CVE-2025-3262 is a Regular Expression Denial of Service (ReDoS) vulnerability identified in the huggingface/transformers library, specifically affecting version 4.49.0. The vulnerability arises from inefficient regular expression complexity within the SETTING_RE variable located in the transformers/commands/chat.py file. This regex includes repetition groups and non-optimized quantifiers that cause exponential backtracking when processing inputs that nearly match the pattern but fail at the end. Such behavior can severely degrade application performance, leading to denial-of-service conditions by exhausting CPU resources during regex evaluation. The vulnerability does not affect confidentiality or integrity but impacts availability by slowing or halting the affected application. The issue has been addressed and fixed in version 4.51.0 of the library. The CVSS v3.0 score is 5.3 (medium severity), reflecting that the vulnerability can be exploited remotely without authentication or user interaction, but only impacts availability. No known exploits are currently reported in the wild. This vulnerability is relevant to applications using the vulnerable huggingface/transformers versions, particularly those that process untrusted input through the affected regex. Huggingface transformers is a widely used open-source library for natural language processing tasks, including chatbots and AI assistants, making this vulnerability significant in contexts where the library is exposed to external input streams.

For European organizations, the impact of this vulnerability primarily concerns service availability and operational continuity. Organizations deploying AI-powered chatbots, virtual assistants, or other NLP applications built on huggingface/transformers version 4.49.0 or earlier could experience performance degradation or downtime if an attacker sends specially crafted input designed to trigger the ReDoS condition. This could disrupt customer-facing services, internal automation, or data processing pipelines relying on these models. While the vulnerability does not compromise data confidentiality or integrity, denial-of-service conditions can lead to loss of productivity, customer dissatisfaction, and potential financial losses. Sectors such as finance, healthcare, telecommunications, and public services in Europe that increasingly integrate AI-driven interfaces are particularly at risk if they have not updated to patched versions. Additionally, organizations with exposed APIs or web services that accept user input processed by this library are more vulnerable to exploitation attempts. Given the medium severity and absence of known exploits, the immediate risk is moderate but warrants prompt remediation to prevent potential abuse.

European organizations should take the following specific mitigation steps: 1) Identify all deployments using huggingface/transformers version 4.49.0 or earlier, especially those exposing chat or NLP interfaces to external or untrusted inputs. 2) Upgrade the transformers library to version 4.51.0 or later, where the inefficient regex has been optimized and the vulnerability fixed. 3) Implement input validation and sanitization to limit or reject inputs that could trigger excessive regex backtracking, such as unusually long or complex strings. 4) Employ runtime monitoring and anomaly detection to identify unusual CPU usage spikes or latency increases that may indicate attempted ReDoS attacks. 5) Where feasible, isolate NLP processing components behind rate limiting or request throttling to reduce the impact of potential abuse. 6) Conduct security testing including fuzzing and regex performance profiling on NLP input handlers to proactively detect similar inefficiencies. 7) Maintain an inventory of AI/ML components and their versions to ensure timely patching of vulnerabilities. These targeted actions go beyond generic advice by focusing on the specific vulnerable regex and operational context of huggingface transformers in AI applications.