Common Weakness Enumeration
CWE-369
AllowedDivide By Zero
Abstraction: Base · Status: Draft
The product divides a value by zero.
594 vulnerabilities reference this CWE, most recent first.
GHSA-QQJQ-59G6-GV8W
Vulnerability from github – Published: 2022-05-17 00:47 – Updated: 2022-05-17 00:47
VLAI
Details
NVIDIA Windows GPU Display Driver contains a vulnerability in the kernel mode layer handler for DxgkDdiCreateAllocation where untrusted user input is used as a divisor without validation while processing block linear information which may lead to a potential divide by zero and denial of service.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2017-6271"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2017-09-22T17:29:00Z",
"severity": "MODERATE"
},
"details": "NVIDIA Windows GPU Display Driver contains a vulnerability in the kernel mode layer handler for DxgkDdiCreateAllocation where untrusted user input is used as a divisor without validation while processing block linear information which may lead to a potential divide by zero and denial of service.",
"id": "GHSA-qqjq-59g6-gv8w",
"modified": "2022-05-17T00:47:33Z",
"published": "2022-05-17T00:47:33Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2017-6271"
},
{
"type": "WEB",
"url": "http://nvidia.custhelp.com/app/answers/detail/a_id/4544"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/101001"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-QV87-XF2V-GGHW
Vulnerability from github – Published: 2024-11-28 00:39 – Updated: 2024-12-18 18:30
VLAI
Details
In VideoFrameScheduler.cpp of VideoFrameScheduler::PLL::fit, there is a possible remote denial of service due to divide by 0. This could lead to remote denial of service with no additional execution privileges needed. User interaction is needed for exploitation.
Severity
6.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2018-9354"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-11-27T23:15:04Z",
"severity": "MODERATE"
},
"details": "In VideoFrameScheduler.cpp of VideoFrameScheduler::PLL::fit, there is a\u00a0possible remote denial of service due to divide by 0. This could lead to\u00a0remote denial of service with no additional execution privileges needed.\u00a0User interaction is needed for exploitation.",
"id": "GHSA-qv87-xf2v-gghw",
"modified": "2024-12-18T18:30:50Z",
"published": "2024-11-28T00:39:26Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2018-9354"
},
{
"type": "WEB",
"url": "https://source.android.com/docs/security/bulletin/pixel/2018-06-01"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-QX67-F44J-4WQ4
Vulnerability from github – Published: 2022-05-17 00:33 – Updated: 2025-04-20 03:46
VLAI
Details
decode_line_info in dwarf2.c in the Binary File Descriptor (BFD) library (aka libbfd), as distributed in GNU Binutils 2.29, allows remote attackers to cause a denial of service (divide-by-zero error and application crash) via a crafted ELF file.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2017-15025"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2017-10-05T01:29:00Z",
"severity": "MODERATE"
},
"details": "decode_line_info in dwarf2.c in the Binary File Descriptor (BFD) library (aka libbfd), as distributed in GNU Binutils 2.29, allows remote attackers to cause a denial of service (divide-by-zero error and application crash) via a crafted ELF file.",
"id": "GHSA-qx67-f44j-4wq4",
"modified": "2025-04-20T03:46:20Z",
"published": "2022-05-17T00:33:50Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2017-15025"
},
{
"type": "WEB",
"url": "https://blogs.gentoo.org/ago/2017/10/03/binutils-divide-by-zero-in-decode_line_info-dwarf2-c"
},
{
"type": "WEB",
"url": "https://sourceware.org/bugzilla/show_bug.cgi?id=22186"
},
{
"type": "WEB",
"url": "https://sourceware.org/git/gitweb.cgi?p=binutils-gdb.git%3Bh=d8010d3e75ec7194a4703774090b27486b742d48"
},
{
"type": "WEB",
"url": "https://sourceware.org/git/gitweb.cgi?p=binutils-gdb.git;h=d8010d3e75ec7194a4703774090b27486b742d48"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-QXV7-458J-GMMX
Vulnerability from github – Published: 2022-05-14 02:05 – Updated: 2022-05-14 02:05
VLAI
Details
The _TIFFFax3fillruns function in libtiff before 4.0.6 allows remote attackers to cause a denial of service (divide-by-zero error and application crash) via a crafted Tiff image.
