CVE-2024-0960 in ai-flow
Summary
by MITRE • 01/27/2024
A vulnerability was found in flink-extended ai-flow 0.3.1. It has been declared as critical. Affected by this vulnerability is the function cloudpickle.loads of the file \ai_flow\cli\commands\workflow_command.py. The manipulation leads to deserialization. The attack can be launched remotely. The complexity of an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. The identifier VDB-252205 was assigned to this vulnerability.
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Analysis
by VulDB Data Team • 06/02/2025
The vulnerability identified as CVE-2024-0960 represents a critical security flaw within the flink-extended ai-flow 0.3.1 framework, specifically targeting the cloudpickle.loads function implementation. This issue resides in the i_flow file component where the deserialization process lacks proper input validation and sanitization mechanisms. The vulnerability stems from insufficient restrictions on the data being processed through the cloudpickle.loads function, which is designed to deserialize Python objects from serialized data streams. When maliciously crafted serialized data is passed to this function, it can execute arbitrary code on the target system, creating a severe remote code execution vector that directly impacts the integrity and confidentiality of the affected environment.
The technical exploitation of this vulnerability occurs through the manipulation of serialized data structures that cloudpickle processes during deserialization. This flaw aligns with CWE-502, which specifically addresses deserialization of untrusted data as a critical security concern. Attackers can craft malicious payloads that, when processed by the vulnerable cloudpickle.loads function, trigger unintended code execution within the context of the running application. The vulnerability's classification as critical indicates that it can be exploited without requiring authentication or specific user interaction, making it particularly dangerous in production environments where such deserialization functions are commonly used for data exchange and processing workflows.
The operational impact of CVE-2024-0960 extends beyond immediate system compromise to encompass potential data breaches, service disruption, and lateral movement within network environments. Since the affected component is part of the ai-flow framework used for machine learning and data processing pipelines, successful exploitation could lead to unauthorized access to sensitive data, modification of processing workflows, or complete system takeover. The vulnerability's presence in a data processing framework means that attackers could potentially manipulate machine learning models, corrupt data pipelines, or gain access to proprietary algorithms and training datasets that are critical to enterprise operations.
Mitigation strategies for this vulnerability should prioritize immediate patching of the affected flink-extended ai-flow 0.3.1 version, as vendors typically provide security updates to address such critical flaws. Organizations should implement network segmentation to limit access to systems containing vulnerable components, employ strict input validation and sanitization measures, and consider disabling or restricting the use of cloudpickle.loads functionality where possible. Security monitoring should be enhanced to detect anomalous deserialization activities, and regular security assessments should be conducted to identify similar vulnerabilities in other components. Additionally, implementing the principle of least privilege and maintaining up-to-date threat intelligence feeds will help organizations better protect against exploitation attempts targeting this and similar deserialization vulnerabilities. The ATT&CK framework categorizes this type of vulnerability under T1059.001 for command and scripting interpreter, as exploitation typically involves executing arbitrary code through the deserialization process.