Common Weakness Enumeration
CWE-369
AllowedDivide By Zero
Abstraction: Base · Status: Draft
The product divides a value by zero.
579 vulnerabilities reference this CWE, most recent first.
GHSA-C52R-MJQP-7W2R
Vulnerability from github – Published: 2026-05-12 18:30 – Updated: 2026-05-15 21:31
VLAI
Details
Divide by zero for some Intel(R) QAT software drivers for Windows before version 1.13 within Ring 3: User Applications may allow a denial of service. Unprivileged software adversary with an authenticated user combined with a low complexity attack may enable denial of service. This result may potentially occur via local access when attack requirements are not present without special internal knowledge and requires no user interaction. The potential vulnerability may impact the confidentiality (none), integrity (none) and availability (high) of the vulnerable system, resulting in subsequent system confidentiality (none), integrity (none) and availability (none) impacts.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2026-20881"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-05-12T17:16:19Z",
"severity": "MODERATE"
},
"details": "Divide by zero for some Intel(R) QAT software drivers for Windows before version 1.13 within Ring 3: User Applications may allow a denial of service. Unprivileged software adversary with an authenticated user combined with a low complexity attack may enable denial of service. This result may potentially occur via local access when attack requirements are not present without special internal knowledge and requires no user interaction. The potential vulnerability may impact the confidentiality (none), integrity (none) and availability (high) of the vulnerable system, resulting in subsequent system confidentiality (none), integrity (none) and availability (none) impacts.",
"id": "GHSA-c52r-mjqp-7w2r",
"modified": "2026-05-15T21:31:31Z",
"published": "2026-05-12T18:30:39Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-20881"
},
{
"type": "WEB",
"url": "https://intel.com/content/www/us/en/security-center/advisory/intel-sa-01387.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"
},
{
"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/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
GHSA-C589-8XVR-7CM3
Vulnerability from github – Published: 2026-03-18 18:31 – Updated: 2026-05-29 21:31
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
fbdev: rivafb: fix divide error in nv3_arb()
A userspace program can trigger the RIVA NV3 arbitration code by calling the FBIOPUT_VSCREENINFO ioctl on /dev/fb*. When doing so, the driver recomputes FIFO arbitration parameters in nv3_arb(), using state->mclk_khz (derived from the PRAMDAC MCLK PLL) as a divisor without validating it first.
In a normal setup, state->mclk_khz is provided by the real hardware and is non-zero. However, an attacker can construct a malicious or misconfigured device (e.g. a crafted/emulated PCI device) that exposes a bogus PLL configuration, causing state->mclk_khz to become zero. Once nv3_get_param() calls nv3_arb(), the division by state->mclk_khz in the gns calculation causes a divide error and crashes the kernel.
Fix this by checking whether state->mclk_khz is zero and bailing out before doing the division.
The following log reveals it:
rivafb: setting virtual Y resolution to 2184 divide error: 0000 [#1] PREEMPT SMP KASAN PTI CPU: 0 PID: 2187 Comm: syz-executor.0 Not tainted 5.18.0-rc1+ #1 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS rel-1.12.0-59-gc9ba5276e321-prebuilt.qemu.org 04/01/2014 RIP: 0010:nv3_arb drivers/video/fbdev/riva/riva_hw.c:439 [inline] RIP: 0010:nv3_get_param+0x3ab/0x13b0 drivers/video/fbdev/riva/riva_hw.c:546 Call Trace: nv3CalcArbitration.constprop.0+0x255/0x460 drivers/video/fbdev/riva/riva_hw.c:603 nv3UpdateArbitrationSettings drivers/video/fbdev/riva/riva_hw.c:637 [inline] CalcStateExt+0x447/0x1b90 drivers/video/fbdev/riva/riva_hw.c:1246 riva_load_video_mode+0x8a9/0xea0 drivers/video/fbdev/riva/fbdev.c:779 rivafb_set_par+0xc0/0x5f0 drivers/video/fbdev/riva/fbdev.c:1196 fb_set_var+0x604/0xeb0 drivers/video/fbdev/core/fbmem.c:1033 do_fb_ioctl+0x234/0x670 drivers/video/fbdev/core/fbmem.c:1109 fb_ioctl+0xdd/0x130 drivers/video/fbdev/core/fbmem.c:1188 __x64_sys_ioctl+0x122/0x190 fs/ioctl.c:856
