EUVD-2026-19021Severity by source
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Primary rating from Vendor (VulDB) · only source for this CVE.
CVSS VectorVendor: VulDB
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Lifecycle Timeline
3DescriptionCVE.org
A weakness has been identified in FedML-AI FedML up to 0.8.9. Affected is the function sendMessage of the file grpc_server.py of the component gRPC server. Executing a manipulation can lead to deserialization. The attack may be performed from remote. The vendor was contacted early about this disclosure but did not respond in any way.
AnalysisAI
Unsafe deserialization in FedML-AI FedML's gRPC server allows unauthenticated remote attackers to achieve confidentiality, integrity, and availability compromise through malicious payloads sent to the sendMessage function in versions up to 0.8.9. EPSS data not available; no CISA KEV listing indicates no confirmed active exploitation at time of analysis. Vendor unresponsive to coordinated disclosure attempts, raising concerns about patch availability and ongoing risk for production deployments of this federated machine learning framework.
Technical ContextAI
FedML is a federated learning framework that uses gRPC for distributed machine learning communication between clients and servers. The vulnerability resides in the sendMessage function within grpc_server.py, which implements the gRPC service endpoint. CWE-502 (Deserialization of Untrusted Data) indicates the application accepts serialized objects from remote sources without proper validation or sanitization. In Python environments, unsafe deserialization commonly involves pickle, YAML, or other serialization libraries that can execute arbitrary code during object reconstruction. The gRPC protocol operates over HTTP/2, and the affected component likely deserializes message payloads without verifying their origin or content integrity. Given FedML's distributed architecture for coordinating machine learning training across multiple nodes, the gRPC server represents a critical attack surface that accepts external connections from federated learning participants.
RemediationAI
No vendor-released patch identified at time of analysis, as the vendor did not respond to coordinated disclosure attempts per the CVE description. Organizations running FedML versions 0.8.9 or earlier should implement immediate compensating controls including network segmentation to restrict gRPC server access to trusted federated learning participants only, deployment of web application firewalls or reverse proxies with deserialization attack signatures, and comprehensive logging of all gRPC endpoint activity for detection of exploitation attempts. Monitor the official FedML GitHub repository and security advisories for patch releases addressing CVE-2026-5536. Consider migrating to alternative federated learning frameworks if vendor security responsiveness remains inadequate. If continued operation is required, implement strict input validation and consider replacing vulnerable deserialization routines with safer alternatives like JSON schema validation. Review firewall rules to ensure gRPC ports are not exposed to untrusted networks, and deploy intrusion detection signatures for CWE-502 exploitation patterns. Consult VulDB intelligence at https://vuldb.com/vuln/355289/cti for ongoing threat intelligence updates.
Same weakness CWE-502 – Deserialization of Untrusted Data
View allSame technique Deserialization
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External POC / Exploit Code
Leaving vuln.today