CVE-2025-3162 in LMDeploy
Summary
by MITRE • 04/03/2025
A vulnerability was found in InternLM LMDeploy up to 0.7.1. It has been classified as critical. Affected is the function load_weight_ckpt of the file lmdeploy/lmdeploy/vl/model/utils.py of the component PT File Handler. The manipulation leads to deserialization. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used.
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Analysis
by VulDB Data Team • 04/24/2025
The vulnerability identified as CVE-2025-3162 represents a critical deserialization flaw within the InternLM LMDeploy framework version 0.7.1 and earlier. This security issue resides in the load_weight_ckpt function located within the lmdeploy/lmdeploy/vl/model/utils.py file, which serves as a crucial component for handling PT file operations. The vulnerability stems from insufficient validation during the deserialization process, creating a potential attack vector that could be exploited by local adversaries who have access to the system. The flaw specifically affects the PT File Handler component, which is responsible for managing model weight loading operations in vision language models. This critical classification indicates the severity of potential impact, as deserialization vulnerabilities often allow attackers to execute arbitrary code or manipulate system behavior through crafted input data. The vulnerability's local attack requirement suggests that exploitation would need physical or authenticated access to the target system, but this limitation does not diminish its potential for causing significant damage within compromised environments.
The technical implementation of this vulnerability involves the improper handling of serialized data structures during the weight loading process. When the load_weight_ckpt function processes PT files containing model weights, it fails to adequately validate or sanitize the incoming serialized data before deserializing it into memory. This oversight creates an environment where maliciously crafted PT files could trigger unintended code execution or data manipulation within the application context. The vulnerability's classification aligns with CWE-502, which specifically addresses deserialization of untrusted data, and follows patterns commonly associated with the ATT&CK technique T1059.001 for command and scripting interpreter. The exploitation of this flaw could potentially enable attackers to execute arbitrary commands with the privileges of the running process, leading to complete system compromise. The public disclosure of this exploit means that threat actors have already developed methods to leverage this weakness, making immediate remediation essential for affected systems.
The operational impact of CVE-2025-3162 extends beyond simple code execution, as it represents a fundamental security weakness in the model deployment infrastructure. Organizations utilizing InternLM LMDeploy for vision language model operations face potential risks including data exfiltration, model integrity compromise, and unauthorized access to sensitive computational resources. The vulnerability's presence in the PT File Handler component suggests that any system relying on this framework for model loading operations is at risk, particularly in environments where model weights are frequently updated or loaded from untrusted sources. The local attack requirement does not prevent the vulnerability from being leveraged in broader compromise scenarios, as attackers could potentially gain local access through other means or exploit additional weaknesses to establish a foothold. This flaw could particularly affect research institutions, cloud service providers, and enterprise environments that deploy vision language models for various applications including automated content analysis, security monitoring, and machine learning research.
Mitigation strategies for CVE-2025-3162 must prioritize immediate version updates to LMDeploy 0.7.2 or later, where the deserialization vulnerability has been addressed through proper input validation and sanitization. Organizations should implement strict access controls to prevent unauthorized local access to systems running affected versions, while also conducting comprehensive vulnerability assessments to identify other potential attack vectors that could be leveraged in combination with this weakness. The remediation process should include disabling or restricting PT file loading operations until the vulnerability is fully patched, along with implementing monitoring mechanisms to detect unusual file loading activities. Security teams should also consider implementing application whitelisting policies and restricting the execution of potentially malicious serialized data. Additional protective measures include network segmentation to limit the impact of potential exploitation and regular security audits of model deployment environments. The vulnerability's public disclosure necessitates urgent action to prevent exploitation, as it represents a known attack pattern that threat actors are actively using in the wild. Organizations should also review their incident response procedures to ensure preparedness for potential exploitation attempts and implement proper logging and alerting mechanisms to detect unauthorized model weight loading operations.