Common Weakness Enumeration
CWE-681
AllowedIncorrect Conversion between Numeric Types
Abstraction: Base · Status: Draft
When converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. If the resulting values are used in a sensitive context, then dangerous behaviors may occur.
131 vulnerabilities reference this CWE, most recent first.
GHSA-F3XX-69MR-6RX6
Vulnerability from github – Published: 2026-08-11 00:31 – Updated: 2026-08-11 00:31
VLAI
Details
A type mismatch vulnerability was found in QEMU's vhost inflight migration VMState handling. The destination buffer size is stored as a uint64_t but read by the VMS_VBUFFER load path as a signed int32_t. On little-endian hosts, a crafted incoming migration state with bit 31 set causes the value to be interpreted as negative and then implicitly converted to a very large size_t, leading qemu_get_buffer() to copy migration-stream data beyond the bounds of the mmap-backed inflight region.
This can result in a crash of the QEMU process or memory corruption. Exploitation requires control of the migration producer or write access to the migration channel, combined with a destination configured to use vhost inflight migration.
Severity
4.4 (Medium)
{
"affected": [],
"aliases": [
"CVE-2026-6426"
],
"database_specific": {
"cwe_ids": [
"CWE-681"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-08-10T22:17:10Z",
"severity": "MODERATE"
},
"details": "A type mismatch vulnerability was found in QEMU\u0027s vhost inflight migration VMState handling. The destination buffer size is stored as a uint64_t but read by the VMS_VBUFFER load path as a signed int32_t. On little-endian hosts, a crafted incoming migration state with bit 31 set causes the value to be interpreted as negative and then implicitly converted to a very large size_t, leading qemu_get_buffer() to copy migration-stream data beyond the bounds of the mmap-backed inflight region.\n\nThis can result in a crash of the QEMU process or memory corruption. Exploitation requires control of the migration producer or write access to the migration channel, combined with a destination configured to use vhost inflight migration.",
"id": "GHSA-f3xx-69mr-6rx6",
"modified": "2026-08-11T00:31:11Z",
"published": "2026-08-11T00:31:11Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-6426"
},
{
"type": "WEB",
"url": "https://access.redhat.com/security/cve/CVE-2026-6426"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2513498"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:H/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-FRQF-HCMW-8JJF
Vulnerability from github – Published: 2022-05-24 17:33 – Updated: 2025-10-22 00:32
VLAI
Details
Windows Kernel Local Elevation of Privilege Vulnerability
Severity
7.8 (High)
{
"affected": [],
"aliases": [
"CVE-2020-17087"
],
"database_specific": {
"cwe_ids": [
"CWE-131",
"CWE-269",
"CWE-681"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2020-11-11T07:15:00Z",
"severity": "HIGH"
},
"details": "Windows Kernel Local Elevation of Privilege Vulnerability",
"id": "GHSA-frqf-hcmw-8jjf",
"modified": "2025-10-22T00:32:00Z",
"published": "2022-05-24T17:33:52Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-17087"
},
{
"type": "WEB",
"url": "https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-17087"
},
{
"type": "WEB",
"url": "https://www.cisa.gov/known-exploited-vulnerabilities-catalog?field_cve=CVE-2020-17087"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-G4H2-GQM3-C9WQ
Vulnerability from github – Published: 2021-05-21 14:23 – Updated: 2024-10-30 23:27
VLAI
Summary
Segfault in tf.raw_ops.ImmutableConst
Details
Impact
Calling tf.raw_ops.ImmutableConst with a dtype of tf.resource or tf.variant results in a segfault in the implementation as code assumes that the tensor contents are pure scalars.
>>> import tensorflow as tf
>>> tf.raw_ops.ImmutableConst(dtype=tf.resource, shape=[], memory_region_name="/tmp/test.txt")
...
Segmentation fault
Patches
We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Workarounds
If using tf.raw_ops.ImmutableConst in code, you can prevent the segfault by inserting a filter for the dtype argument.
