CVE-2024-0960 is a deserialization vulnerability classified under CWE-502 found in the flink-extended ai-flow product, specifically version 0.3.1. The vulnerability resides in the cloudpickle.loads function within the ai_flow\cli\commands\workflow_command.py file. Deserialization vulnerabilities occur when untrusted data is deserialized, potentially allowing attackers to execute arbitrary code or manipulate application logic. In this case, the vulnerability allows remote attackers to manipulate the deserialization process. However, the attack complexity is rated as high, indicating that exploitation requires significant effort or specific conditions. The vulnerability does not require privileges (PR:N) but does require user interaction (UI:R), and the attack vector is network-based (AV:N). The CVSS v3.1 score is 5.0 (medium severity), reflecting limited confidentiality, integrity, and availability impacts (each rated low). The scope remains unchanged (S:U), meaning the vulnerability affects only the vulnerable component without impacting other components. No known exploits are currently in the wild, and no patches have been published yet. The vulnerability was publicly disclosed on January 27, 2024, and the exploit details are available, which increases the risk of future exploitation. The affected function cloudpickle.loads is commonly used for serializing and deserializing Python objects, and improper handling of untrusted input in this context can lead to remote code execution or other malicious activities. Given the nature of ai-flow as a workflow orchestration tool for AI pipelines, exploitation could disrupt AI workflows or compromise data integrity within AI operations.

For European organizations utilizing flink-extended ai-flow version 0.3.1, this vulnerability poses a moderate risk. AI and data-driven enterprises relying on ai-flow for orchestrating machine learning workflows could face disruptions or data manipulation if exploited. Although the attack complexity is high and user interaction is required, the remote attack vector means that exposed services accepting untrusted input could be targeted by attackers. Confidentiality, integrity, and availability impacts are rated low individually, but combined they could affect the reliability of AI workflows, potentially leading to incorrect AI model training or deployment decisions. This could have downstream effects on business operations, especially in sectors like finance, healthcare, and manufacturing where AI workflows are critical. The lack of available patches increases the window of exposure. Organizations with exposed ai-flow instances should consider this vulnerability seriously to avoid potential operational disruptions or data integrity issues.

1. Immediate mitigation should include restricting network access to ai-flow services, limiting exposure to trusted internal networks only. 2. Implement strict input validation and sanitization on any data that is deserialized by cloudpickle.loads to prevent untrusted data from being processed. 3. Monitor and log all deserialization activities and unusual behaviors in ai-flow workflows to detect potential exploitation attempts. 4. Employ application-layer firewalls or runtime application self-protection (RASP) solutions to detect and block malicious deserialization payloads. 5. Since no official patch is available, consider upgrading to a newer version of flink-extended ai-flow if available or applying vendor-recommended workarounds. 6. Educate users about the risk of interacting with untrusted inputs that could trigger deserialization attacks. 7. Isolate AI workflow environments to minimize the impact of a potential compromise. 8. Regularly review and audit the use of serialization/deserialization functions in the codebase to identify and remediate unsafe practices.