Severity
7.5 (High)
{
"affected": [],
"aliases": [
"CVE-2016-5323"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2017-01-20T15:59:00Z",
"severity": "HIGH"
},
"details": "The _TIFFFax3fillruns function in libtiff before 4.0.6 allows remote attackers to cause a denial of service (divide-by-zero error and application crash) via a crafted Tiff image.",
"id": "GHSA-qxv7-458j-gmmx",
"modified": "2022-05-14T02:05:04Z",
"published": "2022-05-14T02:05:04Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2016-5323"
},
{
"type": "WEB",
"url": "https://security.gentoo.org/glsa/201701-16"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2016-12/msg00017.html"
},
{
"type": "WEB",
"url": "http://www.debian.org/security/2017/dsa-3762"
},
{
"type": "WEB",
"url": "http://www.openwall.com/lists/oss-security/2016/06/15/6"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/91196"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-R35G-4525-29FQ
Vulnerability from github – Published: 2021-05-21 14:23 – Updated: 2024-10-31 20:53
VLAI
Summary
Division by 0 in `FusedBatchNorm`
Details
Impact
An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.FusedBatchNorm:
import tensorflow as tf
x = tf.constant([], shape=[1, 1, 1, 0], dtype=tf.float32)
scale = tf.constant([], shape=[0], dtype=tf.float32)
offset = tf.constant([], shape=[0], dtype=tf.float32)
mean = tf.constant([], shape=[0], dtype=tf.float32)
variance = tf.constant([], shape=[0], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean,
variance=variance, epsilon=epsilon,
exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)
This is because the implementation performs a division based on the last dimension of the x tensor:
const int depth = x.dimension(3);
const int rest_size = size / depth;
Since this is controlled by the user, an attacker can trigger a denial of service.
Patches
We have patched the issue in GitHub commit 1a2a87229d1d61e23a39373777c056161eb4084d.
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.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.1.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.2.0"
},
{
"fixed": "2.2.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.3.0"
},
{
"fixed": "2.3.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
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"introduced": "0"
},
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"fixed": "2.1.4"
}
],
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}
]
},
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"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.2.0"
},
{
"fixed": "2.2.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.3.0"
},
{
"fixed": "2.3.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
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{
"introduced": "0"
},
{
"fixed": "2.1.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
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"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.2.0"
},
{
"fixed": "2.2.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.3.0"
},
{
"fixed": "2.3.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.2"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2021-29555"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T20:54:44Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.FusedBatchNorm`:\n\n```python\nimport tensorflow as tf\n\nx = tf.constant([], shape=[1, 1, 1, 0], dtype=tf.float32)\nscale = tf.constant([], shape=[0], dtype=tf.float32)\noffset = tf.constant([], shape=[0], dtype=tf.float32)\nmean = tf.constant([], shape=[0], dtype=tf.float32)\nvariance = tf.constant([], shape=[0], dtype=tf.float32)\nepsilon = 0.0\nexponential_avg_factor = 0.0\ndata_format = \"NHWC\"\nis_training = False\n\ntf.raw_ops.FusedBatchNorm(\n x=x, scale=scale, offset=offset, mean=mean,\n variance=variance, epsilon=epsilon,\n exponential_avg_factor=exponential_avg_factor,\n data_format=data_format, is_training=is_training)\n``` \n \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor:\n\n```cc \nconst int depth = x.dimension(3);\nconst int rest_size = size / depth;\n```\n\nSince this is controlled by the user, an attacker can trigger a denial of service.\n\n### Patches\nWe have patched the issue in GitHub commit [1a2a87229d1d61e23a39373777c056161eb4084d](https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d).\n\nThe 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.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n### Attribution\nThis vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.",
"id": "GHSA-r35g-4525-29fq",
"modified": "2024-10-31T20:53:45Z",
"published": "2021-05-21T14:23:58Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r35g-4525-29fq"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29555"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-483.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-681.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-192.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Division by 0 in `FusedBatchNorm`"
}
GHSA-R46J-88X4-7J3F
Vulnerability from github – Published: 2022-05-24 17:31 – Updated: 2022-05-24 17:31
VLAI
Details
GoPro gpmf-parser 1.5 has a division-by-zero vulnerability in GPMF_Decompress(). Parsing malicious input can result in a crash.
