CVE-2025-3263 is a Regular Expression Denial of Service (ReDoS) vulnerability identified in the Hugging Face Transformers library, specifically within the get_configuration_file() function of the transformers.configuration_utils module. The vulnerability affects version 4.49.0 of the library and was resolved in version 4.51.0. The root cause is an inefficient regular expression pattern, config\.(.*)\.json, which is susceptible to catastrophic backtracking when processing specially crafted input strings. This leads to excessive CPU consumption, causing significant delays or complete denial of service in applications that rely on this function for loading configuration files. Since Hugging Face Transformers is widely used for natural language processing (NLP) and machine learning model serving, exploitation of this vulnerability can disrupt model availability and degrade application performance. The CVSS score of 5.3 (medium severity) reflects that the vulnerability can be exploited remotely without authentication or user interaction, impacting availability but not confidentiality or integrity. No known exploits are reported in the wild as of now. The vulnerability is classified under CWE-1333, which pertains to inefficient regular expression complexity leading to resource exhaustion. The issue highlights the importance of carefully designing regular expressions in security-critical code to avoid performance pitfalls that can be weaponized for denial of service attacks.

For European organizations using the Hugging Face Transformers library, especially those deploying NLP models in production environments, this vulnerability poses a risk of service disruption. Applications that load configuration files using the affected function may experience increased latency or become unresponsive under attack, leading to denial of service. This can affect customer-facing services, internal automation, or research workflows relying on timely model inference. Resource exhaustion caused by the ReDoS attack could also increase operational costs due to higher CPU usage and potentially trigger cascading failures in distributed systems. Sectors such as finance, healthcare, and government, which increasingly adopt AI/ML technologies for decision-making and automation, may face operational risks and reputational damage if availability is compromised. However, since the vulnerability does not impact confidentiality or integrity, data breaches or unauthorized data modification are not direct concerns. The medium severity rating suggests that while the threat is significant, it is not critical, and mitigation is feasible through patching or input validation.

European organizations should promptly upgrade the Hugging Face Transformers library to version 4.51.0 or later, where the vulnerability is fixed. If immediate upgrading is not possible, implement input validation or sanitization to restrict or reject suspicious configuration file names that could trigger the vulnerable regex pattern. Monitoring CPU usage and application response times can help detect potential exploitation attempts. Employing runtime application self-protection (RASP) or Web Application Firewalls (WAFs) with rules to detect abnormal request patterns targeting configuration file loading endpoints may provide additional defense. Developers should review and refactor any custom regular expressions in their codebase to avoid similar inefficiencies. Finally, incorporating fuzz testing and performance profiling for regex patterns during development can prevent future ReDoS vulnerabilities.