CVE-2025-6921
HighCVSS 7.5Exploitation Probability (EPSS)
Low risk42th percentile - higher than 42% of all known CVEs
Summary
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.
Risk Assessment
An attacker controlling patterns in the lists can cause machine learning tasks to hang and render services unresponsive.
Recommendation
Update the transformers library to version 4.53.0 or later.
Other vulnerabilities in huggingface/transformers
See all- CVE-2026-5241Critical
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.
- 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.
Original NVD description (English source)
The huggingface/transformers library, versions prior to 4.53.0, is vulnerable to Regular Expression Denial of Service (ReDoS) in the AdamWeightDecay optimizer. The vulnerability arises from the _do_use_weight_decay method, which processes user-controlled regular expressions in the include_in_weight_decay and exclude_from_weight_decay lists. Malicious regular expressions can cause catastrophic backtracking during the re.search call, leading to 100% CPU utilization and a denial of service. This issue can be exploited by attackers who can control the patterns in these lists, potentially causing the machine learning task to hang and rendering services unresponsive.
Vulnerability data from NVD (NIST) · CISA KEV · EPSS