CVE-2025-54412 is a high-severity vulnerability affecting the Python library skops, specifically versions 0.11.0 and below. Skops is designed to facilitate sharing and shipping of scikit-learn based machine learning models. The vulnerability arises from an inconsistency in the OperatorFuncNode component of skops, which fails to properly distinguish between trusted and untrusted operator methods. This insufficient type distinction (classified under CWE-351) allows an attacker to conceal the execution of malicious operator methods within seemingly safe function calls. By exploiting this flaw, an adversary can perform a code reuse attack, invoking legitimate functions in a misleading manner to escalate privileges and achieve arbitrary code execution. The exploitation requires some user interaction but no prior authentication, and the attack vector is local (AV:L), meaning the attacker must have local access to the system or environment where skops is running. The vulnerability impacts confidentiality, integrity, and availability at a high level due to the potential for arbitrary code execution. The issue has been addressed in skops version 0.12.0, and users are strongly advised to upgrade to this or later versions to mitigate the risk. No known exploits are currently reported in the wild, but the high CVSS score of 8.7 indicates a serious risk if exploited.

For European organizations utilizing skops for machine learning model deployment and sharing, this vulnerability poses a significant risk. The ability to execute arbitrary code can lead to unauthorized data access, manipulation of machine learning models, and potential disruption of services relying on these models. Given the growing adoption of AI and ML in sectors such as finance, healthcare, manufacturing, and research across Europe, exploitation could result in data breaches, intellectual property theft, and operational downtime. The local attack vector suggests that insider threats or compromised user accounts could be leveraged to exploit this vulnerability. Additionally, since skops is often integrated into data science pipelines, a successful attack could undermine trust in automated decision-making systems and lead to regulatory compliance issues under GDPR and other data protection laws. The high impact on confidentiality, integrity, and availability underscores the critical need for timely remediation in European enterprises.

European organizations should immediately audit their environments to identify any use of skops versions below 0.12.0. The primary mitigation is to upgrade skops to version 0.12.0 or later, where the vulnerability is fixed. Additionally, organizations should implement strict access controls to limit local access to systems running skops, reducing the risk of exploitation by unauthorized users. Monitoring and logging of operator method invocations within skops workflows can help detect anomalous or suspicious activity indicative of exploitation attempts. Incorporating runtime application self-protection (RASP) or behavior-based anomaly detection tools in data science environments may provide early warning of code reuse attacks. Educating data scientists and developers about the risks of executing untrusted code and enforcing code review policies for model sharing workflows will further reduce exposure. Finally, organizations should maintain up-to-date incident response plans that include scenarios involving machine learning pipeline compromises.