A vulnerability in the LightGlue model loading path of huggingface/transformers version 5.2.0 allows an attacker-controlled model repository to execute arbitrary code during model initialization. The issue arises because the trust_remote_code parameter, intended to prevent remote code execution, is overridden by untrusted serialized configuration data in a nested code path. Specifically, when loading a LightGlue model using AutoModel.from_pretrained() with trust_remote_code=False, the LightGlueConfig reads the trust_remote_code value from the untrusted config.json file and propagates it into nested AutoConfig.from_pretrained() calls. This results in the execution of attacker-provided Python modules, even when the victim explicitly disables remote code execution. The vulnerability poses a high risk for environments such as API inference servers, research notebooks, CI/CD pipelines, and model evaluation workers, potentially leading to credential theft, lateral movement, or persistence/backdoor deployment.
{
"cna_assigner": "@huntr_ai",
"cwe_ids": [
"CWE-829"
],
"osv_generated_from": "https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/5xxx/CVE-2026-5241.json"
}{
"extracted_events": [
{
"cpe": "cpe:2.3:a:huggingface:transformers:5.2.0:*:*:*:*:*:*:*",
"range": [
{
"introduced": "5.2.0"
},
{
"last_affected": "5.2.0"
}
],
"source": "CPE_STRING"
},
{
"range": [
{
"fixed": "676559d5022b74aaa0cee1cee0842b7f27c5320e"
}
],
"source": "REFERENCES_COMMIT"
}
]
}