{
"affected": [],
"aliases": [
"CVE-2020-16160"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2020-10-19T18:15:00Z",
"severity": "HIGH"
},
"details": "GoPro gpmf-parser 1.5 has a division-by-zero vulnerability in GPMF_Decompress(). Parsing malicious input can result in a crash.",
"id": "GHSA-r46j-88x4-7j3f",
"modified": "2022-05-24T17:31:13Z",
"published": "2022-05-24T17:31:13Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-16160"
},
{
"type": "WEB",
"url": "https://blog.inhq.net/posts/gopro-gpmf-parser-vuln-1"
},
{
"type": "WEB",
"url": "https://github.com/gopro/gpmf-parser/blob/2cc0af7ffee6f12934e2d57750bdf292f62b0a97/GPMF_parser.c#L1744"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-R4PJ-74MG-8868
Vulnerability from github – Published: 2021-05-21 14:21 – Updated: 2024-10-30 23:16
VLAI
Summary
Division by 0 in `Conv2DBackpropFilter`
Details
Impact
An attacker can trigger a division by 0 in tf.raw_ops.Conv2DBackpropFilter:
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 1, 0], dtype=tf.float32)
filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 0, 1, 1], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(input=input_tensor, filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 66, 18, 1], use_cudnn_on_gpu=True,
padding='SAME', explicit_paddings=[],
data_format='NHWC', dilations=[1, 1, 1, 1])
This is because the implementation does a modulus operation where the divisor is controlled by the caller:
if (dims->in_depth % filter_shape.dim_size(num_dims - 2)) { ... }
Patches
We have patched the issue in GitHub commit fca9874a9b42a2134f907d2fb46ab774a831404a.
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.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.1.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.2.0"
},
{
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}
],
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}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
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{
"introduced": "2.3.0"
},
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}
],
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}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.2"
}
],
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}
]
},
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"name": "tensorflow-cpu"
},
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"introduced": "0"
},
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}
]
},
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},
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{
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}
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},
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]
},
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{
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},
{
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"name": "tensorflow-gpu"
},
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{
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{
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]
},
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},
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},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.2"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2021-29524"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T23:19:06Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([], shape=[0, 0, 1, 0], dtype=tf.float32)\nfilter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)\nout_backprop = tf.constant([], shape=[0, 0, 1, 1], dtype=tf.float32)\n\ntf.raw_ops.Conv2DBackpropFilter(input=input_tensor, filter_sizes=filter_sizes,\n out_backprop=out_backprop,\n strides=[1, 66, 18, 1], use_cudnn_on_gpu=True,\n padding=\u0027SAME\u0027, explicit_paddings=[],\n data_format=\u0027NHWC\u0027, dilations=[1, 1, 1, 1])\n``` \n \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller:\n\n```cc \n if (dims-\u003ein_depth % filter_shape.dim_size(num_dims - 2)) { ... }\n```\n \n### Patches\nWe have patched the issue in GitHub commit [fca9874a9b42a2134f907d2fb46ab774a831404a](https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a).\n\nThe 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.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n \n### Attribution\nThis vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.",
"id": "GHSA-r4pj-74mg-8868",
"modified": "2024-10-30T23:16:52Z",
"published": "2021-05-21T14:21:47Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4pj-74mg-8868"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29524"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-452.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-650.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-161.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Division by 0 in `Conv2DBackpropFilter`"
}
GHSA-R534-VWMW-5GMQ
Vulnerability from github – Published: 2025-09-05 18:31 – Updated: 2025-11-26 18:31
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
net: hibmcge: fix the division by zero issue
When the network port is down, the queue is released, and ring->len is 0. In debugfs, hbg_get_queue_used_num() will be called, which may lead to a division by zero issue.
