CVE-2026-78673 in Maximo AI Service
Summary
by MITRE • 10/12/2026
IBM Maximo AI Service 9.1.0, and 9.2.0 could allow a remote attacker to cause a denial of service due to improper input validation.
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Analysis
by VulDB Data Team • 10/13/2026
The vulnerability identified in IBM Maximo AI Service versions 9.1.0 and 9.2.0 represents a critical security flaw rooted in the application's handling of external data inputs. As an enterprise-grade artificial intelligence platform, Maximo AI Service processes complex queries and machine learning model requests from various clients. The core issue lies in improper input validation mechanisms within the service layer. When a remote attacker submits specifically crafted or malformed input payloads to the affected endpoints, the system fails to adequately sanitize or validate these inputs before processing them. This lack of rigorous checks allows malicious data structures to bypass expected parsing logic, leading to unexpected behavior within the application's runtime environment.
From a technical perspective, this flaw is characteristic of CWE-20 Improper Input Validation. The vulnerability likely manifests when the service attempts to parse untrusted data without verifying its structure, length, or content against predefined schemas or constraints. In many such scenarios involving AI services, inputs may include complex JSON objects, large binary blobs, or specially crafted strings designed to trigger edge cases in parsing libraries or memory allocation routines. When these malformed inputs are processed, they can cause the application thread to hang, consume excessive CPU resources, or exhaust available memory pools. The result is a denial of service condition where legitimate users are unable to access the AI services due to resource exhaustion or process crashes caused by the unhandled exception states triggered by the malicious input.
The operational impact of this vulnerability is significant for organizations relying on IBM Maximo AI Service for predictive maintenance, asset management insights, and automated decision-making processes. A successful exploitation does not necessarily lead to unauthorized data access or remote code execution in this specific context but rather disrupts service availability. Attackers can exploit this flaw by sending a high volume of malformed requests or even a single carefully constructed payload that triggers the resource exhaustion state. This denial of service condition effectively halts business operations dependent on real-time AI analytics, leading to potential financial losses and operational delays. The ease with which a remote attacker can trigger this state without authentication requirements further exacerbates the risk profile for exposed instances of these software versions.
To mitigate this vulnerability, organizations must prioritize immediate patching by upgrading IBM Maximo AI Service to a version that includes fixes for improper input validation issues. Until patches are applied, network-level controls such as Web Application Firewalls can be configured to inspect incoming traffic for patterns indicative of malformed inputs or known attack signatures associated with denial-of-service exploits. Additionally, implementing rate limiting and request size restrictions at the load balancer or API gateway level can help absorb malicious bursts before they reach the application server. Security teams should also monitor system logs for unusual spikes in error rates or resource consumption that may indicate an ongoing exploitation attempt. Regular security assessments and penetration testing focused on input validation weaknesses are recommended to ensure robust defenses against similar vulnerabilities across the enterprise software stack. This incident underscores the importance of rigorous input sanitization practices, particularly in services that handle complex data structures like artificial intelligence platforms, aligning with broader industry standards for secure coding such as those outlined by OWASP Top Ten and MITRE ATT&CK techniques related to resource exhaustion.