CVE-2024-10572 is a vulnerability identified in the open-source machine learning platform h2oai/h2o-3, specifically in version 3.46.0.1. The issue arises from the `run_tool` command, which exposes classes within the `water.tools` package through the abstract syntax tree (AST) parser. Among these classes is the `XGBoostLibExtractTool`, which can be leveraged by an attacker to perform unauthorized operations. The core weakness is classified under CWE-94, indicating improper control of code generation, which allows attackers to execute unintended code paths or commands. Exploitation does not require any authentication or user interaction, making it remotely exploitable over the network. The attacker can cause the server to shut down and write large files to arbitrary directories, leading to denial of service (DoS) conditions by exhausting system resources or corrupting operational files. Although no exploits have been observed in the wild, the vulnerability’s CVSS score of 7.5 (high severity) reflects its potential impact on availability. The vulnerability does not affect confidentiality or integrity directly but poses a significant risk to service continuity in environments relying on h2o-3 for critical machine learning tasks. The lack of patches at the time of reporting necessitates immediate risk mitigation through access controls and monitoring.

For European organizations, especially those leveraging h2o-3 in AI/ML workflows, this vulnerability poses a significant risk to operational availability. A successful exploit can cause server shutdowns and uncontrolled file writes, potentially leading to extended downtime and disruption of machine learning services. This can affect sectors such as finance, healthcare, manufacturing, and research institutions that rely on h2o-3 for predictive analytics and decision-making. The denial of service could interrupt automated processes, delay critical insights, and impact dependent business functions. Additionally, recovery from such an attack may require manual intervention and system restoration, increasing operational costs and reducing trust in AI infrastructure. Since exploitation requires no authentication, exposed instances accessible over the internet or poorly segmented internal networks are at higher risk. The absence of confidentiality or integrity compromise limits data breach concerns but does not diminish the operational impact.

1. Immediately restrict network access to the `run_tool` command interface by implementing strict firewall rules and network segmentation to limit exposure to trusted hosts only. 2. Monitor logs for unusual activity related to the `water.tools` package and unexpected file writes or server shutdown events. 3. Employ application-layer access controls or authentication proxies to prevent unauthorized invocation of the `run_tool` command. 4. Disable or remove the `XGBoostLibExtractTool` class exposure if feasible until a patch is released. 5. Regularly check for official patches or updates from h2oai and apply them promptly once available. 6. Conduct internal audits of h2o-3 deployments to identify exposed instances and remediate accordingly. 7. Implement resource usage monitoring and alerting to detect abnormal file system usage or process terminations. 8. Educate operational teams about this vulnerability and establish incident response plans for potential DoS events related to h2o-3.