Common Weakness Enumeration
CWE-617
AllowedReachable Assertion
Abstraction: Base · Status: Draft
The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.
989 vulnerabilities reference this CWE, most recent first.
GHSA-9CXR-PMG7-9W5V
Vulnerability from github – Published: 2022-05-13 01:32 – Updated: 2022-05-13 01:32
VLAI
Details
A problem with the implementation of the new serve-stale feature in BIND 9.12 can lead to an assertion failure in rbtdb.c, even when stale-answer-enable is off. Additionally, problematic interaction between the serve-stale feature and NSEC aggressive negative caching can in some cases cause undesirable behavior from named, such as a recursion loop or excessive logging. Deliberate exploitation of this condition could cause operational problems depending on the particular manifestation -- either degradation or denial of service. Affects BIND 9.12.0 and 9.12.1.
Severity
7.5 (High)
{
"affected": [],
"aliases": [
"CVE-2018-5737"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2019-01-16T20:29:00Z",
"severity": "HIGH"
},
"details": "A problem with the implementation of the new serve-stale feature in BIND 9.12 can lead to an assertion failure in rbtdb.c, even when stale-answer-enable is off. Additionally, problematic interaction between the serve-stale feature and NSEC aggressive negative caching can in some cases cause undesirable behavior from named, such as a recursion loop or excessive logging. Deliberate exploitation of this condition could cause operational problems depending on the particular manifestation -- either degradation or denial of service. Affects BIND 9.12.0 and 9.12.1.",
"id": "GHSA-9cxr-pmg7-9w5v",
"modified": "2022-05-13T01:32:04Z",
"published": "2022-05-13T01:32:04Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2018-5737"
},
{
"type": "WEB",
"url": "https://kb.isc.org/docs/aa-01606"
},
{
"type": "WEB",
"url": "https://security.netapp.com/advisory/ntap-20180926-0004"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/104236"
},
{
"type": "WEB",
"url": "http://www.securitytracker.com/id/1040942"
}
],
"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-9FPG-838V-WPV7
Vulnerability from github – Published: 2022-09-16 22:20 – Updated: 2022-09-19 19:36
VLAI
Summary
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`
Details
Impact
If FakeQuantWithMinMaxVars is given min or max tensors of a nonzero rank, it results in a CHECK fail that can be used to trigger a denial of service attack.
import tensorflow as tf
num_bits = 8
narrow_range = False
inputs = tf.constant(0, shape=[2,3], dtype=tf.float32)
min = tf.constant(0, shape=[2,3], dtype=tf.float32)
max = tf.constant(0, shape=[2,3], dtype=tf.float32)
tf.raw_ops.FakeQuantWithMinMaxVars(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range)
Patches
We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0.
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.
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, Secure Systems Labs, Brown University. - 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
Severity
5.9 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "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"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "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-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "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-2022-35971"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T22:20:09Z",
"nvd_published_at": "2022-09-16T21:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nIf `FakeQuantWithMinMaxVars` is given `min` or `max` tensors of a nonzero rank, it results in a `CHECK` fail that can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\n\nnum_bits = 8\nnarrow_range = False\ninputs = tf.constant(0, shape=[2,3], dtype=tf.float32)\nmin = tf.constant(0, shape=[2,3], dtype=tf.float32)\nmax = tf.constant(0, shape=[2,3], dtype=tf.float32)\ntf.raw_ops.FakeQuantWithMinMaxVars(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0).\n\nThe 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.\n\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\n### Attribution\nThis vulnerability has been reported by:\n - Neophytos Christou, Secure Systems Labs, Brown University.\n - \u5218\u529b\u6e90, Information System \u0026 Security and Countermeasures Experiments Center, Beijing Institute of Technology\n",
"id": "GHSA-9fpg-838v-wpv7",
"modified": "2022-09-19T19:36:24Z",
"published": "2022-09-16T22:20:09Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9fpg-838v-wpv7"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35971"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": " TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`"
}
GHSA-9GHH-P583-M6M8
Vulnerability from github – Published: 2026-03-25 12:30 – Updated: 2026-04-24 18:30
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
mm: thp: deny THP for files on anonymous inodes
file_thp_enabled() incorrectly allows THP for files on anonymous inodes (e.g. guest_memfd and secretmem). These files are created via alloc_file_pseudo(), which does not call get_write_access() and leaves inode->i_writecount at 0. Combined with S_ISREG(inode->i_mode) being true, they appear as read-only regular files when CONFIG_READ_ONLY_THP_FOR_FS is enabled, making them eligible for THP collapse.
Anonymous inodes can never pass the inode_is_open_for_write() check since their i_writecount is never incremented through the normal VFS open path. The right thing to do is to exclude them from THP eligibility altogether, since CONFIG_READ_ONLY_THP_FOR_FS was designed for real filesystem files (e.g. shared libraries), not for pseudo-filesystem inodes.
