Deserialization of Untrusted DataWeakness · CWE-502

CVE-2025-71356

HIGH · 8.1 CVSS v3.1 Published 2026-07-04
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
88/100
Remediation priority · High
Remotely reachable No privileges 7 weeks old

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
picklescan before 0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls in pickle files. Attackers can embed undetected code in pickle files that executes remote code when loaded by victims.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

Picklescan before v0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls embedded in pickle files, allowing attackers to embed remote code execution payloads that bypass the scanner undetected.

MitigationUpgrade picklescan to version 0.0.28 or later which includes detection for this attack vector; avoid loading untrusted pickle files from unknown sources.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
None
User interaction
Required
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
None

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

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Check if picklescan is installed
    Run 'pip show picklescan' or 'pip list | grep picklescan' to see if the package is installed
    Affected if picklescan is not installed but you rely on it for scanning pickle files
  2. Determine installed picklescan version
    Run 'pip show picklescan' and note the Version field, or run 'picklescan --version' if available
    Affected if The version is lower than v0.0.28 or cannot be determined (likely vulnerable)
  3. Identify pickle files from untrusted sources
    Review your codebase or workflow for instances where pickle.load() or similar deserialization functions process files from unknown or untrusted sources
    Affected if You load pickle files from untrusted sources and use a picklescan version before v0.0.28 to scan them
  4. Check for PyTorch usage with symbolic shapes
    Inspect whether your environment includes PyTorch with torch.fx.experimental.symbolic_shapes module by running 'python -c "import torch.fx.experimental.symbolic_shapes"'
    Affected if PyTorch with symbolic_shapes is present and you process pickle files from untrusted sources

You are affected if picklescan is installed with a version before v0.0.28 and you scan pickle files from untrusted sources, particularly those that may contain PyTorch-related payloads.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

dbcve · scoped
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Upgrade picklescan to version 0.0.28 or later which includes detection for this attack vector; avoid loading untrusted pickle files from unknown sources.

Recommended fix High confidence

picklescan 0.0.28

  1. 1. Check the currently installed version of picklescan using: pip show picklescan or pip list | grep picklescan
  2. 2. Upgrade picklescan to version 0.0.28 or later using: pip install picklescan>=0.0.28
  3. 3. Verify the upgrade was successful by running: pip show picklescan and confirming the version number

Generated from the published advisory — verify against the referenced sources before acting.

Have this fixed Scoped from the published advisory
  • Consultation3.0 h
  • Implementation6.0 h
  • Testing3.0 h
  • Review / QA2.0 h
14.0 hours of engineering $2,490
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Scan for this in your stack

Free · runs locally
dbcve dependency scanner

Check whether your project pulls in CVE-2025-71356 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.

References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2025-71356 in production — separate from our analysis above.

No notes yet

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
  • No weaponised exploit code, or anything meant to cause harm
  • No spam, self-promotion, credentials, or personal data