Deserialization of Untrusted DataWeakness · CWE-502

CVE-2026-31249

HIGH · 7.3 CVSS v3.1 Published 2026-05-11
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
82/100
Remediation priority · High
Remotely reachable No privileges Zero-click

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
CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its make_parquet_list.py data processing tool. The script loads PyTorch .pt files (utterance embeddings, speaker embeddings, speech tokens) using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing malicious .pt files within a data directory. When a victim processes this directory using the tool, arbitrary code is executed on the victim's system.

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

CosyVoice's make_parquet_list.py data processing tool loads PyTorch .pt files (utterance embeddings, speaker embeddings, speech tokens) using torch.load() without the weights_only=True security parameter. Since torch.load() relies on pickle for deserialization, this allows attackers to embed malicious Python objects in .pt files that execute arbitrary code when processed by the victim.

MitigationAdd weights_only=True to all torch.load() calls in make_parquet_list.py, or migrate to safer loading methods that don't permit arbitrary object deserialization.

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
None
Scope
Unchanged
Confidentiality
Low
Integrity
Low
Availability
Low

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

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. Identify CosyVoice installation
    Search for CosyVoice in your Python environment using 'pip show cosyvoice' or check for the 'cosyvoice' package directory
    Affected if CosyVoice is installed and contains the make_parquet_list.py tool
  2. Locate make_parquet_list.py
    Find the file make_parquet_list.py within the CosyVoice package, typically in the cosyvoice/utils or cosyvoice directory
    Affected if The file exists in your CosyVoice installation
  3. Inspect torch.load() calls for weights_only parameter
    Open make_parsearch.py and grep for 'torch.load(' to find all calls, then verify each call includes weights_only=True
    Affected if Any torch.load() call is found without the weights_only=True parameter
  4. Verify .pt file processing
    Check if the tool loads .pt files (utterance embeddings, speaker embeddings, speech tokens) from external or untrusted sources
    Affected if The tool processes .pt files without validation, particularly from external inputs

Your environment is affected if make_parquet_list.py contains torch.load() calls without the weights_only=True parameter and processes external .pt files.

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.

From vendor data
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Add weights_only=True to all torch.load() calls in make_parquet_list.py, or migrate to safer loading methods that don't permit arbitrary object deserialization.

Have this fixed Scoped from the published advisory
  • Consultation2.0 h
  • Implementation1.0 h
  • Testing2.0 h
  • Review / QA1.0 h
6.0 hours of engineering $1,060
Get help mitigating

An estimate, not a bill — we confirm scope with you before any work starts. Need it this week? Rush from $1,696.

Scan for this in your stack

Free · runs locally
dbcve dependency scanner

Check whether your project pulls in CVE-2026-31249 — 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-2026-31249 in production — separate from our analysis above.

No notes yet

Be the first to add a field note for this CVE — a mitigation you’ve verified, a version caveat, or a link to a working fix. Sign in above to contribute.

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