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Picklescan missing detection when calling pytorch function torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expressionGHSA-f4x7-rfwp-v3xw

Fix: mmaitre314/picklescan#47

GHSA-f4x7-rfwp-v3xw is a Deserialization of Untrusted Data vulnerability in picklescan. A fix is available for picklescan — see the affected versions and patch details below.

Also known asCVE-2025-71356
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Nov 3, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

Proof-of-concept exploit code exists

  • CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
  • A successful exploit gives an attacker total control of the affected component, not partial access.

Exploitation and automatability from CISA’s SSVC triage for GHSA-f4x7-rfwp-v3xw.

EPSS Exploitation Probability

via FIRST.org ↗
0.4%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs32th percentile — riskier than 32% of all scored CVEsHighest risk
0.00%0.31%0.62%0.93%0.3%0.4%0.4%0.4%0.4%Aug 26Oct 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

Real-World Exposure

1 pkg affected
🐍picklescan

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Summary

Using torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function, which is a pytorch library function to execute remote pickle file.

Details

The attack payload executes in the following steps:

First, the attacker craft the payload by calling to torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution.

PoC

import types
import torch.fx.experimental.symbolic_shapes as symbolic_shapes

class EvilTorchFxSymbolicShapesEvaluateGuardsExpression:
    def __reduce__(self):
        fake_self = str
        code = "__import__('os').system('whoami')"
        args = []
        return symbolic_shapes.ShapeEnv.evaluate_guards_expression, (fake_self, code, args)

Impact

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects.

Corresponding

https://github.com/FredericDT https://github.com/Qhaoduoyu

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIpicklescanall versions0.0.28pip install --upgrade 'picklescan==0.0.28'

Detection & mitigation playbook

Open-source dependency

  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for picklescan, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update picklescan to 0.0.28 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-f4x7-rfwp-v3xw is resolved across your whole dependency graph.

  3. Workarounds

    Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.

Frequently Asked Questions

### Summary Using torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus
O3 Security · Impact-Aware SCA

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