InternLM LMDeploy up to 0.11.0 torch.load deserialization

CVSS Meta Temp Score
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CTI Interest Score
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7.4$0-$5k0.00

Summaryinfo

A vulnerability marked as critical has been reported in InternLM LMDeploy up to 0.11.0. Impacted is the function torch.load. Performing a manipulation results in deserialization. This vulnerability is known as CVE-2025-67729. Remote exploitation of the attack is possible. No exploit is available. It is suggested to upgrade the affected component.

Detailsinfo

A vulnerability, which was classified as critical, was found in InternLM LMDeploy up to 0.11.0. This affects the function torch.load. The manipulation with an unknown input leads to a deserialization vulnerability. CWE is classifying the issue as CWE-502. The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. This is going to have an impact on confidentiality, integrity, and availability. The summary by CVE is:

LMDeploy is a toolkit for compressing, deploying, and serving LLMs. Prior to version 0.11.1, an insecure deserialization vulnerability exists in lmdeploy where torch.load() is called without the weights_only=True parameter when loading model checkpoint files. This allows an attacker to execute arbitrary code on the victim's machine when they load a malicious .bin or .pt model file. This issue has been patched in version 0.11.1.

It is possible to read the advisory at github.com. This vulnerability is uniquely identified as CVE-2025-67729. The exploitability is told to be easy. It is possible to initiate the attack remotely. No form of authentication is needed for exploitation. It demands that the victim is doing some kind of user interaction. Technical details of the vulnerability are known, but there is no available exploit.

Upgrading to version 0.11.1 eliminates this vulnerability.

The vulnerability is also documented in the vulnerability database at EUVD (EUVD-2025-205455). Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Productinfo

Vendor

Name

Version

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔒
VulDB Reliability: 🔍

CVSSv3info

VulDB Meta Base Score: 7.6
VulDB Meta Temp Score: 7.4

VulDB Base Score: 6.3
VulDB Temp Score: 6.0
VulDB Vector: 🔒
VulDB Reliability: 🔍

CNA Base Score: 8.8
CNA Vector: 🔒

CVSSv2info

AVACAuCIA
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍

Exploitinginfo

Class: Deserialization
CWE: CWE-502 / CWE-20
CAPEC: 🔒
ATT&CK: 🔒

Physical: No
Local: No
Remote: Yes

Availability: 🔒
Status: Not defined

EPSS Score: 🔒
EPSS Percentile: 🔒

Price Prediction: 🔍
Current Price Estimation: 🔒

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Threat Intelligenceinfo

Interest: 🔍
Active Actors: 🔍
Active APT Groups: 🔍

Countermeasuresinfo

Recommended: Upgrade
Status: 🔍

0-Day Time: 🔒

Upgrade: LMDeploy 0.11.1

Timelineinfo

12/26/2025 Advisory disclosed
12/26/2025 +0 days VulDB entry created
12/27/2025 +0 days VulDB entry last update

Sourcesinfo

Advisory: github.com
Status: Confirmed

CVE: CVE-2025-67729 (🔒)
GCVE (CVE): GCVE-0-2025-67729
GCVE (VulDB): GCVE-100-338475
EUVD: 🔒

Entryinfo

Created: 12/26/2025 19:10
Updated: 12/27/2025 00:27
Changes: 12/26/2025 19:10 (51), 12/26/2025 23:54 (1), 12/27/2025 00:27 (12)
Complete: 🔍
Cache ID: 216::103

Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

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