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2026-23266"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-03-18T18:16:25Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nfbdev: rivafb: fix divide error in nv3_arb()\n\nA userspace program can trigger the RIVA NV3 arbitration code by calling\nthe FBIOPUT_VSCREENINFO ioctl on /dev/fb*. When doing so, the driver\nrecomputes FIFO arbitration parameters in nv3_arb(), using state-\u003emclk_khz\n(derived from the PRAMDAC MCLK PLL) as a divisor without validating it\nfirst.\n\nIn a normal setup, state-\u003emclk_khz is provided by the real hardware and is\nnon-zero. However, an attacker can construct a malicious or misconfigured\ndevice (e.g. a crafted/emulated PCI device) that exposes a bogus PLL\nconfiguration, causing state-\u003emclk_khz to become zero. Once\nnv3_get_param() calls nv3_arb(), the division by state-\u003emclk_khz in the gns\ncalculation causes a divide error and crashes the kernel.\n\nFix this by checking whether state-\u003emclk_khz is zero and bailing out before\ndoing the division.\n\nThe following log reveals it:\n\nrivafb: setting virtual Y resolution to 2184\ndivide error: 0000 [#1] PREEMPT SMP KASAN PTI\nCPU: 0 PID: 2187 Comm: syz-executor.0 Not tainted 5.18.0-rc1+ #1\nHardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS rel-1.12.0-59-gc9ba5276e321-prebuilt.qemu.org 04/01/2014\nRIP: 0010:nv3_arb drivers/video/fbdev/riva/riva_hw.c:439 [inline]\nRIP: 0010:nv3_get_param+0x3ab/0x13b0 drivers/video/fbdev/riva/riva_hw.c:546\nCall Trace:\n nv3CalcArbitration.constprop.0+0x255/0x460 drivers/video/fbdev/riva/riva_hw.c:603\n nv3UpdateArbitrationSettings drivers/video/fbdev/riva/riva_hw.c:637 [inline]\n CalcStateExt+0x447/0x1b90 drivers/video/fbdev/riva/riva_hw.c:1246\n riva_load_video_mode+0x8a9/0xea0 drivers/video/fbdev/riva/fbdev.c:779\n rivafb_set_par+0xc0/0x5f0 drivers/video/fbdev/riva/fbdev.c:1196\n fb_set_var+0x604/0xeb0 drivers/video/fbdev/core/fbmem.c:1033\n do_fb_ioctl+0x234/0x670 drivers/video/fbdev/core/fbmem.c:1109\n fb_ioctl+0xdd/0x130 drivers/video/fbdev/core/fbmem.c:1188\n __x64_sys_ioctl+0x122/0x190 fs/ioctl.c:856",
"id": "GHSA-c589-8xvr-7cm3",
"modified": "2026-05-29T21:31:16Z",
"published": "2026-03-18T18:31:18Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-23266"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/0209e21e3c372fa2da04c39214bec0b64e4eb5f4"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/3e4cbd1d46c246dfa684c8e9d8c20ae0b960c50a"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/526460a96c5443e2fc0fd231edd1f9c49d2de26b"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/52916878db2b8e3769743a94484729f0844352df"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/73f0391e92d404da68f7484e57c106c5e673dc7e"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/78daf5984d96edec3b920c72a93bd6821b8710b7"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/9efa0dc46270a8723c158c64afbcf1dead72b28c"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/ec5a58f4fd581875593ea92a65485e1906a53c0f"
}
],
"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-C6CX-89C4-F7WJ
Vulnerability from github – Published: 2025-02-06 06:31 – Updated: 2025-02-06 18:31
VLAI
Details
A floating point exception (divide-by-zero) vulnerability was discovered in Bento4 1.6.0-641 in function AP4_TfraAtom() of Ap4TfraAtom.cpp which allows a remote attacker to cause a denial of service vulnerability.