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
{
"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"
},
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"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"
}
],
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}
]
},
{
"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-2021-29539"
],
"database_specific": {
"cwe_ids": [
"CWE-681"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T22:09:59Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nCalling [`tf.raw_ops.ImmutableConst`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars.\n\n```python\n\u003e\u003e\u003e import tensorflow as tf\n\u003e\u003e\u003e tf.raw_ops.ImmutableConst(dtype=tf.resource, shape=[], memory_region_name=\"/tmp/test.txt\")\n...\nSegmentation fault\n```\n\n### Patches\nWe have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.\n\n### Workarounds\nIf using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument.\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-g4h2-gqm3-c9wq",
"modified": "2024-10-30T23:27:31Z",
"published": "2021-05-21T14:23:05Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g4h2-gqm3-c9wq"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29539"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/4f663d4b8f0bec1b48da6fa091a7d29609980fa4"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-467.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-665.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-176.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": "Segfault in tf.raw_ops.ImmutableConst"
}
GHSA-G8WG-CJWC-XHHP
Vulnerability from github – Published: 2021-08-25 14:41 – Updated: 2024-11-13 21:15
VLAI
Summary
Heap OOB in nested `tf.map_fn` with `RaggedTensor`s
Details
Impact
It is possible to nest a tf.map_fn within another tf.map_fn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap:
import tensorflow as tf
x = tf.ragged.constant([[1,2,3], [4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
z = tf.ragged.constant([[[1,2,3],[1,2,3],[1,2,3]],[[4,5],[4,5]],[[6]]])
The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information.
The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions in the above example.
The same implementation can result in data loss, if input tensor is tweaked:
import tensorflow as tf
x = tf.ragged.constant([[1,2], [3,4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
Here, the output tensor will only have 2 elements for each inner dimension.
Patches
We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 Haris Sahovic.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.3.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.5.0"
},
{
"fixed": "2.5.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.5.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.3.4"
}
],
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}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.5.0"
},
{
"fixed": "2.5.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.5.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.3.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.5.0"
},
{
"fixed": "2.5.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.5.0"
]
}
],
"aliases": [
"CVE-2021-37679"
],
"database_specific": {
"cwe_ids": [
"CWE-125",
"CWE-681"
],
"github_reviewed": true,
"github_reviewed_at": "2021-08-24T16:17:59Z",
"nvd_published_at": "2021-08-12T23:15:00Z",
"severity": "HIGH"
},
"details": "### Impact\nIt is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap:\n\n```python\nimport tensorflow as tf\nx = tf.ragged.constant([[1,2,3], [4,5], [6]])\nt = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)\nz = tf.ragged.constant([[[1,2,3],[1,2,3],[1,2,3]],[[4,5],[4,5]],[[6]]])\n```\n \nThe `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information.\n\nThe bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions in the above example.\n\nThe same implementation can result in data loss, if input tensor is tweaked:\n\n```python\nimport tensorflow as tf\nx = tf.ragged.constant([[1,2], [3,4,5], [6]])\nt = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x) \n```\n\nHere, the output tensor will only have 2 elements for each inner dimension.\n\n### Patches\nWe have patched the issue in GitHub commit [4e2565483d0ffcadc719bd44893fb7f609bb5f12](https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12).\n\nThe fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 Haris Sahovic.",
"id": "GHSA-g8wg-cjwc-xhhp",
"modified": "2024-11-13T21:15:11Z",
"published": "2021-08-25T14:41:00Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-37679"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-592.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-790.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-301.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Heap OOB in nested `tf.map_fn` with `RaggedTensor`s"
}
GHSA-G9CG-CHVX-P8M5
Vulnerability from github – Published: 2025-08-12 18:31 – Updated: 2025-08-12 18:31
VLAI
Details
Incorrect conversion between numeric types in Microsoft Office Word allows an unauthorized attacker to execute code locally.
