Common Weakness Enumeration
CWE-787
Allowed-with-ReviewOut-of-bounds Write
Abstraction: Base · Status: Draft
The product writes data past the end, or before the beginning, of the intended buffer.
15246 vulnerabilities reference this CWE, most recent first.
GHSA-4HH5-2HP6-3Q94
Vulnerability from github – Published: 2022-05-24 17:42 – Updated: 2026-07-05 03:30
VLAI
Details
Buffer overflow in Yz1 0.30 and 0.32, as used in IZArc 4.4, ZipGenius 6.3.2.3116, and Explzh (extension) 8.14, allows attackers to execute arbitrary code via a crafted archive file, related to filename handling.
Severity
7.8 (High)
{
"affected": [],
"aliases": [
"CVE-2020-24175"
],
"database_specific": {
"cwe_ids": [
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-02-22T16:15:00Z",
"severity": "HIGH"
},
"details": "Buffer overflow in Yz1 0.30 and 0.32, as used in IZArc 4.4, ZipGenius 6.3.2.3116, and Explzh (extension) 8.14, allows attackers to execute arbitrary code via a crafted archive file, related to filename handling.",
"id": "GHSA-4hh5-2hp6-3q94",
"modified": "2026-07-05T03:30:39Z",
"published": "2022-05-24T17:42:47Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-24175"
},
{
"type": "WEB",
"url": "https://gist.github.com/illikainen/315a420a9c28cbe882e16b8eba40b2e1"
},
{
"type": "WEB",
"url": "https://gist.github.com/illikainen/ced14e08e00747fef613ba619bb25bb4"
},
{
"type": "WEB",
"url": "https://illikainen.dev/advisories/014-yz1-izarc"
},
{
"type": "WEB",
"url": "http://yz1.com"
}
],
"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-4HHR-JHMC-R3CJ
Vulnerability from github – Published: 2022-05-17 00:27 – Updated: 2025-04-12 13:06
VLAI
Details
When decoding data out of a dataset encoded with the H5Z_NBIT decoding, the HDF5 1.8.16 library will fail to ensure that the precision is within the bounds of the size leading to arbitrary code execution.
Severity
8.6 (High)
{
"affected": [],
"aliases": [
"CVE-2016-4331"
],
"database_specific": {
"cwe_ids": [
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2016-11-18T20:59:00Z",
"severity": "HIGH"
},
"details": "When decoding data out of a dataset encoded with the H5Z_NBIT decoding, the HDF5 1.8.16 library will fail to ensure that the precision is within the bounds of the size leading to arbitrary code execution.",
"id": "GHSA-4hhr-jhmc-r3cj",
"modified": "2025-04-12T13:06:33Z",
"published": "2022-05-17T00:27:23Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2016-4331"
},
{
"type": "WEB",
"url": "https://security.gentoo.org/glsa/201701-13"
},
{
"type": "WEB",
"url": "http://www.debian.org/security/2016/dsa-3727"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/94411"
},
{
"type": "WEB",
"url": "http://www.talosintelligence.com/reports/TALOS-2016-0177"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-4HM2-JFF2-CW82
Vulnerability from github – Published: 2025-12-16 00:30 – Updated: 2025-12-16 00:30
VLAI
Details
AA maliciously crafted CATPART file, when parsed through certain Autodesk products, can force an Out-of-Bounds Write vulnerability. A malicious actor may leverage this vulnerability to cause a crash, cause data corruption, or execute arbitrary code in the context of the current process.
Severity
7.8 (High)
{
"affected": [],
"aliases": [
"CVE-2025-10884"
],
"database_specific": {
"cwe_ids": [
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-12-16T00:16:00Z",
"severity": "HIGH"
},
"details": "AA maliciously crafted CATPART file, when parsed through certain Autodesk products, can force an Out-of-Bounds Write vulnerability. A malicious actor may leverage this vulnerability to cause a crash, cause data corruption, or execute arbitrary code in the context of the current process.",
"id": "GHSA-4hm2-jff2-cw82",
"modified": "2025-12-16T00:30:30Z",
"published": "2025-12-16T00:30:30Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-10884"
},
{
"type": "WEB",
"url": "https://www.autodesk.com/products/autodesk-access/overview"
},
{
"type": "WEB",
"url": "https://www.autodesk.com/trust/security-advisories/adsk-sa-2025-0024"
}
],
"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-4HMC-9Q64-H23P
Vulnerability from github – Published: 2022-05-24 16:59 – Updated: 2022-05-24 16:59
VLAI
Details
A heap buffer overflow bug in libpl_droidsonroids_gif before 1.2.19, as used in WhatsApp for Android before version 2.19.291 could allow remote attackers to execute arbitrary code or cause a denial of service.