Severity
6.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2024-57598"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-02-05T22:15:33Z",
"severity": "MODERATE"
},
"details": "A floating point exception (divide-by-zero) vulnerability was discovered in Bento4 1.6.0-641 in function AP4_TfraAtom() of Ap4TfraAtom.cpp which allows a remote attacker to cause a denial of service vulnerability.",
"id": "GHSA-c6cx-89c4-f7wj",
"modified": "2025-02-06T18:31:05Z",
"published": "2025-02-06T06:31:26Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-57598"
},
{
"type": "WEB",
"url": "https://github.com/JWH-96/Bugs-disclosure/blob/main/Bento4_FPE.md"
},
{
"type": "WEB",
"url": "http://bento4.com"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-C6PH-M8CW-RFQH
Vulnerability from github – Published: 2024-01-03 09:30 – Updated: 2024-11-22 18:15
VLAI
Summary
PaddlePaddle floating point exception in paddle.linalg.eig
Details
FPE in paddle.linalg.eig in PaddlePaddle before 2.6.0. This flaw can cause a runtime crash and a denial of service.
Severity
4.7 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "paddlepaddle"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2023-38677"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2024-01-03T21:51:56Z",
"nvd_published_at": "2024-01-03T09:15:08Z",
"severity": "MODERATE"
},
"details": "FPE in paddle.linalg.eig in PaddlePaddle before 2.6.0. This flaw can cause a runtime crash and a denial of service.\n\n\n\n",
"id": "GHSA-c6ph-m8cw-rfqh",
"modified": "2024-11-22T18:15:54Z",
"published": "2024-01-03T09:30:32Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-38677"
},
{
"type": "WEB",
"url": "https://github.com/PaddlePaddle/Paddle/commit/19da5c0c4d8c5e4dfef2a92e24141c3f51884dcc"
},
{
"type": "PACKAGE",
"url": "https://github.com/PaddlePaddle/Paddle"
},
{
"type": "WEB",
"url": "https://github.com/PaddlePaddle/Paddle/blob/develop/security/advisory/pdsa-2023-009.md"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/paddlepaddle/PYSEC-2024-132.yaml"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:N/I:N/A:L",
"type": "CVSS_V3"
}
],
"summary": "PaddlePaddle floating point exception in paddle.linalg.eig"
}
GHSA-C75X-6682-HRM4
Vulnerability from github – Published: 2022-05-14 01:43 – Updated: 2022-05-14 01:43
VLAI
Details
Amazon Web Services (AWS) FreeRTOS through 1.3.1, FreeRTOS up to V10.0.1 (with FreeRTOS+TCP), and WITTENSTEIN WHIS Connect middleware TCP/IP component allow division by zero in prvCheckOptions.
Severity
7.4 (High)
{
"affected": [],
"aliases": [
"CVE-2018-16523"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2018-12-06T23:29:00Z",
"severity": "HIGH"
},
"details": "Amazon Web Services (AWS) FreeRTOS through 1.3.1, FreeRTOS up to V10.0.1 (with FreeRTOS+TCP), and WITTENSTEIN WHIS Connect middleware TCP/IP component allow division by zero in prvCheckOptions.",
"id": "GHSA-c75x-6682-hrm4",
"modified": "2022-05-14T01:43:18Z",
"published": "2022-05-14T01:43:18Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2018-16523"
},
{
"type": "WEB",
"url": "https://blog.zimperium.com/freertos-tcpip-stack-vulnerabilities-details"
},
{
"type": "WEB",
"url": "https://blog.zimperium.com/freertos-tcpip-stack-vulnerabilities-put-wide-range-devices-risk-compromise-smart-homes-critical-infrastructure-systems"
},
{
"type": "WEB",
"url": "https://github.com/aws/amazon-freertos/blob/v1.3.2/CHANGELOG.md"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-C7X8-XX5V-964J
Vulnerability from github – Published: 2025-05-01 15:31 – Updated: 2025-11-04 18:31
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
jfs: add sanity check for agwidth in dbMount
The width in dmapctl of the AG is zero, it trigger a divide error when calculating the control page level in dbAllocAG.
