CVE-2025-67729: InternLM lmdeploy · Deserialization of Untrusted Data
Affects InternLM lmdeploySource CVE Database V5
Not known to be exploited · CVSS 8.8 High : upgrade to 0.11.1 soon.
Data as of 25 Oct 2026, 19:02 UTC
- EPSS
- 0.5% chance of exploitation in 30 days – higher than 43% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 0.11.1
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 26 Dec 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-25.
What to do
Upgrade InternLM lmdeploy to 0.11.1.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
PyPIlmdeploypkg:pypi/lmdeployOther threats | Affected
| Fixed in
|
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
To mitigate this vulnerability, European organizations should: 1) Immediately upgrade lmdeploy to version 0.11.1 or later, where the vulnerability is patched by enforcing weights_only=true during model loading. 2) Implement strict validation and integrity checks on all model files before loading, including cryptographic signatures or hashes from trusted sources. 3) Restrict model file loading to isolated or sandboxed environments to limit potential damage from malicious payloads. 4) Educate users and developers about the risks of loading untrusted model files and enforce policies that prohibit using models from unverified sources. 5) Monitor systems running lmdeploy for unusual behavior indicative of exploitation attempts, such as unexpected process spawning or network activity. 6) Employ network segmentation and least privilege principles to reduce the attack surface and limit lateral movement if compromise occurs. 7) Maintain up-to-date backups of critical AI models and system configurations to enable recovery in case of compromise.
Description
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.