CVE-2026-5241
CriticalCVSS 9.6Exploitation Probability (EPSS)
Low risk43th percentile - higher than 43% of all known CVEs
Summary
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 by overriding the trust_remote_code parameter with untrusted configuration data.
Risk Assessment
An attacker can execute arbitrary code on the server during model loading, potentially leading to credential theft, lateral movement, or persistent backdoor deployment, especially in API servers, research notebooks, and CI/CD pipelines.
Recommendation
Update the transformers library to a version that prevents trust_remote_code from being overridden by model configuration.
Other vulnerabilities in huggingface/transformers
See all- CVE-2026-4372High
All versions of the HuggingFace transformers library prior to 5.3.0 contain a critical remote code execution vulnerability. An attacker can craft a malicious config.json file with the _attn_implementation_internal field set to an attacker-controlled HuggingFace Hub repository ID. When a victim loads the model via AutoModelForCausalLM.from_pretrained(), the library downloads and executes arbitrary Python code from the attacker's repository with full OS privileges.
- CVE-2025-6921High
The huggingface/transformers library, versions prior to 4.53.0, is vulnerable to Regular Expression Denial of Service (ReDoS) in the AdamWeightDecay optimizer. The _do_use_weight_decay method processes user-controlled regular expressions, which can cause catastrophic backtracking and 100% CPU utilization.
Original NVD description (English source)
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.
Vulnerability data from NVD (NIST) · CISA KEV · EPSS