Common Weakness Enumeration
CWE-1284
AllowedImproper Validation of Specified Quantity in Input
Abstraction: Base · Status: Incomplete
The product receives input that is expected to specify a quantity (such as size or length), but it does not validate or incorrectly validates that the quantity has the required properties.
500 vulnerabilities reference this CWE, most recent first.
GHSA-59P6-MHGR-2MP7
Vulnerability from github – Published: 2026-07-03 03:34 – Updated: 2026-07-17 09:31
VLAI
Details
** UNSUPPORTED WHEN ASSIGNED ** Improper Validation of Specified Quantity in Input in the ASUS AI Suite 3 driver allows a local user to access unintended memory regions via crafted IOCTL requests, leading to privilege escalation.
Severity
{
"affected": [],
"aliases": [
"CVE-2022-4989"
],
"database_specific": {
"cwe_ids": [
"CWE-1284"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-07-03T03:16:22Z",
"severity": "HIGH"
},
"details": "** UNSUPPORTED WHEN ASSIGNED ** Improper Validation of Specified Quantity in Input in the ASUS\u00a0AI Suite 3 driver\u00a0allows a local user to access unintended memory regions via crafted IOCTL requests, leading to privilege escalation.",
"id": "GHSA-59p6-mhgr-2mp7",
"modified": "2026-07-17T09:31:20Z",
"published": "2026-07-03T03:34:13Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-4989"
},
{
"type": "WEB",
"url": "https://www.asus.com/security-advisory"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
GHSA-5H94-MXHC-FGXW
Vulnerability from github – Published: 2024-06-21 12:31 – Updated: 2026-05-12 12:31
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
enic: Validate length of nl attributes in enic_set_vf_port
enic_set_vf_port assumes that the nl attribute IFLA_PORT_PROFILE is of length PORT_PROFILE_MAX and that the nl attributes IFLA_PORT_INSTANCE_UUID, IFLA_PORT_HOST_UUID are of length PORT_UUID_MAX. These attributes are validated (in the function do_setlink in rtnetlink.c) using the nla_policy ifla_port_policy. The policy defines IFLA_PORT_PROFILE as NLA_STRING, IFLA_PORT_INSTANCE_UUID as NLA_BINARY and IFLA_PORT_HOST_UUID as NLA_STRING. That means that the length validation using the policy is for the max size of the attributes and not on exact size so the length of these attributes might be less than the sizes that enic_set_vf_port expects. This might cause an out of bands read access in the memcpys of the data of these attributes in enic_set_vf_port.
Severity
7.1 (High)
{
"affected": [],
"aliases": [
"CVE-2024-38659"
],
"database_specific": {
"cwe_ids": [
"CWE-1284"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-06-21T11:15:12Z",
"severity": "HIGH"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nenic: Validate length of nl attributes in enic_set_vf_port\n\nenic_set_vf_port assumes that the nl attribute IFLA_PORT_PROFILE\nis of length PORT_PROFILE_MAX and that the nl attributes\nIFLA_PORT_INSTANCE_UUID, IFLA_PORT_HOST_UUID are of length PORT_UUID_MAX.\nThese attributes are validated (in the function do_setlink in rtnetlink.c)\nusing the nla_policy ifla_port_policy. The policy defines IFLA_PORT_PROFILE\nas NLA_STRING, IFLA_PORT_INSTANCE_UUID as NLA_BINARY and\nIFLA_PORT_HOST_UUID as NLA_STRING. That means that the length validation\nusing the policy is for the max size of the attributes and not on exact\nsize so the length of these attributes might be less than the sizes that\nenic_set_vf_port expects. This might cause an out of bands\nread access in the memcpys of the data of these\nattributes in enic_set_vf_port.",
"id": "GHSA-5h94-mxhc-fgxw",
"modified": "2026-05-12T12:31:57Z",
"published": "2024-06-21T12:31:21Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-38659"
},
{
"type": "WEB",
"url": "https://cert-portal.siemens.com/productcert/html/ssa-265688.html"
},
{
"type": "WEB",
"url": "https://cert-portal.siemens.com/productcert/html/ssa-613116.html"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/25571a12fbc8a1283bd8380d461267956fd426f7"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/2b649d7e0cb42a660f0260ef25fd55fdc9c6c600"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/3c0d36972edbe56fcf98899622d9b90ac9965227"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/7077c22f84f41974a711604a42fd0e0684232ee5"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/aee1955a1509a921c05c70dad5d6fc8563dfcb31"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/ca63fb7af9d3e531aa25f7ae187bfc6c7166ec2d"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/e8021b94b0412c37bcc79027c2e382086b6ce449"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/f6638e955ca00c489894789492776842e102af9c"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2024/06/msg00020.html"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-5JJ8-Q8MR-R2VW
Vulnerability from github – Published: 2022-12-12 09:30 – Updated: 2022-12-14 18:30
VLAI
Details
A vulnerability in the Cisco Discovery Protocol functionality of Cisco ATA 190 Series Analog Telephone Adapter firmware could allow an unauthenticated, remote attacker to execute arbitrary code on an affected device and cause Cisco Discovery Protocol service to restart. This vulnerability is due to missing length validation of certain Cisco Discovery Protocol packet header fields. An attacker could exploit these vulnerabilities by sending a malicious Cisco Discovery Protocol packet to an affected device. A successful exploit could allow the attacker to execute code on the affected device and cause Cisco Discovery Protocol to restart unexpectedly, resulting in a DoS condition.