This patch adds a check, if ring->len is 0, hbg_get_queue_used_num() directly returns 0.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2025-38719"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-09-04T16:15:41Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nnet: hibmcge: fix the division by zero issue\n\nWhen the network port is down, the queue is released, and ring-\u003elen is 0.\nIn debugfs, hbg_get_queue_used_num() will be called,\nwhich may lead to a division by zero issue.\n\nThis patch adds a check, if ring-\u003elen is 0,\nhbg_get_queue_used_num() directly returns 0.",
"id": "GHSA-r534-vwmw-5gmq",
"modified": "2025-11-26T18:31:01Z",
"published": "2025-09-05T18:31:17Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-38719"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/475e06113c615dafd44262d6d6bd37786f7f4206"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/7004b26f0b64331143eb0b312e77a357a11427ce"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/c945e1ad4f3b77166a3215dabc0c6c980d4a9c73"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-R85G-7QGQ-6WRQ
Vulnerability from github – Published: 2022-05-24 19:20 – Updated: 2022-05-24 19:20
VLAI
Details
A Divide by Zero vulnerability in the function static int read_samples of Speex v1.2 allows attackers to cause a denial of service (DoS) via a crafted WAV file.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2020-23903"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-11-10T22:15:00Z",
"severity": "MODERATE"
},
"details": "A Divide by Zero vulnerability in the function static int read_samples of Speex v1.2 allows attackers to cause a denial of service (DoS) via a crafted WAV file.",
"id": "GHSA-r85g-7qgq-6wrq",
"modified": "2022-05-24T19:20:12Z",
"published": "2022-05-24T19:20:12Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-23903"
},
{
"type": "WEB",
"url": "https://github.com/xiph/speex/issues/13"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LXCRAYNW5ESCE2PIGTUXZNZHNYFLJ6PX"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/R3SEV2ZRR47GSD3M7O5PH4XEJMKJJNG2"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-R8HX-F24G-25MV
Vulnerability from github – Published: 2024-05-01 06:31 – Updated: 2024-07-03 18:37
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
crypto: iaa - Fix nr_cpus < nr_iaa case
If nr_cpus < nr_iaa, the calculated cpus_per_iaa will be 0, which causes a divide-by-0 in rebalance_wq_table().
Make sure cpus_per_iaa is 1 in that case, and also in the nr_iaa == 0 case, even though cpus_per_iaa is never used if nr_iaa == 0, for paranoia.
Severity
8.4 (High)
{
"affected": [],
"aliases": [
"CVE-2024-26945"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-05-01T06:15:10Z",
"severity": "HIGH"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\ncrypto: iaa - Fix nr_cpus \u003c nr_iaa case\n\nIf nr_cpus \u003c nr_iaa, the calculated cpus_per_iaa will be 0, which\ncauses a divide-by-0 in rebalance_wq_table().\n\nMake sure cpus_per_iaa is 1 in that case, and also in the nr_iaa == 0\ncase, even though cpus_per_iaa is never used if nr_iaa == 0, for\nparanoia.",
"id": "GHSA-r8hx-f24g-25mv",
"modified": "2024-07-03T18:37:58Z",
"published": "2024-05-01T06:31:41Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-26945"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/5a7e89d3315d1be86aff8a8bf849023cda6547f7"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/a5ca1be7f9817de4e93085778b3ee2219bdc2664"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
No mitigation information available for this CWE.
No CAPEC attack patterns related to this CWE.