CVE-2024-1748 in AutoPrognosis
Summary
by MITRE • 02/22/2024
A vulnerability classified as critical was found in van_der_Schaar LAB AutoPrognosis 0.1.21. This vulnerability affects the function load_model_from_file of the component Release Note Handler. The manipulation leads to deserialization. The attack can be initiated remotely. The complexity of an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. VDB-254530 is the identifier assigned to this vulnerability. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.
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Analysis
by VulDB Data Team • 04/18/2025
The vulnerability identified as CVE-2024-1748 represents a critical security flaw in the van_der_Schaar LAB AutoPrognosis version 0.1.21 software ecosystem. This issue manifests within the Release Note Handler component specifically through the load_model_from_file function, which serves as the primary attack vector for exploitation. The vulnerability classifies as a deserialization flaw that permits remote code execution through carefully crafted malicious input data. The security implications are severe as the flaw allows attackers to execute arbitrary code on affected systems without requiring authentication, making it particularly dangerous in production environments where such software might be exposed to untrusted networks. The attack complexity is rated as high due to the sophisticated nature of the exploitation techniques required to successfully leverage this vulnerability.
The technical implementation of this vulnerability stems from improper handling of serialized data within the load_model_from_file function, which processes release notes and model files without adequate validation or sanitization of input parameters. This deserialization weakness creates a pathway for attackers to inject malicious payloads that can be executed during the deserialization process, effectively allowing for remote code execution. The vulnerability's classification as a critical risk aligns with CWE-502 which specifically addresses deserialization of untrusted data as a significant security concern. The attack surface is expanded through the remote accessibility of the vulnerability, meaning that threat actors can exploit this flaw from external networks without requiring physical access to the target system.
From an operational impact perspective, organizations utilizing AutoPrognosis 0.1.21 may face severe consequences including unauthorized access to sensitive data, system compromise, and potential lateral movement within network environments. The difficulty of exploitation, while high, does not mitigate the risk as public disclosure of the exploit (VDB-254530) means that sophisticated attackers can leverage this vulnerability without requiring advanced technical skills. The lack of vendor response to early disclosure attempts compounds the risk as organizations have no assurance of receiving timely patches or updates to address this critical flaw. This vulnerability directly aligns with ATT&CK technique T1210 which covers exploitation of remote services, and represents a significant risk for organizations that may be using this software in healthcare or research environments where sensitive patient data might be processed.
The exploitation of CVE-2024-1748 requires attackers to craft malicious serialized objects that can be processed by the vulnerable load_model_from_file function, potentially leading to complete system compromise. Organizations should immediately implement network segmentation to limit access to affected systems, disable unnecessary network services, and monitor for suspicious activities related to the Release Note Handler component. Security teams should also consider implementing application whitelisting controls and network-based intrusion detection systems to identify potential exploitation attempts. The lack of vendor response underscores the importance of proactive security measures and maintaining awareness of public exploits that may affect software components in use across enterprise environments, particularly in research and healthcare sectors where AutoPrognosis might be deployed for predictive analytics and medical research applications.