Severity
5.3 (Medium)
{
"affected": [],
"aliases": [
"CVE-2022-20688"
],
"database_specific": {
"cwe_ids": [
"CWE-125",
"CWE-1284",
"CWE-20"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-12-12T09:15:00Z",
"severity": "MODERATE"
},
"details": "A vulnerability in the Cisco Discovery Protocol functionality of Cisco ATA 190 Series Analog Telephone Adapter firmware could allow an unauthenticated, remote attacker to execute arbitrary code on an affected device and cause Cisco Discovery Protocol service to restart. This vulnerability is due to missing length validation of certain Cisco Discovery Protocol packet header fields. An attacker could exploit these vulnerabilities by sending a malicious Cisco Discovery Protocol packet to an affected device. A successful exploit could allow the attacker to execute code on the affected device and cause Cisco Discovery Protocol to restart unexpectedly, resulting in a DoS condition.",
"id": "GHSA-5jj8-q8mr-r2vw",
"modified": "2022-12-14T18:30:28Z",
"published": "2022-12-12T09:30:36Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-20688"
},
{
"type": "WEB",
"url": "https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-ata19x-multivuln-GEZYVvs"
},
{
"type": "WEB",
"url": "https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-ata19x-multivuln-GEZYVvs"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
}
]
}
GHSA-5MPF-9QFH-9G4R
Vulnerability from github – Published: 2026-03-25 18:31 – Updated: 2026-03-27 15:30
VLAI
Details
Improper Validation of Specified Quantity in Input vulnerability in GalleryCreator SimpLy Gallery simply-gallery-block allows Accessing Functionality Not Properly Constrained by ACLs.This issue affects SimpLy Gallery: from n/a through <= 3.3.2.
Severity
9.9 (Critical)
{
"affected": [],
"aliases": [
"CVE-2026-25345"
],
"database_specific": {
"cwe_ids": [
"CWE-1284"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-03-25T17:16:45Z",
"severity": "CRITICAL"
},
"details": "Improper Validation of Specified Quantity in Input vulnerability in GalleryCreator SimpLy Gallery simply-gallery-block allows Accessing Functionality Not Properly Constrained by ACLs.This issue affects SimpLy Gallery: from n/a through \u003c= 3.3.2.",
"id": "GHSA-5mpf-9qfh-9g4r",
"modified": "2026-03-27T15:30:25Z",
"published": "2026-03-25T18:31:51Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-25345"
},
{
"type": "WEB",
"url": "https://patchstack.com/database/Wordpress/Plugin/simply-gallery-block/vulnerability/wordpress-simply-gallery-plugin-3-3-2-arbitrary-code-execution-vulnerability?_s_id=cve"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-5V77-J66X-4C4G
Vulnerability from github – Published: 2022-05-24 22:07 – Updated: 2022-05-24 22:07
VLAI
Summary
Missing validation causes denial of service via `Conv3DBackpropFilterV2`
Details
Impact
The implementation of tf.raw_ops.Conv3DBackpropFilterV2 does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:
import tensorflow as tf
tf.raw_ops.Conv3DBackpropFilterV2(
input=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16),
filter_sizes=tf.constant(0, shape=[], dtype=tf.int32),
out_backprop=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16),
strides=[1, 1, 1, 1, 1],
padding="VALID",
data_format="NDHWC",
dilations=[1, 1, 1, 1, 1])
The code does not validate that the filter_sizes argument is a vector.
