CVE-2022-35987— `CHECK` fail in `DenseBincount` in TensorFlow
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I. Basic Information for CVE-2022-35987
Vulnerability Information
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Vulnerability Title
`CHECK` fail in `DenseBincount` in TensorFlow
Source: NVD (National Vulnerability Database)
Vulnerability Description
TensorFlow is an open source platform for machine learning. `DenseBincount` assumes its input tensor `weights` to either have the same shape as its input tensor `input` or to be length-0. A different `weights` shape will trigger a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bf4c14353c2328636a18bfad1e151052c81d5f43. 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: NVD (National Vulnerability Database)
CVSS Information
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Source: NVD (National Vulnerability Database)
Vulnerability Type
可达断言
Source: NVD (National Vulnerability Database)
Vulnerability Title
Google TensorFlow 安全漏洞
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 存在安全漏洞，该漏洞源于 DenseBincount 假设其输入张量 weights 要么具有与其输入张量 input 相同的形状，要么长度为 0。不同的 权重 形状将触发 检查 失败，可用于触发拒绝服务攻击。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-2022-35987
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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-2022-35974 | 5.9 MEDIUM | Segfault in `QuantizeDownAndShrinkRange` in TensorFlow |
| 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-2022-35973 | 5.9 MEDIUM | Segfault in `QuantizedMatMul` in TensorFlow |
| CVE-2022-35972 | 5.9 MEDIUM | Segfault in `QuantizedBiasAdd` in TensorFlow |
| CVE-2022-35967 | 5.9 MEDIUM | Segfault in `QuantizedAdd` in TensorFlow |
| CVE-2022-35979 | 5.9 MEDIUM | Segfault in `QuantizedRelu` and `QuantizedRelu6` |
| 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 |
Showing top 20 of 59 CVEs. View all on vendor page → →
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