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VulnerabilityPublished Updated

CVE-2025-71348: Picklescan is missing detection when calling pytorch function torch.utils._config_module.load_config

Affects picklescanSource GCVE Database

0

Not known to be exploited · CVSS 8.1 High : upgrade to 0.0.28 soon.

Data as of 26 Oct 2026, 17:16 UTC

CVSS 3.1 base score
8.1High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.5% chance of exploitation in 30 days – higher than 45% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.0.28
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 22 Aug 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

What to do

Upgrade picklescan to 0.0.28.

Affected software

ProductAffectedFixed in
PyPIpicklescanAffected
  • <0.0.28
Fixed in
  • 0.0.28

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

A patch is available for Picklescan to address this vulnerability. Users should upgrade to version 0.0.28 or later to ensure detection of malicious pickle files using torch.utils._config_module.load_config. Until patched, caution is advised when loading pickle files from untrusted sources, especially those related to PyTorch models.

Description

### Summary Using torch.utils._config_module.load_config function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker crafts the payload by calling the torch.utils._config_module.load_config function via the __reduce__ method. Then the victim checks whether the pickle file is safe using the Picklescan library. Because the library fails to detect any dangerous functions, the victim decides to pickle.load() this malicious file, leading to remote code execution. ### PoC ``` import pickle from torch.utils._config_module import ConfigModule class Evil: def __reduce__(self): return (os.system, ('whoami',)) class EvilTorchUtilsConfigModuleLoadConfig: def __reduce__(self): evil_payload = pickle.dumps(Evil()) return ConfigModule.load_config, (None, evil_payload) ``` ### 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

References

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