CVE-2023-30444 in Watson Machine Learning on Cloud Pak for Data
Summary
by MITRE • 04/27/2023
IBM Watson Machine Learning on Cloud Pak for Data 4.0 and 4.5 is vulnerable to server-side request forgery (SSRF). This may allow an authenticated attacker to send unauthorized requests from the system, potentially leading to network enumeration or facilitating other attacks. IBM X-Force ID: 253350.
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Analysis
by VulDB Data Team • 05/21/2023
The vulnerability CVE-2023-30444 represents a critical server-side request forgery flaw in IBM Watson Machine Learning component running within IBM Cloud Pak for Data versions 4.0 and 4.5. This security weakness stems from inadequate input validation mechanisms that fail to properly sanitize user-supplied data before processing external requests. The flaw allows authenticated adversaries to manipulate the application's request handling logic and potentially access internal network resources that should remain isolated from external access. Such vulnerabilities typically arise when applications blindly forward user inputs to backend services without proper validation or filtering of destination addresses or protocols. The affected IBM Watson Machine Learning service operates within a containerized environment that exposes REST API endpoints susceptible to manipulation through crafted requests.
The technical exploitation of this SSRF vulnerability occurs when authenticated users submit maliciously formatted requests that cause the system to make unintended network calls to internal services or external endpoints controlled by the attacker. This flaw enables attackers to perform network enumeration activities by probing internal systems that are normally protected by network segmentation. The vulnerability specifically impacts the machine learning platform's ability to properly validate and restrict outbound connections, potentially allowing attackers to access internal databases, service discovery mechanisms, or other sensitive backend systems. According to CWE-918, this represents a server-side request forgery vulnerability where attackers can manipulate the target of a request to point to internal resources. The flaw demonstrates poor input sanitization practices that permit attackers to bypass normal access controls and potentially escalate privileges within the system.
The operational impact of this vulnerability extends beyond simple information disclosure to potentially enable more sophisticated attack vectors including lateral movement within the network infrastructure. An authenticated attacker could leverage this flaw to discover internal service endpoints, extract sensitive data from internal databases, or even establish command and control channels through manipulated requests. The vulnerability particularly affects organizations using IBM Cloud Pak for Data deployments where Watson Machine Learning serves as a core component for artificial intelligence workloads. Attackers could use this vulnerability to map internal network topology, identify running services, and potentially access other systems that share the same network infrastructure. This weakness aligns with ATT&CK technique T1071.004 for application layer protocol and T1046 for network service scanning, representing both reconnaissance and exploitation phases of an attack lifecycle. The vulnerability's impact is amplified in environments where multiple services share network resources or where proper network segmentation has not been implemented.
Organizations should implement immediate mitigations including strict input validation for all user-supplied data, implementation of network access controls to restrict outbound connections, and comprehensive monitoring of unusual network activity patterns. The recommended approach involves deploying web application firewalls that can detect and block suspicious outbound requests, implementing proper network segmentation to isolate critical systems, and conducting regular security assessments to identify similar vulnerabilities. IBM has released patches and updates to address this vulnerability, and organizations should prioritize applying these fixes to their Cloud Pak for Data deployments. Additional mitigations include implementing strict API rate limiting, enabling detailed logging of all external requests, and conducting regular penetration testing to identify potential SSRF vectors. Security teams should also consider implementing privilege separation mechanisms to limit the scope of potential damage if exploitation occurs. The vulnerability serves as a reminder of the critical importance of input validation and proper access controls in modern cloud-based machine learning platforms where data processing often involves complex network interactions and service dependencies.