The vulnerability identified as CVE-2025-52645 affects HCL AION version 2.0, specifically its model packaging and distribution mechanisms. The core issue is the lack of sufficient authenticity verification for model artifacts during packaging and distribution. This deficiency means that modified or unverified models could be introduced into the system, potentially leading to integrity violations or unintended operational behavior of the AI models. The vulnerability requires local access with high privileges (AV:L/PR:H), indicating that an attacker must already have significant system access to exploit it. No user interaction is needed, and the scope is unchanged, meaning the vulnerability affects only the component where it exists without broader system impact. The CVSS v3.1 base score is 1.9, reflecting low severity due to limited impact on confidentiality, availability, and the high complexity of exploitation. No known public exploits or patches have been reported yet. This vulnerability highlights the importance of robust authenticity checks in AI model lifecycle management to prevent tampering or supply chain attacks.

The primary impact of this vulnerability is on the integrity of AI models managed by HCL AION 2.0. If exploited, attackers with high-level local access could introduce modified or malicious model artifacts, potentially causing the AI system to behave unexpectedly or incorrectly. This could undermine trust in AI-driven decisions or outputs, especially in critical applications relying on model accuracy and integrity. However, since exploitation requires high privileges and local access, the risk of remote or widespread attacks is minimal. There is no direct impact on confidentiality or availability, limiting the scope of damage. Organizations using HCL AION in sensitive or regulated environments may face compliance or operational risks if model integrity is compromised. Overall, the threat is contained but warrants attention in environments where AI model integrity is critical.

To mitigate this vulnerability, organizations should implement strict access controls to limit local high-privilege access to trusted personnel only. Employing system-level protections such as mandatory code signing and integrity verification for model artifacts can help detect tampering before deployment. Monitoring and auditing model packaging and distribution processes for anomalies or unauthorized changes is recommended. Organizations should stay alert for official patches or updates from HCL addressing this issue and apply them promptly once available. Additionally, adopting a defense-in-depth approach by segregating AI model management environments and using cryptographic verification methods for model artifacts can further reduce risk. Regular security assessments of AI supply chain processes will help identify and remediate similar weaknesses proactively.