CVE-2024-1748 is a deserialization vulnerability identified in version 0.1.21 of the AutoPrognosis software developed by van_der_Schaar LAB. The vulnerability resides in the load_model_from_file function within the Release Note Handler component. Specifically, this function improperly handles deserialization of data, allowing an attacker to manipulate input that is deserialized by the application. Deserialization vulnerabilities occur when untrusted data is deserialized without sufficient validation, potentially enabling remote code execution, arbitrary code injection, or other malicious actions. In this case, the vulnerability can be triggered remotely, indicating that an attacker does not require local access to exploit the flaw. However, the attack complexity is considered rather high, and exploitation is difficult, which suggests that a successful attack would require significant expertise and possibly specific conditions or knowledge about the target environment. The vulnerability has been publicly disclosed, but no patches or fixes have been released by the vendor, who has not responded to disclosure attempts. There are no known exploits currently observed in the wild. CWE-502 (Deserialization of Untrusted Data) is the underlying weakness, which is a common source of critical security issues in software that processes serialized objects. The vulnerability affects only version 0.1.21 of AutoPrognosis, a software product used primarily for automated prognostic modeling, which may be deployed in healthcare or research environments. The lack of vendor response and absence of patches increases the risk for organizations still using this version, as attackers could develop exploits based on the public disclosure.

For European organizations, the impact of this vulnerability could be significant, especially for those in healthcare, research institutions, or companies relying on AutoPrognosis for predictive modeling and decision support. Successful exploitation could lead to unauthorized code execution, data manipulation, or system compromise, potentially affecting confidentiality, integrity, and availability of sensitive data and services. Given the nature of AutoPrognosis, which likely handles sensitive patient or research data, a breach could result in exposure of personal health information, violation of GDPR regulations, and severe reputational damage. The difficulty of exploitation and high attack complexity somewhat mitigate immediate risk, but the public disclosure and lack of vendor patching mean that motivated attackers could eventually develop reliable exploits. The vulnerability’s remote attack vector increases the threat surface, especially for organizations exposing AutoPrognosis services to external networks or insufficiently segmented internal networks. The absence of known exploits in the wild currently reduces immediate threat but does not eliminate the risk of future attacks. Overall, the vulnerability poses a medium risk but with potential for escalation if exploited in targeted attacks against critical European healthcare or research infrastructure.

1. Immediate mitigation should focus on isolating and restricting network access to AutoPrognosis instances, ensuring that only trusted internal users and systems can communicate with the vulnerable service. 2. Implement strict input validation and sanitization on any data fed into the load_model_from_file function or related deserialization processes, if possible through configuration or application-level controls. 3. Monitor logs and network traffic for unusual deserialization activity or attempts to load unexpected serialized objects. 4. Consider deploying runtime application self-protection (RASP) or endpoint detection and response (EDR) solutions that can detect anomalous behavior indicative of deserialization attacks. 5. If feasible, upgrade to a newer version of AutoPrognosis once the vendor releases a patch or consider alternative software solutions that do not have this vulnerability. 6. In the absence of a patch, consider applying application-layer sandboxing or containerization to limit the impact of potential exploitation. 7. Conduct regular security assessments and penetration testing focused on deserialization and remote code execution vectors within the environment. 8. Educate developers and system administrators about the risks of deserialization vulnerabilities and encourage secure coding practices to prevent similar issues in custom integrations or extensions.