{
"affected": [],
"aliases": [
"CVE-2019-11933"
],
"database_specific": {
"cwe_ids": [
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2019-10-23T16:15:00Z",
"severity": "CRITICAL"
},
"details": "A heap buffer overflow bug in libpl_droidsonroids_gif before 1.2.19, as used in WhatsApp for Android before version 2.19.291 could allow remote attackers to execute arbitrary code or cause a denial of service.",
"id": "GHSA-4hmc-9q64-h23p",
"modified": "2022-05-24T16:59:44Z",
"published": "2022-05-24T16:59:44Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2019-11933"
},
{
"type": "WEB",
"url": "https://www.facebook.com/security/advisories/cve-2019-11933"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-4HP4-5C2H-V77H
Vulnerability from github – Published: 2025-12-16 00:30 – Updated: 2025-12-16 00:30
VLAI
Details
A maliciously crafted PRT file, when parsed through certain Autodesk products, can force a Memory corruption vulnerability. A malicious actor can leverage this vulnerability to execute arbitrary code in the context of the current process.
Severity
7.8 (High)
{
"affected": [],
"aliases": [
"CVE-2025-9457"
],
"database_specific": {
"cwe_ids": [
"CWE-120",
"CWE-122",
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-12-16T00:16:03Z",
"severity": "HIGH"
},
"details": "A maliciously crafted PRT file, when parsed through certain Autodesk products, can force a Memory corruption vulnerability. A malicious actor can leverage this vulnerability to execute arbitrary code in the context of the current process.",
"id": "GHSA-4hp4-5c2h-v77h",
"modified": "2025-12-16T00:30:30Z",
"published": "2025-12-16T00:30:30Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-9457"
},
{
"type": "WEB",
"url": "https://www.autodesk.com/products/autodesk-access/overview"
},
{
"type": "WEB",
"url": "https://www.autodesk.com/trust/security-advisories/adsk-sa-2025-0024"
}
],
"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-4HP5-9MP9-5574
Vulnerability from github – Published: 2023-04-09 21:30 – Updated: 2025-02-11 21:32
VLAI
Details
D-Link DIR878 1.30B08 was discovered to contain a stack overflow in the sub_498308 function. This vulnerability allows attackers to cause a Denial of Service (DoS) or execute arbitrary code via a crafted payload.
Severity
9.8 (Critical)
{
"affected": [],
"aliases": [
"CVE-2023-27718"
],
"database_specific": {
"cwe_ids": [
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2023-04-09T21:15:00Z",
"severity": "CRITICAL"
},
"details": "D-Link DIR878 1.30B08 was discovered to contain a stack overflow in the sub_498308 function. This vulnerability allows attackers to cause a Denial of Service (DoS) or execute arbitrary code via a crafted payload.",
"id": "GHSA-4hp5-9mp9-5574",
"modified": "2025-02-11T21:32:00Z",
"published": "2023-04-09T21:30:14Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-27718"
},
{
"type": "WEB",
"url": "https://github.com/HolyTruth/DIR_878-1.30B08/blob/main/1.md"
},
{
"type": "WEB",
"url": "https://www.dlink.com/en/security-bulletin"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-4HRH-9VMP-2JGG
Vulnerability from github – Published: 2021-05-21 14:23 – Updated: 2024-10-31 19:58
VLAI
Summary
Heap buffer overflow in `StringNGrams`
Details
Impact
An attacker can cause a heap buffer overflow by passing crafted inputs to tf.raw_ops.StringNGrams:
import tensorflow as tf
separator = b'\x02\x00'
ngram_widths = [7, 6, 11]
left_pad = b'\x7f\x7f\x7f\x7f\x7f'
right_pad = b'\x7f\x7f\x25\x5d\x53\x74'
pad_width = 50
preserve_short_sequences = True
l = ['', '', '', '', '', '', '', '', '', '', '']
data = tf.constant(l, shape=[11], dtype=tf.string)
l2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 3]
data_splits = tf.constant(l2, shape=[116], dtype=tf.int64)
out = tf.raw_ops.StringNGrams(data=data,
data_splits=data_splits, separator=separator,
ngram_widths=ngram_widths, left_pad=left_pad,
right_pad=right_pad, pad_width=pad_width,
preserve_short_sequences=preserve_short_sequences)
This is because the implementation fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements:
for (int ngram_index = 0; ngram_index < num_ngrams; ++ngram_index) {
int pad_width = get_pad_width(ngram_width);
int left_padding = std::max(0, pad_width - ngram_index);
int right_padding = std::max(0, pad_width - (num_ngrams - (ngram_index + 1)));
int num_tokens = ngram_width - (left_padding + right_padding);
int data_start_index = left_padding > 0 ? 0 : ngram_index - pad_width;
...
