Skip to main content
VulnerabilityPublished Updated

CVE-2026-33625: InternLM lmdeploy · Uncontrolled Resource Consumption

Affects InternLM lmdeploySource CVE Database V5

0
Compare

Not known to be exploited · CVSS 8.8 High : upgrade to 0.12.3 soon.

Data as of 2 Nov 2026, 10:58 UTC

CVSS 3.1 base score
8.8High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.4% chance of exploitation in 30 days – higher than 38% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.12.3
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 18 Sep 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq 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 2 Nov 2026.

What to do

Upgrade InternLM lmdeploy to 0.12.3.

Affected software

ProductAffectedFixed in
InternLMlmdeployAffected
  • >=0.12.1 <0.12.3
Fixed in
  • 0.12.3
PyPIlmdeploypkg:pypi/lmdeployOther threatsAffected
  • >=0.12.1 <0.12.3
Fixed in
  • 0.12.3

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

Users should upgrade to lmdeploy version 0.12.3 or later, which contains the official patch addressing this vulnerability. No other mitigations are indicated or required once the patch is applied.

Description

LMDeploy is a toolkit for compressing, deploying, and serving large language models. Versions 012.1 through 0.12.2 contain a code injection vulnerability in `lmdeploy/pytorch/config.py` line 620 that allows an attacker to execute arbitrary Python code by publishing a malicious HuggingFace model with a crafted `quantization_config.quant_dtype` value. When a user loads the model with lmdeploy, the `quant_dtype` is passed to `eval(f'torch.{quant_dtype}')` without any validation. Version 0.12.3 contains a patch.

References

Community reviews

Loading reviews…