Last seen June 3, 2026

LightGlue Remote Code Execution Vulnerability

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.

Technical Severity
Medium severity
Lifecycle Status

STABLE

What Happened

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.

Why This Matters

The evidence matters to defenders using LightGlue because it could let an attacker run code in affected environments.

Recommended Action

Confirm whether Path is present in your environment and review vendor guidance for this report. Apply available patches or mitigations if your deployment matches the described conditions.

Exposure

My Interests Exposure

Exposure unknown

Recommended Response
Last Seen

Jun 03, 2026 21:00

Exposure reason: This incident does not currently match a technology in My Interests.

Exploitation status: DEMONSTRATED

Primary entities:

huggingfaceLightGluetransformersRemote Code ExecutionvulnerabilityDuring Model Initialization

Timeline

  • Incident first seen
    Jun 03, 2026 21:00

    BugSkan first recorded this incident.

  • huggingface/transformers: Arbitrary Code Execution During Model Initialization in the LightGlue Model Loading Path
    Jun 03, 2026 21:00

    GitHub Advisory Database · Vulnerability

Sources

huggingface/transformers: Arbitrary Code Execution During Model Initialization in the LightGlue Model Loading Path

GitHub Advisory Database · Jun 03, 2026 21:00

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.

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