For guest_memfd, this allows khugepaged and MADV_COLLAPSE to create large folios in the page cache via the collapse path, but the guest_memfd fault handler does not support large folios. This triggers WARN_ON_ONCE(folio_test_large(folio)) in kvm_gmem_fault_user_mapping().
For secretmem, collapse_file() tries to copy page contents through the direct map, but secretmem pages are removed from the direct map. This can result in a kernel crash:
BUG: unable to handle page fault for address: ffff88810284d000
RIP: 0010:memcpy_orig+0x16/0x130
Call Trace:
collapse_file
hpage_collapse_scan_file
madvise_collapse
Secretmem is not affected by the crash on upstream as the memory failure recovery handles the failed copy gracefully, but it still triggers confusing false memory failure reports:
Memory failure: 0x106d96f: recovery action for clean unevictable
LRU page: Recovered
Check IS_ANON_FILE(inode) in file_thp_enabled() to deny THP for all anonymous inode files.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2026-23375"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-03-25T11:16:37Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nmm: thp: deny THP for files on anonymous inodes\n\nfile_thp_enabled() incorrectly allows THP for files on anonymous inodes\n(e.g. guest_memfd and secretmem). These files are created via\nalloc_file_pseudo(), which does not call get_write_access() and leaves\ninode-\u003ei_writecount at 0. Combined with S_ISREG(inode-\u003ei_mode) being\ntrue, they appear as read-only regular files when\nCONFIG_READ_ONLY_THP_FOR_FS is enabled, making them eligible for THP\ncollapse.\n\nAnonymous inodes can never pass the inode_is_open_for_write() check\nsince their i_writecount is never incremented through the normal VFS\nopen path. The right thing to do is to exclude them from THP eligibility\naltogether, since CONFIG_READ_ONLY_THP_FOR_FS was designed for real\nfilesystem files (e.g. shared libraries), not for pseudo-filesystem\ninodes.\n\nFor guest_memfd, this allows khugepaged and MADV_COLLAPSE to create\nlarge folios in the page cache via the collapse path, but the\nguest_memfd fault handler does not support large folios. This triggers\nWARN_ON_ONCE(folio_test_large(folio)) in kvm_gmem_fault_user_mapping().\n\nFor secretmem, collapse_file() tries to copy page contents through the\ndirect map, but secretmem pages are removed from the direct map. This\ncan result in a kernel crash:\n\n BUG: unable to handle page fault for address: ffff88810284d000\n RIP: 0010:memcpy_orig+0x16/0x130\n Call Trace:\n collapse_file\n hpage_collapse_scan_file\n madvise_collapse\n\nSecretmem is not affected by the crash on upstream as the memory failure\nrecovery handles the failed copy gracefully, but it still triggers\nconfusing false memory failure reports:\n\n Memory failure: 0x106d96f: recovery action for clean unevictable\n LRU page: Recovered\n\nCheck IS_ANON_FILE(inode) in file_thp_enabled() to deny THP for all\nanonymous inode files.",
"id": "GHSA-9ghh-p583-m6m8",
"modified": "2026-04-24T18:30:41Z",
"published": "2026-03-25T12:30:24Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-23375"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/0524ee56af2c9bfbad152a810f1ca95de8ca00d7"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/08de46a75f91a6661bc1ce0a93614f4bc313c581"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/dd085fe9a8ebfc5d10314c60452db38d2b75e609"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/f6fa05f0dddd387417d0c28281ddb951582514d6"
}
],
"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-9GHJ-MRHR-8WXX
Vulnerability from github – Published: 2025-04-18 15:31 – Updated: 2025-11-07 00:30
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
net_sched: skbprio: Remove overly strict queue assertions
In the current implementation, skbprio enqueue/dequeue contains an assertion that fails under certain conditions when SKBPRIO is used as a child qdisc under TBF with specific parameters. The failure occurs because TBF sometimes peeks at packets in the child qdisc without actually dequeuing them when tokens are unavailable.
This peek operation creates a discrepancy between the parent and child qdisc queue length counters. When TBF later receives a high-priority packet, SKBPRIO's queue length may show a different value than what's reflected in its internal priority queue tracking, triggering the assertion.
