for a fixed version. If immediate upgrade is impossible, restrict exposure and increase monitoring.
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 存在输入验证错误漏洞，该漏洞源于如果为 QuantizedMatMul 提供以下非标量输入： min_a 、 max_a 、 min_b 或 max_b ，它会给出一个可用于触发拒绝服务攻击的段错误。该漏洞将在 2.10.0 版本， 2.9.1 版本， 2.8.1 版本， 2.7.2 版本中得到修复。
Possible ATT&CK Techniques 1 AI
T1499 · Endpoint Denial of Service
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I. Basic Information for CVE-2022-35973
Vulnerability Information
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Vulnerability Title
Segfault in `QuantizedMatMul` in TensorFlow
Source: CVE Program / CVE List V5
Vulnerability Description
TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Source: CVE Program / CVE List V5
CVSS Information
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Source: CVE Program / CVE List V5
Vulnerability Type
输入验证不恰当
Source: CVE Program / CVE List V5
Vulnerability Title
Google TensorFlow 输入验证错误漏洞
Source: CNNVD (China National Vulnerability Database)
Vulnerability 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 存在输入验证错误漏洞，该漏洞源于如果为 QuantizedMatMul 提供以下非标量输入： min_a 、 max_a 、 min_b 或 max_b ，它会给出一个可用于触发拒绝服务攻击的段错误。该漏洞将在 2.10.0 版本， 2.9.1 版本， 2.8.1 版本， 2.7.2 版本中得到修复。
Source: CNNVD (China National Vulnerability Database)
CVSS Information
N/A
Source: CNNVD (China National Vulnerability Database)
Vulnerability Type
N/A
Source: CNNVD (China National Vulnerability Database)
Affected Products
| Vendor | Product | Affected Versions | CPE | Subscribe |
|---|---|---|---|---|
| tensorflow | tensorflow | < 2.7.2 | - |
|
II. Public POCs for CVE-2022-35973
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Patches & Fixes for CVE-2022-35973 (1)
Same Patch Batch · tensorflow · 2022-09-16 · 59 CVEs total
| CVE-2022-35938 | 7.0 HIGH | OOB read in `Gather_nd` op in TensorFlow Lite Micro |
| CVE-2022-35939 | 7.0 HIGH | Out of bounds write in `scatter_nd` op in TensorFlow Lite |
| CVE-2022-35937 | 7.0 HIGH | OOB read in `Gather_nd` op in TensorFlow Lite |
| CVE-2022-35979 | 5.9 MEDIUM | Segfault in `QuantizedRelu` and `QuantizedRelu6` |
| CVE-2022-35959 | 5.9 MEDIUM | `CHECK` failures in `AvgPool3DGrad` in TensorFlow |
| CVE-2022-35941 | 5.9 MEDIUM | `CHECK` failure in `AvgPoolOp` in Tensorflow |
| CVE-2022-35940 | 5.9 MEDIUM | Int overflow in `RaggedRangeOp` in Tensoflow |
| CVE-2022-35952 | 5.9 MEDIUM | `CHECK` failures in `UnbatchGradOp` in TensorFlow |
| CVE-2022-35970 | 5.9 MEDIUM | Segfault in `QuantizedInstanceNorm` in TensorFlow |
| CVE-2022-35969 | 5.9 MEDIUM | `CHECK` fail in `Conv2DBackpropInput` in TensorFlow |
| CVE-2022-35971 | 5.9 MEDIUM | `CHECK` fail in `FakeQuantWithMinMaxVars` in TensorFlow |
| CVE-2022-35972 | 5.9 MEDIUM | Segfault in `QuantizedBiasAdd` in TensorFlow |
| CVE-2022-35974 | 5.9 MEDIUM | Segfault in `QuantizeDownAndShrinkRange` in TensorFlow |
| CVE-2022-35967 | 5.9 MEDIUM | Segfault in `QuantizedAdd` in TensorFlow |
| CVE-2022-35981 | 5.9 MEDIUM | `CHECK` fail in `FractionalMaxPoolGrad` in TensorFlow |
| CVE-2022-35982 | 5.9 MEDIUM | Segfault in `SparseBincount` in TensorFlow |
| CVE-2022-35988 | 5.9 MEDIUM | `CHECK` fail in `tf.linalg.matrix_rank` in TensorFlow |
| CVE-2022-35989 | 5.9 MEDIUM | `CHECK` fail in `MaxPool` in TensorFlow |
| CVE-2022-35983 | 5.9 MEDIUM | `CHECK` fail in `Save` and `SaveSlices` in TensorFlow |
| CVE-2022-35984 | 5.9 MEDIUM | `CHECK` fail in `ParameterizedTruncatedNormal` in TensorFlow |
Showing top 20 of 59 CVEs. View all on vendor page → →
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