CVE-2024-10190 is a critical vulnerability in the Horovod distributed deep learning framework, specifically affecting versions up to and including v0.28.1. The root cause is improper handling of deserialization in the ElasticRendezvousHandler, a subclass of KVStoreHandler. The vulnerability occurs in the _put_value method, which processes base64-encoded data by calling codec.loads_base64(value). This function eventually invokes cloudpickle.loads(decoded), which deserializes the data. Since cloudpickle can deserialize arbitrary Python objects, an attacker can craft a malicious pickle payload and send it via an unauthenticated HTTP PUT request to the vulnerable Horovod service. This leads to remote code execution (RCE) on the server hosting Horovod. The attack requires no authentication or user interaction and can be performed remotely over the network. The vulnerability affects the confidentiality, integrity, and availability of the system, as arbitrary code execution can lead to data theft, system compromise, or denial of service. The CVSS v3.0 score is 9.8 (critical), reflecting the ease of exploitation and severe impact. No patches or fixes are currently linked, and no known exploits have been reported in the wild. Horovod is commonly used in distributed machine learning clusters, often in cloud or on-premises environments, making this vulnerability particularly dangerous for organizations relying on AI workloads and data processing pipelines.

For European organizations, the impact of CVE-2024-10190 is significant due to the widespread adoption of Horovod in AI research, financial modeling, and technology sectors. Successful exploitation can lead to full system compromise, allowing attackers to execute arbitrary commands, steal sensitive data, manipulate machine learning models, or disrupt critical AI workflows. This can result in intellectual property theft, regulatory non-compliance (e.g., GDPR violations due to data breaches), operational downtime, and reputational damage. Organizations running Horovod on cloud infrastructure or exposed networks are especially at risk. The vulnerability’s unauthenticated nature means attackers can exploit it without prior access, increasing the threat surface. Additionally, compromised AI systems could be manipulated to produce incorrect or biased outputs, affecting decision-making processes. The lack of known exploits in the wild provides a window for proactive defense, but the critical severity demands urgent attention.

1. Immediately restrict network access to Horovod services, especially the ElasticRendezvousHandler endpoint, using firewalls or network segmentation to limit exposure to trusted hosts only. 2. Implement strict input validation and filtering on incoming PUT requests to detect and block suspicious base64-encoded payloads that could contain malicious pickle objects. 3. Monitor logs for unusual PUT requests or deserialization attempts and set up alerts for anomalous activity targeting the KVStoreHandler endpoints. 4. Where possible, disable or replace the use of cloudpickle deserialization in Horovod workflows until a patch is available. 5. Deploy runtime application self-protection (RASP) or endpoint detection and response (EDR) tools to detect and prevent execution of unauthorized code on servers running Horovod. 6. Engage with Horovod maintainers or community to obtain patches or updates addressing this vulnerability as soon as they become available. 7. Conduct a thorough security review of all machine learning infrastructure to identify and remediate similar deserialization risks. 8. Educate development and operations teams about the dangers of insecure deserialization and enforce secure coding practices in AI/ML pipelines.