To avoid this issue, add a check for agwidth in dbAllocAG.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2025-37740"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-05-01T13:15:52Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\njfs: add sanity check for agwidth in dbMount\n\nThe width in dmapctl of the AG is zero, it trigger a divide error when\ncalculating the control page level in dbAllocAG.\n\nTo avoid this issue, add a check for agwidth in dbAllocAG.",
"id": "GHSA-c7x8-xx5v-964j",
"modified": "2025-11-04T18:31:32Z",
"published": "2025-05-01T15:31:42Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-37740"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/722e72f7f9c69fcb3ab7988c2471feff7a4c8de1"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/a065cec230aa807c18828a3eee82f1c8592c2adf"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/a260bf14cd347878f01f70739ba829442a474a16"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/a741f29ac8b6374c9904be8b7ac7cdfcd7e7e4fa"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/c8c96a9e7660e5e5eea445978fe8f2e432d91c1f"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/cc0bc4cb62ce5fa0c383e3bf0765d01f46bd49ac"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/ccd97c8a4f90810f228ee40d1055148fa146dd57"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/ddf2846f22e8575d6b4b6a66f2100f168b8cd73d"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/e3f85edb03183fb06539e5b50dd2c4bb42b869f0"
},
{
"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-C968-PQ7H-7FXV
Vulnerability from github – Published: 2021-05-21 14:21 – Updated: 2024-10-30 23:10
VLAI
Summary
Division by 0 in `Conv3DBackprop*`
Details
Impact
The tf.raw_ops.Conv3DBackprop* operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0:
import tensorflow as tf
input_sizes = tf.constant([0, 0, 0, 0, 0], shape=[5], dtype=tf.int32)
filter_tensor = tf.constant([], shape=[0, 0, 0, 1, 0], dtype=tf.float32)
out_backprop = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
import tensorflow as tf
input_sizes = tf.constant([1], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
filter_tensor = tf.constant([0, 0, 0, 1, 0], shape=[5], dtype=tf.int32)
out_backprop = tf.constant([], shape=[1, 1, 1, 1, 0], dtype=tf.float32)
tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
This is because the implementation does not check that the divisor used in computing the shard size is not zero:
const int64 size_A = output_image_size * dims.out_depth;
const int64 size_B = filter_total_size * dims.out_depth;
const int64 size_C = output_image_size * filter_total_size;
const int64 work_unit_size = size_A + size_B + size_C;
...
const size_t shard_size =
use_parallel_contraction
? 1
: (target_working_set_size + work_unit_size - 1) / work_unit_size;
Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error.
Patches
We have patched the issue in GitHub commit 311403edbc9816df80274bd1ea8b3c0c0f22c3fa.
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-2021-29522"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T23:22:10Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nThe `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0:\n\n```python\nimport tensorflow as tf\n\ninput_sizes = tf.constant([0, 0, 0, 0, 0], shape=[5], dtype=tf.int32)\nfilter_tensor = tf.constant([], shape=[0, 0, 0, 1, 0], dtype=tf.float32)\nout_backprop = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)\n \ntf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding=\u0027SAME\u0027, data_format=\u0027NDHWC\u0027, dilations=[1, 1, 1, 1, 1])\n```\n```python\nimport tensorflow as tf\n\ninput_sizes = tf.constant([1], shape=[1, 1, 1, 1, 1], dtype=tf.float32)\nfilter_tensor = tf.constant([0, 0, 0, 1, 0], shape=[5], dtype=tf.int32)\nout_backprop = tf.constant([], shape=[1, 1, 1, 1, 0], dtype=tf.float32)\n\ntf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding=\u0027SAME\u0027, data_format=\u0027NDHWC\u0027, dilations=[1, 1, 1, 1, 1])\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero:\n\n```cc\n const int64 size_A = output_image_size * dims.out_depth;\n const int64 size_B = filter_total_size * dims.out_depth;\n const int64 size_C = output_image_size * filter_total_size;\n const int64 work_unit_size = size_A + size_B + size_C;\n ...\n const size_t shard_size =\n use_parallel_contraction\n ? 1\n : (target_working_set_size + work_unit_size - 1) / work_unit_size;\n```\n\nThus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error.\n\n### Patches\nWe have patched the issue in GitHub commit [311403edbc9816df80274bd1ea8b3c0c0f22c3fa](https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa).\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-c968-pq7h-7fxv",
"modified": "2024-10-30T23:10:55Z",