tstring* ngram = &output[ngram_index];
ngram->reserve(ngram_size);
for (int n = 0; n < left_padding; ++n) {
ngram->append(left_pad_);
ngram->append(separator_);
}
for (int n = 0; n < num_tokens - 1; ++n) {
ngram->append(data[data_start_index + n]);
ngram->append(separator_);
}
ngram->append(data[data_start_index + num_tokens - 1]); // <<<
for (int n = 0; n < right_padding; ++n) {
ngram->append(separator_);
ngram->append(right_pad_);
}
...
}
If input is such that num_tokens is 0, then, for data_start_index=0 (when left padding is present), the marked line would result in reading data[-1].
Patches
We have patched the issue in GitHub commit ba424dd8f16f7110eea526a8086f1a155f14f22b.
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"
},
{
"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": [
{
"introduced": "0"
},
{
"fixed": "2.1.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"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": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.1.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"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-2021-29542"
],
"database_specific": {
"cwe_ids": [
"CWE-131",
"CWE-787"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T21:54:20Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`:\n\n```python\nimport tensorflow as tf\n\nseparator = b\u0027\\x02\\x00\u0027 \nngram_widths = [7, 6, 11]\nleft_pad = b\u0027\\x7f\\x7f\\x7f\\x7f\\x7f\u0027\nright_pad = b\u0027\\x7f\\x7f\\x25\\x5d\\x53\\x74\u0027\npad_width = 50\npreserve_short_sequences = True\n \nl = [\u0027\u0027, \u0027\u0027, \u0027\u0027, \u0027\u0027, \u0027\u0027, \u0027\u0027, \u0027\u0027, \u0027\u0027, \u0027\u0027, \u0027\u0027, \u0027\u0027]\n \ndata = tf.constant(l, shape=[11], dtype=tf.string)\n \nl2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 3]\ndata_splits = tf.constant(l2, shape=[116], dtype=tf.int64)\n\nout = tf.raw_ops.StringNGrams(data=data,\n data_splits=data_splits, separator=separator,\n ngram_widths=ngram_widths, left_pad=left_pad,\n right_pad=right_pad, pad_width=pad_width,\n preserve_short_sequences=preserve_short_sequences)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements:\n \n```cc\nfor (int ngram_index = 0; ngram_index \u003c num_ngrams; ++ngram_index) {\n int pad_width = get_pad_width(ngram_width);\n int left_padding = std::max(0, pad_width - ngram_index);\n int right_padding = std::max(0, pad_width - (num_ngrams - (ngram_index + 1)));\n int num_tokens = ngram_width - (left_padding + right_padding);\n int data_start_index = left_padding \u003e 0 ? 0 : ngram_index - pad_width;\n ...\n tstring* ngram = \u0026output[ngram_index];\n ngram-\u003ereserve(ngram_size);\n for (int n = 0; n \u003c left_padding; ++n) {\n ngram-\u003eappend(left_pad_);\n ngram-\u003eappend(separator_);\n }\n for (int n = 0; n \u003c num_tokens - 1; ++n) {\n ngram-\u003eappend(data[data_start_index + n]);\n ngram-\u003eappend(separator_);\n }\n ngram-\u003eappend(data[data_start_index + num_tokens - 1]); // \u003c\u003c\u003c\n for (int n = 0; n \u003c right_padding; ++n) {\n ngram-\u003eappend(separator_);\n ngram-\u003eappend(right_pad_);\n }\n ...\n}\n```\n\nIf input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`.\n\n### Patches\nWe have patched the issue in GitHub commit [ba424dd8f16f7110eea526a8086f1a155f14f22b](https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b).\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-4hrh-9vmp-2jgg",
"modified": "2024-10-31T19:58:52Z",
"published": "2021-05-21T14:23:15Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29542"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-470.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-668.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-179.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": "Heap buffer overflow in `StringNGrams`"
}
GHSA-4HRJ-6FHW-HCVF
Vulnerability from github – Published: 2024-06-05 21:31 – Updated: 2024-06-05 21:31
VLAI
Details
An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_nan_publish_get_nl_params(), there is no input validation check on hal_req->service_specific_info_len coming from userspace, which can lead to a heap overwrite.