Severity
8.4 (High)
{
"affected": [],
"aliases": [
"CVE-2025-53733"
],
"database_specific": {
"cwe_ids": [
"CWE-681"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-08-12T18:15:43Z",
"severity": "HIGH"
},
"details": "Incorrect conversion between numeric types in Microsoft Office Word allows an unauthorized attacker to execute code locally.",
"id": "GHSA-g9cg-chvx-p8m5",
"modified": "2025-08-12T18:31:32Z",
"published": "2025-08-12T18:31:32Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-53733"
},
{
"type": "WEB",
"url": "https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-53733"
}
],
"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"
}
]
}
GHSA-GF47-3HHJ-GV96
Vulnerability from github – Published: 2022-05-24 19:04 – Updated: 2025-10-22 00:32
VLAI
Details
Windows MSHTML Platform Remote Code Execution Vulnerability
Severity
8.8 (High)
{
"affected": [],
"aliases": [
"CVE-2021-33742"
],
"database_specific": {
"cwe_ids": [
"CWE-119",
"CWE-681",
"CWE-787"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-06-08T23:15:00Z",
"severity": "HIGH"
},
"details": "Windows MSHTML Platform Remote Code Execution Vulnerability",
"id": "GHSA-gf47-3hhj-gv96",
"modified": "2025-10-22T00:32:13Z",
"published": "2022-05-24T19:04:45Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-33742"
},
{
"type": "WEB",
"url": "https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2021-33742"
},
{
"type": "WEB",
"url": "https://www.cisa.gov/known-exploited-vulnerabilities-catalog?field_cve=CVE-2021-33742"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-GF88-J2MG-CC82
Vulnerability from github – Published: 2021-08-25 14:42 – Updated: 2024-11-13 20:54
VLAI
Summary
Crash caused by integer conversion to unsigned
Details
Impact
An attacker can cause a denial of service in boosted_trees_create_quantile_stream_resource by using negative arguments:
import tensorflow as tf
from tensorflow.python.ops import gen_boosted_trees_ops
import numpy as np
v= tf.Variable([0.0, 0.0, 0.0, 0.0, 0.0])
gen_boosted_trees_ops.boosted_trees_create_quantile_stream_resource(
quantile_stream_resource_handle = v.handle,
epsilon = [74.82224],
num_streams = [-49],
max_elements = np.int32(586))
The implementation does not validate that num_streams only contains non-negative numbers. In turn, this results in using this value to allocate memory:
class BoostedTreesQuantileStreamResource : public ResourceBase {
public:
BoostedTreesQuantileStreamResource(const float epsilon,
const int64 max_elements,
const int64 num_streams)
: are_buckets_ready_(false),
epsilon_(epsilon),
num_streams_(num_streams),
max_elements_(max_elements) {
streams_.reserve(num_streams_);
...
}
}
However, reserve receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library.
Patches
We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.
Severity
5.5 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.3.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.5.0"
},
{
"fixed": "2.5.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.5.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.3.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.5.0"
},
{
"fixed": "2.5.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.5.0"
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.3.4"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.5.0"
},
{
"fixed": "2.5.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.5.0"
]
}
],
"aliases": [
"CVE-2021-37661"
],
"database_specific": {
"cwe_ids": [
"CWE-681"
],
"github_reviewed": true,
"github_reviewed_at": "2021-08-24T13:23:52Z",
"nvd_published_at": "2021-08-12T21:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nAn attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments:\n\n```python\nimport tensorflow as tf\nfrom tensorflow.python.ops import gen_boosted_trees_ops\nimport numpy as np\n\nv= tf.Variable([0.0, 0.0, 0.0, 0.0, 0.0])\ngen_boosted_trees_ops.boosted_trees_create_quantile_stream_resource(\n quantile_stream_resource_handle = v.handle,\n epsilon = [74.82224],\n num_streams = [-49], \n max_elements = np.int32(586))\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40):\n\n```cc\nclass BoostedTreesQuantileStreamResource : public ResourceBase {\n public:\n BoostedTreesQuantileStreamResource(const float epsilon,\n const int64 max_elements,\n const int64 num_streams)\n : are_buckets_ready_(false),\n epsilon_(epsilon),\n num_streams_(num_streams),\n max_elements_(max_elements) {\n streams_.reserve(num_streams_);\n ...\n }\n}\n```\n\nHowever, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library.\n\n### Patches\nWe have patched the issue in GitHub commit [8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992](https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992).\n\nThe fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.",
"id": "GHSA-gf88-j2mg-cc82",
"modified": "2024-11-13T20:54:40Z",
"published": "2021-08-25T14:42:28Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf88-j2mg-cc82"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-37661"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-574.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-772.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-283.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"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"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Crash caused by integer conversion to unsigned"
}
GHSA-GHRH-GXWW-GQFR
Vulnerability from github – Published: 2022-05-24 19:11 – Updated: 2022-05-24 19:11
VLAI
Details
An exploitable integer truncation vulnerability exists within the MPEG-4 decoding functionality of the GPAC Project on Advanced Content library v1.0.1. When processing the 'hdlr' FOURCC code, a specially crafted MPEG-4 input can cause an improper memory allocation resulting in a heap-based buffer overflow that causes memory corruption. An attacker can convince a user to open a video to trigger this vulnerability.
