CVE-2025-52645 in AION
Summary
by MITRE • 03/16/2026
HCL AION is affected by a vulnerability where model packaging and distribution mechanisms may not include sufficient authenticity verification. This may allow the possibility of unverified or modified model artifacts being used, potentially leading to integrity concerns or unintended behaviour.
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Analysis
by VulDB Data Team • 03/21/2026
The vulnerability identified as CVE-2025-52645 affects HCL AION, a platform designed for artificial intelligence model development and deployment. This security weakness resides within the model packaging and distribution mechanisms of the system, specifically concerning the authenticity verification processes that should safeguard against unauthorized modifications or tampering of machine learning artifacts. The issue represents a critical gap in the software supply chain security posture, where the platform fails to adequately validate the integrity of model components during the packaging and distribution phases.
The technical flaw manifests as insufficient verification mechanisms that should ensure model artifacts remain unmodified and authentic throughout their lifecycle. When model packages are created and distributed through HCL AION, the system does not perform robust cryptographic verification or integrity checks that would detect if files have been altered or replaced with malicious content. This vulnerability creates a pathway for attackers to potentially inject compromised model components that could execute unintended behaviors or provide backdoor access to systems that rely on these models. The weakness directly aligns with CWE-345 Insufficient Verification of Data Authenticity, which addresses scenarios where systems fail to properly authenticate data sources or detect data corruption.
The operational impact of this vulnerability extends beyond simple integrity concerns, potentially enabling sophisticated attack vectors that could compromise entire AI workflows. If an attacker successfully introduces a modified model artifact, the consequences could range from data exfiltration and system compromise to complete service disruption, depending on how the model is deployed and utilized. The vulnerability undermines trust in the AI model supply chain, potentially allowing adversaries to manipulate model behavior without detection, leading to incorrect decisions or malicious outcomes. This threat scenario particularly concerns organizations that depend on HCL AION for critical AI applications where model integrity is paramount to system security and reliability.
Organizations utilizing HCL AION should implement immediate mitigations including enhanced cryptographic verification processes, implementation of secure model signing mechanisms, and regular integrity checks of model artifacts. The recommended approach involves establishing robust digital signature validation procedures that verify model authenticity before deployment, implementing automated integrity checks during model ingestion, and maintaining detailed audit trails of all model modifications. Security measures should align with industry best practices outlined in the NIST Cybersecurity Framework and incorporate principles from the MITRE ATT&CK framework, particularly focusing on supply chain security and defense against malicious model injection attacks. Organizations should also consider implementing continuous monitoring solutions that can detect anomalous behavior patterns indicative of compromised model artifacts, while ensuring that all model distribution channels maintain proper authentication and authorization controls.