H2O 通过 POJO 模型导入实现远程代码执行

该 RCE 漏洞的影响极其严重。攻击者可以利用此漏洞在 H2O 服务器上执行任意代码，从而完全控制服务器。这可能导致敏感数据的泄露、系统配置的篡改，甚至整个网络的破坏。攻击者可能利用此漏洞进行横向移动，攻击其他连接到同一网络的系统。由于 H2O 经常用于处理敏感数据，例如金融数据或医疗记录，因此该漏洞的潜在影响非常大。类似于其他 POJO 反序列化漏洞，攻击者可以精心构造恶意模型来触发 RCE。

Organizations heavily reliant on H2O for machine learning tasks, particularly those exposing the H2O dashboard to external networks or untrusted users, are at significant risk. Environments where model import functionality is frequently used, especially with models sourced from external providers, are also particularly vulnerable. Legacy H2O deployments that have not been regularly updated are at heightened risk.

• java / server: Monitor H2O server logs for suspicious activity related to model imports. Look for errors or unusual processes being spawned.

journalctl -u h2o -f | grep -i "error" 

• java / supply-chain: Examine any third-party libraries or dependencies used by the H2O server for potential vulnerabilities. • generic web: Monitor network traffic to the H2O dashboard for unusual requests or data uploads. • java / server: Use a Java profiler to monitor memory usage and identify potential deserialization vulnerabilities.

1. 2023-11-16Disclosure

   disclosure

1. 已保留2023-11-08
2. 发布日期2023-11-16
3. 修改日期2024-08-02
4. EPSS 更新日期2026-04-02

目前，官方尚未发布修复版本。作为临时缓解措施，建议禁用 H2O 仪表盘的 POJO 模型导入功能。如果必须使用该功能，请严格限制允许导入的模型来源，并对所有导入的模型进行严格的安全审查。此外，可以考虑使用 Web 应用防火墙 (WAF) 来检测和阻止恶意请求。监控 H2O 服务器的日志，查找任何异常活动，例如未经授权的代码执行尝试。升级到修复版本是最终的解决方案，请密切关注 H2O 官方发布的修复公告。