Severity
8.8 (High)
{
"affected": [],
"aliases": [
"CVE-2021-21861"
],
"database_specific": {
"cwe_ids": [
"CWE-681",
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-08-16T20:15:00Z",
"severity": "HIGH"
},
"details": "An exploitable integer truncation vulnerability exists within the MPEG-4 decoding functionality of the GPAC Project on Advanced Content library v1.0.1. When processing the \u0027hdlr\u0027 FOURCC code, a specially crafted MPEG-4 input can cause an improper memory allocation resulting in a heap-based buffer overflow that causes memory corruption. An attacker can convince a user to open a video to trigger this vulnerability.",
"id": "GHSA-ghrh-gxww-gqfr",
"modified": "2022-05-24T19:11:10Z",
"published": "2022-05-24T19:11:10Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-21861"
},
{
"type": "WEB",
"url": "https://talosintelligence.com/vulnerability_reports/TALOS-2021-1298"
},
{
"type": "WEB",
"url": "https://www.debian.org/security/2021/dsa-4966"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-GMPM-XP43-F7G6
Vulnerability from github – Published: 2022-05-24 17:27 – Updated: 2022-06-24 01:23
VLAI
Summary
Signed to Unsigned Conversion Error in Facebook Hermes
Details
An Integer signedness error in the JavaScript Interpreter in Facebook Hermes prior to commit 2c7af7ec481ceffd0d14ce2d7c045e475fd71dc6 allows attackers to cause a denial of service attack or a potential RCE via crafted JavaScript. Note that this is only exploitable if the application using Hermes permits evaluation of untrusted JavaScript. Hence, most React Native applications are not affected.
Severity
8.1 (High)
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 0.4.3"
},
"package": {
"ecosystem": "npm",
"name": "hermes-engine"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.5.2"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2020-1913"
],
"database_specific": {
"cwe_ids": [
"CWE-195",
"CWE-681"
],
"github_reviewed": true,
"github_reviewed_at": "2022-06-24T01:23:39Z",
"nvd_published_at": "2020-09-09T19:15:00Z",
"severity": "HIGH"
},
"details": "An Integer signedness error in the JavaScript Interpreter in Facebook Hermes prior to commit 2c7af7ec481ceffd0d14ce2d7c045e475fd71dc6 allows attackers to cause a denial of service attack or a potential RCE via crafted JavaScript. Note that this is only exploitable if the application using Hermes permits evaluation of untrusted JavaScript. Hence, most React Native applications are not affected.",
"id": "GHSA-gmpm-xp43-f7g6",
"modified": "2022-06-24T01:23:39Z",
"published": "2022-05-24T17:27:39Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-1913"
},
{
"type": "WEB",
"url": "https://github.com/facebook/hermes/commit/2c7af7ec481ceffd0d14ce2d7c045e475fd71dc6"
},
{
"type": "WEB",
"url": "https://www.facebook.com/security/advisories/cve-2020-1913"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "Signed to Unsigned Conversion Error in Facebook Hermes"
}
GHSA-H5QR-R3M8-3G2V
Vulnerability from github – Published: 2024-12-12 03:33 – Updated: 2024-12-12 03:33
VLAI
Details
Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability
Severity
8.8 (High)
{
"affected": [],
"aliases": [
"CVE-2024-49093"
],
"database_specific": {
"cwe_ids": [
"CWE-681"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-12-12T02:04:34Z",
"severity": "HIGH"
},
"details": "Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability",
"id": "GHSA-h5qr-r3m8-3g2v",
"modified": "2024-12-12T03:33:05Z",
"published": "2024-12-12T03:33:05Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-49093"
},
{
"type": "WEB",
"url": "https://msrc.microsoft.com/update-guide/vulnerability/CVE-2024-49093"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
Mitigation
Implementation
Avoid making conversion between numeric types. Always check for the allowed ranges.
No CAPEC attack patterns related to this CWE.