CVE-2025-48887 is a Regular Expression Denial of Service (ReDoS) vulnerability identified in the vLLM project, specifically affecting versions from 0.6.4 up to but excluding 0.9.0. vLLM is an inference and serving engine designed for large language models (LLMs), which are increasingly used in AI-driven applications. The vulnerability resides in the file `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py`, where a highly complex and nested regular expression is employed to detect tool calls. This regular expression contains multiple nested quantifiers, optional groups, and inner repetitions, making it susceptible to catastrophic backtracking. An attacker can craft input strings that exploit this complexity, causing the regex engine to consume excessive CPU resources and severely degrade the performance of the service or render it completely unavailable. The vulnerability does not impact confidentiality or integrity but directly affects availability, leading to a denial of service condition. The issue has been addressed in version 0.9.0 of vLLM, which includes a patch that presumably simplifies or replaces the vulnerable regex pattern to prevent excessive backtracking. The CVSS v3.1 base score is 6.5 (medium severity), reflecting that the attack can be launched remotely over the network, requires low complexity, needs privileges (PR:L) but no user interaction, and impacts availability only. There are no known exploits in the wild at the time of publication, but the vulnerability poses a risk to any deployment of affected vLLM versions, especially in production environments serving LLM inference requests.

For European organizations utilizing vLLM versions prior to 0.9.0, this vulnerability could lead to significant service disruptions. Since vLLM is used for serving large language models, which are often integrated into customer-facing applications, research platforms, or internal AI tools, a successful ReDoS attack could degrade response times or cause complete outages. This impacts business continuity, user experience, and operational efficiency. In sectors such as finance, healthcare, and critical infrastructure where AI-driven decision-making or automation is increasingly common, availability interruptions could have cascading effects. Additionally, organizations relying on vLLM for AI services may face reputational damage if services become unreliable. The vulnerability does not expose sensitive data or allow unauthorized access, but the denial of service potential can be exploited by malicious actors to disrupt AI services, potentially as part of larger multi-vector attacks. Given the growing adoption of AI technologies in Europe, the threat is relevant to enterprises, research institutions, and cloud service providers hosting vLLM-based solutions.

European organizations should immediately assess their deployments of vLLM and identify any instances running versions between 0.6.4 and 0.9.0. The primary mitigation is to upgrade to vLLM version 0.9.0 or later, which contains the patch addressing the ReDoS vulnerability. If immediate upgrading is not feasible, organizations should implement input validation and sanitization to detect and block maliciously crafted inputs that could trigger catastrophic backtracking. Rate limiting and request throttling can reduce the impact of potential attacks by limiting the number of regex evaluations per client or IP address. Monitoring CPU usage and response times on vLLM services can help detect ongoing exploitation attempts. Network-level protections such as Web Application Firewalls (WAFs) with custom rules to identify suspicious payloads targeting the vulnerable regex pattern may also be effective. Finally, organizations should incorporate this vulnerability into their incident response and vulnerability management workflows to ensure timely patching and detection.