Patches
We have patched the issue in GitHub commit 174c5096f303d5be7ed2ca2662b08371bff4ab88.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.
Severity
5.5 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2022-29196"
],
"database_specific": {
"cwe_ids": [
"CWE-1284",
"CWE-20"
],
"github_reviewed": true,
"github_reviewed_at": "2022-05-24T22:07:44Z",
"nvd_published_at": "2022-05-20T22:16:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nThe implementation of [`tf.raw_ops.Conv3DBackpropFilterV2`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/conv_grad_ops_3d.cc) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.Conv3DBackpropFilterV2(\n input=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16),\n filter_sizes=tf.constant(0, shape=[], dtype=tf.int32),\n out_backprop=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16),\n strides=[1, 1, 1, 1, 1],\n padding=\"VALID\",\n data_format=\"NDHWC\",\n dilations=[1, 1, 1, 1, 1])\n```\n \nThe code does not validate that the `filter_sizes` argument is a vector.\n \n### Patches\nWe have patched the issue in GitHub commit [174c5096f303d5be7ed2ca2662b08371bff4ab88](https://github.com/tensorflow/tensorflow/commit/174c5096f303d5be7ed2ca2662b08371bff4ab88).\n\nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.",
"id": "GHSA-5v77-j66x-4c4g",
"modified": "2022-05-24T22:07:44Z",
"published": "2022-05-24T22:07:44Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5v77-j66x-4c4g"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-29196"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/174c5096f303d5be7ed2ca2662b08371bff4ab88"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/conv_grad_ops_3d.cc"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0"
}
],
"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"
}
],
"summary": "Missing validation causes denial of service via `Conv3DBackpropFilterV2`"
}
GHSA-5VHQ-WV6W-VJ48
Vulnerability from github – Published: 2022-08-18 00:00 – Updated: 2026-02-25 15:31
VLAI
Details
Buffer Over-read in GitHub repository vim/vim prior to 9.0.0217.
Severity
7.8 (High)
{
"affected": [],
"aliases": [
"CVE-2022-2845"
],
"database_specific": {
"cwe_ids": [
"CWE-126",
"CWE-1284"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-08-17T15:15:00Z",
"severity": "HIGH"
},
"details": "Buffer Over-read in GitHub repository vim/vim prior to 9.0.0217.",
"id": "GHSA-5vhq-wv6w-vj48",
"modified": "2026-02-25T15:31:35Z",
"published": "2022-08-18T00:00:17Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-2845"
},
{
"type": "WEB",
"url": "https://github.com/vim/vim/commit/e98c88c44c308edaea5994b8ad4363e65030968c"
},
{
"type": "WEB",
"url": "https://huntr.dev/bounties/3e1d31ac-1cfd-4a9f-bc5c-213376b69445"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/C72HDIMR3KTTAO7QGTXWUMPBNFUFIBRD"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/XWOJOA7PZZAMBI5GFTL6PWHXMWSDLUXL"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/C72HDIMR3KTTAO7QGTXWUMPBNFUFIBRD"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/XWOJOA7PZZAMBI5GFTL6PWHXMWSDLUXL"
},
{
"type": "WEB",
"url": "https://security.gentoo.org/glsa/202305-16"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-5XGR-WQ7F-67XJ
Vulnerability from github – Published: 2022-03-04 00:00 – Updated: 2025-11-04 18:30
VLAI
Details
A buffer overflow vulnerability exists in FRRouting through 8.1.0 due to missing a check on the input packet length in the babel_packet_examin function in babeld/message.c.