"published": "2021-05-21T14:21:39Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29522"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-450.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-648.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-159.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Division by 0 in `Conv3DBackprop*`"
}
GHSA-CC62-FFCM-XV9H
Vulnerability from github – Published: 2022-05-14 01:15 – Updated: 2022-05-14 01:15
VLAI
Details
An issue was discovered in Xpdf 4.01.01. There is an FPE in the function Splash::scaleImageYuXu at Splash.cc for x Bresenham parameters.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2019-10020"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2019-03-25T00:29:00Z",
"severity": "MODERATE"
},
"details": "An issue was discovered in Xpdf 4.01.01. There is an FPE in the function Splash::scaleImageYuXu at Splash.cc for x Bresenham parameters.",
"id": "GHSA-cc62-ffcm-xv9h",
"modified": "2022-05-14T01:15:48Z",
"published": "2022-05-14T01:15:48Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2019-10020"
},
{
"type": "WEB",
"url": "https://forum.xpdfreader.com/viewtopic.php?f=3\u0026t=41274"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-CF72-RGJQ-HH5R
Vulnerability from github – Published: 2022-05-02 03:29 – Updated: 2022-05-02 03:29
VLAI
Details
agent/snmp_agent.c in snmpd in net-snmp 5.0.9 in Red Hat Enterprise Linux (RHEL) 3 allows remote attackers to cause a denial of service (daemon crash) via a crafted SNMP GETBULK request that triggers a divide-by-zero error. NOTE: this vulnerability exists because of an incorrect fix for CVE-2008-4309.
{
"affected": [],
"aliases": [
"CVE-2009-1887"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2009-06-26T18:30:00Z",
"severity": "MODERATE"
},
"details": "agent/snmp_agent.c in snmpd in net-snmp 5.0.9 in Red Hat Enterprise Linux (RHEL) 3 allows remote attackers to cause a denial of service (daemon crash) via a crafted SNMP GETBULK request that triggers a divide-by-zero error. NOTE: this vulnerability exists because of an incorrect fix for CVE-2008-4309.",
"id": "GHSA-cf72-rgjq-hh5r",
"modified": "2022-05-02T03:29:36Z",
"published": "2022-05-02T03:29:36Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2009-1887"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=506903"
},
{
"type": "WEB",
"url": "https://oval.cisecurity.org/repository/search/definition/oval%3Aorg.mitre.oval%3Adef%3A8426"
},
{
"type": "WEB",
"url": "https://oval.cisecurity.org/repository/search/definition/oval%3Aorg.mitre.oval%3Adef%3A9716"
},
{
"type": "WEB",
"url": "http://www.mandriva.com/security/advisories?name=MDVSA-2009:156"
},
{
"type": "WEB",
"url": "http://www.redhat.com/support/errata/RHSA-2009-1124.html"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-CFPJ-3Q4C-JHVR
Vulnerability from github – Published: 2021-08-25 14:40 – Updated: 2024-11-13 21:15
VLAI
Summary
Division by zero in TFLite
Details
Impact
The implementation of fully connected layers in TFLite is vulnerable to a division by zero error:
const int batch_size = input_size / filter->dims->data[1];
An attacker can craft a model such that filter->dims->data[1] is 0.
Patches
We have patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f.
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. Concurrently, it has also been reported by Yakun Zhang of Baidu Security.
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-2021-37680"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-08-24T16:24:24Z",
"nvd_published_at": "2021-08-12T22:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nThe implementation of fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226):\n\n```cc\nconst int batch_size = input_size / filter-\u003edims-\u003edata[1];\n```\n\nAn attacker can craft a model such that `filter-\u003edims-\u003edata[1]` is 0.\n\n### Patches\nWe have patched the issue in GitHub commit [718721986aa137691ee23f03638867151f74935f](https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f).\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. Concurrently, it has also been reported by Yakun Zhang of Baidu Security.",
"id": "GHSA-cfpj-3q4c-jhvr",
"modified": "2024-11-13T21:15:32Z",
"published": "2021-08-25T14:40:38Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfpj-3q4c-jhvr"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-37680"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-593.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-791.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-302.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226"
}
],
"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": "Division by zero in TFLite"
}
No mitigation information available for this CWE.
No CAPEC attack patterns related to this CWE.