Severity
6.7 (Medium)
{
"affected": [],
"aliases": [
"CVE-2024-27374"
],
"database_specific": {
"cwe_ids": [
"CWE-122",
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-06-05T19:15:13Z",
"severity": "MODERATE"
},
"details": "An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_nan_publish_get_nl_params(), there is no input validation check on hal_req-\u003eservice_specific_info_len coming from userspace, which can lead to a heap overwrite.",
"id": "GHSA-4hrj-6fhw-hcvf",
"modified": "2024-06-05T21:31:27Z",
"published": "2024-06-05T21:31:27Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-27374"
},
{
"type": "WEB",
"url": "https://semiconductor.samsung.com/support/quality-support/product-security-updates"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-4HRQ-QFMM-86P6
Vulnerability from github – Published: 2025-12-06 15:30 – Updated: 2025-12-06 15:30
VLAI
Details
A security flaw has been discovered in Linksys RE6500, RE6250, RE6300, RE6350, RE7000 and RE9000 1.0.013.001/1.0.04.001/1.0.04.002/1.1.05.003/1.2.07.001. This vulnerability affects the function RE2000v2Repeater_get_wired_clientlist_setClientsName of the file mod_form.so. The manipulation of the argument clientsname_0 results in stack-based buffer overflow. The attack may be launched remotely. The exploit has been released to the public and may be exploited. The vendor was contacted early about this disclosure but did not respond in any way.
Severity
{
"affected": [],
"aliases": [
"CVE-2025-14136"
],
"database_specific": {
"cwe_ids": [
"CWE-119",
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-12-06T13:15:59Z",
"severity": "HIGH"
},
"details": "A security flaw has been discovered in Linksys RE6500, RE6250, RE6300, RE6350, RE7000 and RE9000 1.0.013.001/1.0.04.001/1.0.04.002/1.1.05.003/1.2.07.001. This vulnerability affects the function RE2000v2Repeater_get_wired_clientlist_setClientsName of the file mod_form.so. The manipulation of the argument clientsname_0 results in stack-based buffer overflow. The attack may be launched remotely. The exploit has been released to the public and may be exploited. The vendor was contacted early about this disclosure but did not respond in any way.",
"id": "GHSA-4hrq-qfmm-86p6",
"modified": "2025-12-06T15:30:16Z",
"published": "2025-12-06T15:30:16Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-14136"
},
{
"type": "WEB",
"url": "https://github.com/wudipjq/my_vuln/blob/main/Linksys2/vuln_65/65.md"
},
{
"type": "WEB",
"url": "https://github.com/wudipjq/my_vuln/blob/main/Linksys2/vuln_65/65.md#poc"
},
{
"type": "WEB",
"url": "https://vuldb.com/?ctiid.334525"
},
{
"type": "WEB",
"url": "https://vuldb.com/?id.334525"
},
{
"type": "WEB",
"url": "https://vuldb.com/?submit.697983"
},
{
"type": "WEB",
"url": "https://www.linksys.com"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P/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-4HVF-HXVG-F67V
Vulnerability from github – Published: 2022-02-09 23:53 – Updated: 2024-11-13 22:37
VLAI
Summary
Read and Write outside of bounds in TensorFlow
Details
Impact
An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.
