CosyVoice up to 2025-30-21 Pickle torch.load deserialization
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 5.5 | $0-$5k | 0.00 |
Summary
A vulnerability was found in CosyVoice up to 2025-30-21. It has been classified as critical. Impacted is the function torch.load of the component Pickle Module. The manipulation leads to deserialization.
This vulnerability is referenced as CVE-2026-31252. No exploit is available.
Details
A vulnerability, which was classified as critical, has been found in CosyVoice up to 2025-30-21. This issue affects the function torch.load of the component Pickle Module. The manipulation with an unknown input leads to a deserialization vulnerability. Using CWE to declare the problem leads to CWE-502. The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. Impacted is confidentiality, integrity, and availability. The summary by CVE is:
CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its model loading component. The framework uses torch.load() to load model weight files (e.g., llm.pt, flow.pt, hift.pt) without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a malicious model directory containing specially crafted model files. When a victim starts the CosyVoice Web UI pointing to this directory, arbitrary code is executed on the victim's system during the model loading process.
It is possible to read the advisory at github.com. The identification of this vulnerability is CVE-2026-31252 since 03/09/2026. Technical details of the vulnerability are known, but there is no available exploit. The pricing for an exploit might be around USD $0-$5k at the moment (estimation calculated on 05/11/2026).
There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Name
Version
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 5.5VulDB Meta Temp Score: 5.5
VulDB Base Score: 5.5
VulDB Temp Score: 5.5
VulDB Vector: 🔒
VulDB Reliability: 🔍
CVSSv2
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: DeserializationCWE: CWE-502 / CWE-20
CAPEC: 🔒
ATT&CK: 🔒
Physical: No
Local: No
Remote: Partially
Availability: 🔒
Status: Not defined
EPSS Score: 🔒
EPSS Percentile: 🔒
Price Prediction: 🔍
Current Price Estimation: 🔒
| 0-Day | Unlock | Unlock | Unlock | Unlock |
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: no mitigation knownStatus: 🔍
0-Day Time: 🔒
Timeline
03/09/2026 CVE reserved05/11/2026 Advisory disclosed
05/11/2026 VulDB entry created
05/11/2026 VulDB entry last update
Sources
Advisory: github.comStatus: Confirmed
CVE: CVE-2026-31252 (🔒)
GCVE (CVE): GCVE-0-2026-31252
GCVE (VulDB): GCVE-100-362675
Entry
Created: 05/11/2026 20:19Changes: 05/11/2026 20:19 (52)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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