CVE-2025-1716 is a vulnerability identified in the mmaitre314 picklescan tool, versions before 0.0.21, which is designed to scan Python Pickle serialized models for unsafe content. The core issue stems from an incomplete list of disallowed globals, specifically the omission of 'pip' as an unsafe global. Python's Pickle module allows arbitrary code execution during deserialization if unsafe globals are present. Attackers can craft malicious serialized models that invoke pip.main(), a function that can install arbitrary Python packages from PyPI or other repositories such as GitHub. Because picklescan does not flag 'pip' as unsafe, these malicious models can pass security scans, misleading users into believing the model is safe. This vulnerability is classified under CWE-184 (Incomplete List of Disallowed Inputs), indicating a failure to comprehensively restrict dangerous inputs. The CVSS 4.0 score is 5.3 (medium), reflecting network attack vector, low attack complexity, no privileges required, but requiring user interaction (scanning the malicious model). The vulnerability can lead to remote code execution during the scanning process, potentially compromising the scanning environment. No patches or fixes were linked at the time of disclosure, and no known exploits are reported in the wild. This vulnerability highlights the risks of insufficient input validation in security tools that analyze serialized data, especially in machine learning workflows where Pickle is common.

For European organizations, the impact of CVE-2025-1716 can be significant in environments where picklescan is used to validate machine learning models before deployment. Successful exploitation could lead to arbitrary code execution on systems performing the scan, potentially compromising build pipelines, continuous integration environments, or production servers. This could result in unauthorized access, data theft, or further malware deployment. Organizations relying on Python-based ML workflows and automated security scanning are at risk of supply chain compromise through malicious model uploads. The medium severity score reflects that while exploitation requires user interaction (scanning the malicious model), the lack of privilege requirements and network attack vector make it feasible in environments where untrusted models are scanned. The vulnerability could disrupt trust in ML model validation processes and introduce risks to data confidentiality and system integrity. European sectors with high adoption of Python ML tools, such as finance, automotive, and research institutions, may face elevated risks.

To mitigate CVE-2025-1716, European organizations should immediately upgrade picklescan to version 0.0.21 or later, where 'pip' is correctly treated as an unsafe global. Until patches are applied, organizations should avoid scanning untrusted or third-party Pickle models with vulnerable picklescan versions. Implement strict model provenance and validation policies to reduce the risk of malicious inputs. Consider sandboxing the scanning environment to limit the impact of potential code execution during scans. Additionally, monitor network traffic and system logs for unusual pip activity or package installations triggered during scanning. Security teams should review and harden CI/CD pipelines that incorporate picklescan to prevent exploitation. Finally, raise awareness among data scientists and developers about the risks of deserializing untrusted Pickle data and encourage the use of safer serialization formats where possible.