CVE-2025-3162 is a deserialization vulnerability identified in the InternLM LMDeploy product, specifically affecting versions 0.7.0 and 0.7.1. The vulnerability resides in the load_weight_ckpt function within the PT File Handler component, located in the lmdeploy/lmdeploy/vl/model/utils.py file. This function is responsible for loading model checkpoint weights, and improper input validation allows an attacker to manipulate the deserialization process. Deserialization vulnerabilities occur when untrusted data is used to reconstruct objects, potentially enabling attackers to execute arbitrary code or cause denial of service by injecting malicious payloads during the deserialization process. However, exploitation of this vulnerability requires local access to the system, meaning an attacker must have the ability to execute code or commands on the target machine prior to exploiting the flaw. The vulnerability has been publicly disclosed, but no known exploits are currently observed in the wild. The severity is classified as medium by the source, but the criticality of deserialization flaws generally depends on the context of exploitation and access requirements. Given that local access is mandatory, the attack surface is limited compared to remote vulnerabilities. The vulnerability affects the PT File Handler component, which is integral to loading model weights, a core function in LMDeploy’s machine learning deployment framework. Successful exploitation could lead to arbitrary code execution or system compromise, impacting the confidentiality, integrity, and availability of the affected system. Since LMDeploy is a tool used for deploying language models, organizations utilizing this software for AI workloads may face risks of unauthorized code execution or tampering with model data if local attackers exploit this vulnerability.

For European organizations, the impact of CVE-2025-3162 depends largely on their adoption of InternLM LMDeploy for AI model deployment. Organizations in sectors such as technology, research, finance, and critical infrastructure that leverage LMDeploy for managing language models could face risks of local privilege escalation or code execution if an attacker gains local access. This could lead to unauthorized manipulation of AI models, data leakage, or disruption of AI services. Given that exploitation requires local access, the threat is more pronounced in environments with weak internal access controls or where attackers can gain footholds through other means (e.g., phishing, insider threats). The vulnerability could also be leveraged as a pivot point for lateral movement within networks. The potential compromise of AI model integrity could have downstream effects on decision-making processes, automated systems, and data privacy compliance, which are critical concerns under European regulations such as GDPR. Additionally, disruption or manipulation of AI services could impact operational continuity in sectors increasingly reliant on AI-driven automation and analytics.

To mitigate CVE-2025-3162, European organizations should: 1) Immediately upgrade LMDeploy to a patched version once available, or apply vendor-provided workarounds if patches are not yet released. 2) Restrict local access to systems running LMDeploy by enforcing strict access controls, including multi-factor authentication and role-based access control to minimize the risk of unauthorized local exploitation. 3) Monitor and audit local user activities on LMDeploy hosts to detect suspicious behavior indicative of exploitation attempts. 4) Employ application whitelisting and endpoint protection solutions to prevent execution of unauthorized code on systems running LMDeploy. 5) Isolate LMDeploy environments within secure network segments to limit lateral movement opportunities in case of compromise. 6) Conduct regular security training for administrators and users with local access to LMDeploy systems to raise awareness of social engineering and insider threats. 7) Review and harden the configuration of LMDeploy deployments, disabling unnecessary features or services that could increase attack surface. 8) Implement integrity verification mechanisms for model checkpoint files to detect tampering attempts before loading. These targeted measures go beyond generic advice by focusing on controlling local access, monitoring, and securing the specific environment where the vulnerability exists.