Patches
We have patched the issue in GitHub commit 6364463d6f5b6254cac3d6aedf999b6a96225038. 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.
Attribution
This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team.
Severity
{
"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-2022-23560"
],
"database_specific": {
"cwe_ids": [
"CWE-125",
"CWE-787"
],
"github_reviewed": true,
"github_reviewed_at": "2022-02-03T20:41:13Z",
"nvd_published_at": "2022-02-04T23:15:00Z",
"severity": "HIGH"
},
"details": "### Impact\nAn attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in [the conversion from sparse tensors to dense tensors](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293).\n\n### Patches\nWe have patched the issue in GitHub commit [6364463d6f5b6254cac3d6aedf999b6a96225038](https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038).\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.\n\n### Attribution\nThis vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team.",
"id": "GHSA-4hvf-hxvg-f67v",
"modified": "2024-11-13T22:37:55Z",
"published": "2022-02-09T23:53:30Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-23560"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-69.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-124.yaml"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Read and Write outside of bounds in TensorFlow"
}
Mitigation MIT-3
Requirements
Strategy: Language Selection
- Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
- For example, many languages that perform their own memory management, such as Java and Perl, are not subject to buffer overflows. Other languages, such as Ada and C#, typically provide overflow protection, but the protection can be disabled by the programmer.
- Be wary that a language's interface to native code may still be subject to overflows, even if the language itself is theoretically safe.
Mitigation MIT-4.1
Architecture and Design
Strategy: Libraries or Frameworks
- Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
- Examples include the Safe C String Library (SafeStr) by Messier and Viega [REF-57], and the Strsafe.h library from Microsoft [REF-56]. These libraries provide safer versions of overflow-prone string-handling functions.
Mitigation MIT-10
Operation
Build and Compilation
Strategy: Environment Hardening
- Use automatic buffer overflow detection mechanisms that are offered by certain compilers or compiler extensions. Examples include: the Microsoft Visual Studio /GS flag, Fedora/Red Hat FORTIFY_SOURCE GCC flag, StackGuard, and ProPolice, which provide various mechanisms including canary-based detection and range/index checking.
- D3-SFCV (Stack Frame Canary Validation) from D3FEND [REF-1334] discusses canary-based detection in detail.
Mitigation MIT-9
Implementation
- Consider adhering to the following rules when allocating and managing an application's memory:
- Double check that the buffer is as large as specified.
- When using functions that accept a number of bytes to copy, such as strncpy(), be aware that if the destination buffer size is equal to the source buffer size, it may not NULL-terminate the string.
- Check buffer boundaries if accessing the buffer in a loop and make sure there is no danger of writing past the allocated space.
- If necessary, truncate all input strings to a reasonable length before passing them to the copy and concatenation functions.
Mitigation MIT-11
Operation
Build and Compilation
Strategy: Environment Hardening
- Run or compile the software using features or extensions that randomly arrange the positions of a program's executable and libraries in memory. Because this makes the addresses unpredictable, it can prevent an attacker from reliably jumping to exploitable code.
- Examples include Address Space Layout Randomization (ASLR) [REF-58] [REF-60] and Position-Independent Executables (PIE) [REF-64]. Imported modules may be similarly realigned if their default memory addresses conflict with other modules, in a process known as "rebasing" (for Windows) and "prelinking" (for Linux) [REF-1332] using randomly generated addresses. ASLR for libraries cannot be used in conjunction with prelink since it would require relocating the libraries at run-time, defeating the whole purpose of prelinking.
- For more information on these techniques see D3-SAOR (Segment Address Offset Randomization) from D3FEND [REF-1335].
Mitigation MIT-12
Operation
Strategy: Environment Hardening
- Use a CPU and operating system that offers Data Execution Protection (using hardware NX or XD bits) or the equivalent techniques that simulate this feature in software, such as PaX [REF-60] [REF-61]. These techniques ensure that any instruction executed is exclusively at a memory address that is part of the code segment.
- For more information on these techniques see D3-PSEP (Process Segment Execution Prevention) from D3FEND [REF-1336].
Mitigation MIT-13
Implementation
Replace unbounded copy functions with analogous functions that support length arguments, such as strcpy with strncpy. Create these if they are not available.
No CAPEC attack patterns related to this CWE.