The fix removes this overly strict assertions in SKBPRIO, they are not necessary at all.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2025-38637"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-04-18T07:15:43Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nnet_sched: skbprio: Remove overly strict queue assertions\n\nIn the current implementation, skbprio enqueue/dequeue contains an assertion\nthat fails under certain conditions when SKBPRIO is used as a child qdisc under\nTBF with specific parameters. The failure occurs because TBF sometimes peeks at\npackets in the child qdisc without actually dequeuing them when tokens are\nunavailable.\n\nThis peek operation creates a discrepancy between the parent and child qdisc\nqueue length counters. When TBF later receives a high-priority packet,\nSKBPRIO\u0027s queue length may show a different value than what\u0027s reflected in its\ninternal priority queue tracking, triggering the assertion.\n\nThe fix removes this overly strict assertions in SKBPRIO, they are not\nnecessary at all.",
"id": "GHSA-9ghj-mrhr-8wxx",
"modified": "2025-11-07T00:30:26Z",
"published": "2025-04-18T15:31:38Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-38637"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/034b293bf17c124fec0f0e663f81203b00aa7a50"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/1284733bab736e598341f1d3f3b94e2a322864a8"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/1dcc144c322a8d526b791135604c0663f1af9d85"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/2286770b07cb5268c03d11274b8efd43dff0d380"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/2f35b7673a3aa3d09b3eb05811669622ebaa98ca"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/32ee79682315e6d3c99947b3f38b078a09a66919"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/7abc8318ce0712182bf0783dcfdd9a6a8331160e"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/864ca690ff135078d374bd565b9872f161c614bc"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/ce8fe975fd99b49c29c42e50f2441ba53112b2e8"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2025/05/msg00030.html"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2025/05/msg00045.html"
}
],
"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-9H5H-27F6-M48G
Vulnerability from github – Published: 2022-04-02 00:00 – Updated: 2022-04-09 00:00
VLAI
Details
Possible assertion due to improper validation of invalid NR CSI-IM resource configuration in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile
Severity
7.5 (High)
{
"affected": [],
"aliases": [
"CVE-2021-30328"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-04-01T05:15:00Z",
"severity": "HIGH"
},
"details": "Possible assertion due to improper validation of invalid NR CSI-IM resource configuration in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile",
"id": "GHSA-9h5h-27f6-m48g",
"modified": "2022-04-09T00:00:48Z",
"published": "2022-04-02T00:00:17Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-30328"
},
{
"type": "WEB",
"url": "https://www.qualcomm.com/company/product-security/bulletins/march-2022-bulletin"
}
],
"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:H",
"type": "CVSS_V3"
}
]
}
GHSA-9J4V-PP28-MXV7
Vulnerability from github – Published: 2022-09-16 21:13 – Updated: 2022-09-19 19:50
VLAI
Summary
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannel`
Details
Impact
If FakeQuantWithMinMaxVarsPerChannel is given min or max tensors of a rank other than one, it results in a CHECK fail that can be used to trigger a denial of service attack.
import tensorflow as tf
num_bits = 8
narrow_range = False
inputs = tf.constant(0, shape=[4], dtype=tf.float32)
min = tf.constant([], shape=[4,0,0], dtype=tf.float32)
max = tf.constant(0, shape=[4], dtype=tf.float32)
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range)
Patches
We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0.
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.
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, Secure Systems Labs, Brown University.
Severity
5.9 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
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"events": [
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},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
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{
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"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
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"events": [
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"fixed": "2.8.1"
}
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}
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},
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"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
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"introduced": "2.9.0"
},
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"fixed": "2.9.1"
}
],
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]
},
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"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
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"fixed": "2.7.2"
}
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"name": "tensorflow-cpu"
},
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},
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}
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"name": "tensorflow-cpu"
},
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]
},
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"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
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},
{
"fixed": "2.7.2"
}
],
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}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2022-36019"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T21:13:43Z",
"nvd_published_at": "2022-09-16T22:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nIf `FakeQuantWithMinMaxVarsPerChannel` is given `min` or `max` tensors of a rank other than one, it results in a `CHECK` fail that can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\n\nnum_bits = 8\nnarrow_range = False\ninputs = tf.constant(0, shape=[4], dtype=tf.float32)\nmin = tf.constant([], shape=[4,0,0], dtype=tf.float32)\nmax = tf.constant(0, shape=[4], dtype=tf.float32)\ntf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0).\n\nThe 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.\n\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\n### Attribution\nThis vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.\n",
"id": "GHSA-9j4v-pp28-mxv7",
"modified": "2022-09-19T19:50:58Z",
"published": "2022-09-16T21:13:43Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9j4v-pp28-mxv7"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-36019"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannel`"
}
GHSA-9JX4-FVC9-GC34
Vulnerability from github – Published: 2025-08-06 09:30 – Updated: 2025-08-06 09:30
VLAI
Details
Transient DOS while processing a random-access response (RAR) with an invalid PDU length on LTE network.
