A vulnerability has been found in ModelCloud GPTQModel up to 7.2.0. This vulnerability affects unknown code of the file gptqmodel/nn_modules/qlinear/tritonv2.py of the component Triton dequantization kernel. Such manipulation of the argument g_idx leads to out-of-bounds read.
The attack can be executed remotely. The exploit has been disclosed to the public and may be used. Upgrading to version 7.3.0 is able to resolve this issue.
The name of the patch is 877c732f7d7dccd56a729844c6a5bd20f3aa8bb1. Upgrading the affected component is recommended.
Attack Parameters
Attack Vector
Network
Can be exploited remotely
Attack Complexity
Low
Easy to exploit
Attack Requirements
None
No additional conditions
Privileges Required
None
No privileges needed
User Interaction
Passive
Minimal interaction
Impact Assessment
Confidentiality
Low
Partial data leak
Integrity
Low
Partial data modification
Availability
Low
Partial disruption
CVSS Vector v4.0
Weakness Type (CWE)
References 9
https://github.com/ModelCloud/GPTQModel/
cna@vuldb.com
https://github.com/ModelCloud/GPTQModel/commit/877c732f7d7dccd56a729844c6a5bd20…
cna@vuldb.com
https://github.com/ModelCloud/GPTQModel/issues/2949
cna@vuldb.com
https://github.com/ModelCloud/GPTQModel/pull/2950
cna@vuldb.com
https://github.com/ModelCloud/GPTQModel/releases/tag/v7.3.0
cna@vuldb.com
https://vuldb.com/cve/CVE-2026-86288
cna@vuldb.com
https://vuldb.com/submit/906135
cna@vuldb.com
https://vuldb.com/vuln/399447
cna@vuldb.com
https://vuldb.com/vuln/399447/cti
cna@vuldb.com