CVE-2024-6960 in H2O
Summary
by MITRE • 07/21/2024
The H2O machine learning platform uses "Iced" classes as the primary means of moving Java Objects around the cluster. The Iced format supports inclusion of serialized Java objects. When a model is deserialized, any class is allowed to be deserialized (no class whitelist). An attacker can construct a crafted Iced model that uses Java gadgets and leads to arbitrary code execution when imported to the H2O platform.
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Analysis
by VulDB Data Team • 03/17/2025
The vulnerability identified as CVE-2024-6960 resides within the H2O machine learning platform's object serialization mechanism, specifically targeting the "Iced" class system that serves as the primary method for transferring Java objects across distributed cluster environments. This fundamental flaw represents a critical security weakness that directly undermines the platform's integrity and operational safety. The Iced format, designed for efficient object serialization and distribution, inadvertently creates an attack surface by permitting unrestricted deserialization of Java objects without implementing any class validation or whitelisting mechanisms. This design decision allows malicious actors to craft specially constructed Iced model files that can execute arbitrary code when imported into the H2O platform, effectively bypassing standard security boundaries and potentially compromising entire distributed computing environments.
The technical exploitation of this vulnerability leverages Java deserialization vulnerabilities through the use of Java gadgets, which are legitimate classes within the Java ecosystem that can be manipulated to execute arbitrary code during the deserialization process. When the H2O platform attempts to import a maliciously crafted Iced model, the deserialization routine processes the serialized objects without any validation of the originating classes, allowing attacker-controlled code to execute within the context of the running H2O process. This attack vector aligns with common patterns found in CVE-2024-6960 that have been catalogued under CWE-502, which specifically addresses "Deserialization of Untrusted Data" as a critical weakness in software systems. The vulnerability operates at the core of the platform's object handling architecture, making it particularly dangerous as it can be exploited through legitimate import operations that users might perform without suspecting malicious intent.
The operational impact of this vulnerability extends far beyond simple code execution, as it can lead to complete system compromise within distributed machine learning environments. An attacker who successfully exploits this vulnerability can gain unauthorized access to compute resources, potentially leading to data exfiltration, system manipulation, or further lateral movement within the network. The distributed nature of H2O clusters means that a successful attack on one node could potentially affect the entire cluster, creating a cascading security risk. Organizations relying on H2O for machine learning operations face significant exposure, particularly in environments where multiple users have the ability to import models or where automated model deployment processes exist. The vulnerability's exploitation requires no specialized privileges beyond the ability to create or import Iced model files, making it accessible to a broad range of threat actors. This risk is compounded by the fact that the attack can be concealed within legitimate-looking model imports, making detection and prevention particularly challenging.
Mitigation strategies for CVE-2024-6960 must address the fundamental deserialization vulnerability through multiple layers of defense. The most effective immediate solution involves implementing strict class whitelisting for deserialization operations, ensuring that only known safe classes can be instantiated during the Iced model import process. Organizations should also consider implementing additional security controls such as code signing verification for imported models, network segmentation to limit exposure, and comprehensive monitoring of model import activities. The implementation of these mitigations aligns with ATT&CK framework techniques related to defense evasion and privilege escalation, as they address the underlying mechanisms that enable arbitrary code execution. Regular security updates and patches from H2O developers should be prioritized, while organizations should also conduct thorough vulnerability assessments of their existing model repositories to identify and remove any potentially compromised Iced files. Additionally, implementing runtime application self-protection measures and using sandboxed environments for model imports can provide additional layers of defense against exploitation attempts.