Severity
7.8 (High)
{
"affected": [],
"aliases": [
"CVE-2022-26127"
],
"database_specific": {
"cwe_ids": [
"CWE-119",
"CWE-1284"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-03-03T18:15:00Z",
"severity": "HIGH"
},
"details": "A buffer overflow vulnerability exists in FRRouting through 8.1.0 due to missing a check on the input packet length in the babel_packet_examin function in babeld/message.c.",
"id": "GHSA-5xgr-wq7f-67xj",
"modified": "2025-11-04T18:30:38Z",
"published": "2022-03-04T00:00:17Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-26127"
},
{
"type": "WEB",
"url": "https://github.com/FRRouting/frr/issues/10487"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2024/04/msg00019.html"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2024/09/msg00007.html"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-627Q-G293-49Q7
Vulnerability from github – Published: 2022-02-07 22:01 – Updated: 2024-11-07 22:27
VLAI
Summary
Abort caused by allocating a vector that is too large in Tensorflow
Details
Impact
During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user:
const auto num_dims = Value(shape_dim);
std::vector<DimensionHandle> dims;
dims.reserve(num_dims);
Patches
We have patched the issue in GitHub commit 1361fb7e29449629e1df94d44e0427ebec8c83c7.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Severity
6.5 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.5.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.6.0"
},
{
"fixed": "2.6.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.7.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.5.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.6.0"
},
{
"fixed": "2.6.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.7.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.5.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.6.0"
},
{
"fixed": "2.6.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.7.0"
},
{
"fixed": "2.7.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.7.0"
]
}
],
"aliases": [
"CVE-2022-23580"
],
"database_specific": {
"cwe_ids": [
"CWE-1284",
"CWE-400"
],
"github_reviewed": true,
"github_reviewed_at": "2022-02-04T20:00:51Z",
"nvd_published_at": "2022-02-04T23:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nDuring shape inference, TensorFlow can [allocate a large vector](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L788-L790) based on a value from a tensor controlled by the user:\n\n```cc\n const auto num_dims = Value(shape_dim);\n std::vector\u003cDimensionHandle\u003e dims;\n dims.reserve(num_dims);\n``` \n \n### Patches \nWe have patched the issue in GitHub commit [1361fb7e29449629e1df94d44e0427ebec8c83c7](https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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. ",
"id": "GHSA-627q-g293-49q7",
"modified": "2024-11-07T22:27:04Z",
"published": "2022-02-07T22:01:24Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-23580"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-89.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-144.yaml"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L788-L790"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "Abort caused by allocating a vector that is too large in Tensorflow"
}
GHSA-633W-44JW-WRP4
Vulnerability from github – Published: 2026-02-10 21:31 – Updated: 2026-02-10 21:31
VLAI
Details
Improper bound check within AMD CPU microcode can allow a malicious guest to write to host memory, potentially resulting in loss of integrity.
Severity
{
"affected": [],
"aliases": [
"CVE-2025-52534"
],
"database_specific": {
"cwe_ids": [
"CWE-1284"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-02-10T20:16:46Z",
"severity": "MODERATE"
},
"details": "Improper bound check within AMD CPU microcode can allow a malicious guest to write to host memory, potentially resulting in loss of integrity.",
"id": "GHSA-633w-44jw-wrp4",
"modified": "2026-02-10T21:31:31Z",
"published": "2026-02-10T21:31:31Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-52534"
},
{
"type": "WEB",
"url": "https://www.amd.com/en/resources/product-security/bulletin/AMD-SB-3023.html"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:N/SC:N/SI:L/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
GHSA-676X-W6CW-FJ64
Vulnerability from github – Published: 2026-01-05 18:30 – Updated: 2026-01-20 15:33
VLAI
Details
Improper Validation of Specified Quantity in Input vulnerability in SaasProject Booking Package allows Accessing Functionality Not Properly Constrained by ACLs.This issue affects Booking Package: from n/a through 1.6.27.
Severity
7.5 (High)
{
"affected": [],
"aliases": [
"CVE-2024-30516"
],
"database_specific": {
"cwe_ids": [
"CWE-1284"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-01-05T17:15:44Z",
"severity": "HIGH"
},
"details": "Improper Validation of Specified Quantity in Input vulnerability in SaasProject Booking Package allows Accessing Functionality Not Properly Constrained by ACLs.This issue affects Booking Package: from n/a through 1.6.27.",
"id": "GHSA-676x-w6cw-fj64",
"modified": "2026-01-20T15:33:06Z",
"published": "2026-01-05T18:30:22Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-30516"
},
{
"type": "WEB",
"url": "https://patchstack.com/database/wordpress/plugin/booking-package/vulnerability/wordpress-booking-package-plugin-1-6-27-price-manipulation-vulnerability?_s_id=cve"
},
{
"type": "WEB",
"url": "https://vdp.patchstack.com/database/wordpress/plugin/booking-package/vulnerability/wordpress-booking-package-plugin-1-6-27-price-manipulation-vulnerability?_s_id=cve"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N",
"type": "CVSS_V3"
}
]
}
Mitigation MIT-5
Implementation
Strategy: Input Validation
- Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
- When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue."
- Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
No CAPEC attack patterns related to this CWE.