Severity
7.5 (High)
{
"affected": [],
"aliases": [
"CVE-2025-21452"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-08-06T08:15:26Z",
"severity": "HIGH"
},
"details": "Transient DOS while processing a random-access response (RAR) with an invalid PDU length on LTE network.",
"id": "GHSA-9jx4-fvc9-gc34",
"modified": "2025-08-06T09:30:35Z",
"published": "2025-08-06T09:30:35Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-21452"
},
{
"type": "WEB",
"url": "https://docs.qualcomm.com/product/publicresources/securitybulletin/august-2025-bulletin.html"
}
],
"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:H",
"type": "CVSS_V3"
}
]
}
GHSA-9MQP-7V2H-2382
Vulnerability from github – Published: 2020-09-25 18:28 – Updated: 2024-10-28 20:02
VLAI
Summary
Denial of Service in Tensorflow
Details
Impact
The SparseFillEmptyRowsGrad implementation has incomplete validation of the shapes of its arguments:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L235-L241
Although reverse_index_map_t and grad_values_t are accessed in a similar pattern, only reverse_index_map_t is validated to be of proper shape. Hence, malicious users can pass a bad grad_values_t to trigger an assertion failure in vec, causing denial of service in serving installations.
Patches
We have patched the issue in 390611e0d45c5793c7066110af37c8514e6a6c54 and will release a patch release for all affected versions.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
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 is a variant of GHSA-63xm-rx5p-xvqr
Severity
5.3 (Medium)
{
"affected": [
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"ecosystem": "PyPI",
"name": "tensorflow"
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"ranges": [
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]
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"name": "tensorflow"
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"introduced": "2.3.0"
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}
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],
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"2.3.0"
]
},
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"name": "tensorflow-cpu"
},
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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-cpu"
},
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],
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]
},
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"name": "tensorflow-gpu"
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]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.2.0"
},
{
"fixed": "2.2.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.2.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.3.0"
},
{
"fixed": "2.3.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.3.0"
]
}
],
"aliases": [
"CVE-2020-15194"
],
"database_specific": {
"cwe_ids": [
"CWE-20",
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2020-09-25T16:38:49Z",
"nvd_published_at": "2020-09-25T19:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nThe `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L235-L241\n\nAlthough `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations.\n\n### Patches\nWe have patched the issue in 390611e0d45c5793c7066110af37c8514e6a6c54 and will release a patch release for all affected versions.\n\nWe recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.\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 is a variant of [GHSA-63xm-rx5p-xvqr](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr)",
"id": "GHSA-9mqp-7v2h-2382",
"modified": "2024-10-28T20:02:43Z",
"published": "2020-09-25T18:28:19Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9mqp-7v2h-2382"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-15194"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-274.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-309.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-117.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
}
],
"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"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Denial of Service in Tensorflow"
}
GHSA-9QV7-83W4-8JMW
Vulnerability from github – Published: 2022-05-24 19:05 – Updated: 2022-05-24 19:05
VLAI
Details
There is an Assertion 'block_found' failed at js-parser-statm.c:2003 parser_parse_try_statement_end in JerryScript 2.2.0.
{
"affected": [],
"aliases": [
"CVE-2020-23314"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-06-10T23:15:00Z",
"severity": "HIGH"
},
"details": "There is an Assertion \u0027block_found\u0027 failed at js-parser-statm.c:2003 parser_parse_try_statement_end in JerryScript 2.2.0.",
"id": "GHSA-9qv7-83w4-8jmw",
"modified": "2022-05-24T19:05:13Z",
"published": "2022-05-24T19:05:13Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-23314"
},
{
"type": "WEB",
"url": "https://github.com/jerryscript-project/jerryscript/issues/3825"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-9QXF-FRVG-WWGQ
Vulnerability from github – Published: 2025-01-22 15:32 – Updated: 2025-01-27 21:30
VLAI
Details
Open5GS MME versions <= 2.6.4 contain an assertion that can be remotely triggered via a malformed ASN.1 packet over the S1AP interface. An attacker may send an Initial UE Message message missing a required PLMN Identity field to repeatedly crash the MME, resulting in denial of service.
Severity
5.3 (Medium)
{
"affected": [],
"aliases": [
"CVE-2023-37012"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-01-22T15:15:10Z",
"severity": "MODERATE"
},
"details": "Open5GS MME versions \u003c= 2.6.4 contain an assertion that can be remotely triggered via a malformed ASN.1 packet over the S1AP interface. An attacker may send an `Initial UE Message` message missing a required `PLMN Identity` field to repeatedly crash the MME, resulting in denial of service.",
"id": "GHSA-9qxf-frvg-wwgq",
"modified": "2025-01-27T21:30:53Z",
"published": "2025-01-22T15:32:34Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-37012"
},
{
"type": "WEB",
"url": "https://cellularsecurity.org/ransacked"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L",
"type": "CVSS_V3"
}
]
}
Mitigation
Implementation
Make sensitive open/close operation non reachable by directly user-controlled data (e.g. open/close resources)
Mitigation
Implementation
Strategy: Input Validation
Perform input validation on user data.
No CAPEC attack patterns related to this CWE.