CVE-2025-1889 identifies a vulnerability in the mmaitre314 picklescan tool, specifically versions prior to 0.0.22. Picklescan is designed to scan pickle files for malicious content to prevent code execution attacks via Python's pickle serialization format. The vulnerability arises because picklescan only considers files with standard pickle file extensions (e.g., .pkl, .pickle) when performing its security scans. An attacker can craft a malicious pickle payload embedded in a file with a non-standard or unexpected file extension, which picklescan does not scan. As a result, the malicious file bypasses the security checks and can be loaded by applications trusting picklescan's validation, leading to arbitrary code execution or other malicious actions. The weakness is classified under CWE-646, which relates to reliance on file names or extensions for security decisions. The CVSS 4.0 vector indicates the attack can be performed remotely over the network without privileges but requires user interaction (e.g., opening or loading the malicious file). The vulnerability impacts the confidentiality and integrity of affected systems by enabling execution of unauthorized code. No patches or exploit code are currently publicly available, but the issue is recognized and fixed in picklescan version 0.0.22. This vulnerability is particularly relevant in environments where pickle files are used extensively, such as machine learning model deployment and data science workflows, and where picklescan is used as a security measure to prevent malicious pickle payloads.

For European organizations, the impact of CVE-2025-1889 is primarily on the security of machine learning and data science environments that utilize Python pickle files and rely on picklescan for security scanning. Successful exploitation could lead to arbitrary code execution, compromising the confidentiality and integrity of sensitive data and systems. This could result in unauthorized data access, data manipulation, or further lateral movement within networks. Although the vulnerability does not directly affect system availability, the downstream effects of a compromise could disrupt operations or lead to data breaches. Organizations in sectors with high reliance on AI and data science, such as finance, healthcare, and manufacturing, may face increased risk. The requirement for user interaction means social engineering or phishing could be used to deliver the malicious file. The lack of known exploits in the wild currently reduces immediate risk but does not eliminate the threat, especially as attackers often target unpatched systems. Failure to update picklescan or implement additional validation controls could leave organizations exposed to this vulnerability.

1. Upgrade picklescan to version 0.0.22 or later, where this vulnerability is fixed by expanding the scope of file extensions scanned. 2. Implement additional validation mechanisms that do not rely solely on file extensions, such as inspecting file headers or magic bytes to confirm file type before processing. 3. Employ strict input validation and sandboxing when loading pickle files to limit the impact of potential malicious payloads. 4. Educate users and developers about the risks of opening pickle files from untrusted sources, emphasizing the need for caution with files having non-standard extensions. 5. Integrate multi-layered security controls such as endpoint detection and response (EDR) solutions to detect anomalous behavior resulting from malicious pickle execution. 6. Regularly audit and monitor machine learning pipelines and data ingestion workflows for unauthorized or suspicious files. 7. Consider alternative serialization formats with safer security profiles where feasible, such as JSON or protobuf, to reduce reliance on pickle files. 8. Establish incident response procedures specific to code execution attacks originating from serialized data files.