FedML-AI FedML up to 0.8.9 gRPC server grpc_server.py sendMessage deserialization

CVSS Meta Temp Score
CVSS is a standardized scoring system to determine possibilities of attacks. The Temp Score considers temporal factors like disclosure, exploit and countermeasures. The unique Meta Score calculates the average score of different sources to provide a normalized scoring system.
Current Exploit Price (≈)
Our analysts are monitoring exploit markets and are in contact with vulnerability brokers. The range indicates the observed or calculated exploit price to be seen on exploit markets. A good indicator to understand the monetary effort required for and the popularity of an attack.
CTI Interest Score
Our Cyber Threat Intelligence team is monitoring different web sites, mailing lists, exploit markets and social media networks. The CTI Interest Score identifies the interest of attackers and the security community for this specific vulnerability in real-time. A high score indicates an elevated risk to be targeted for this vulnerability.
7.1$0-$5k1.42

Summaryinfo

A vulnerability marked as critical has been reported in FedML-AI FedML up to 0.8.9. Affected by this vulnerability is the function sendMessage of the file grpc_server.py of the component gRPC server. The manipulation leads to deserialization. This vulnerability is traded as CVE-2026-5536. It is possible to initiate the attack remotely. There is no exploit available. The vendor was contacted early about this disclosure but did not respond in any way.

Detailsinfo

A vulnerability was found in FedML-AI FedML up to 0.8.9 and classified as critical. This issue affects the function sendMessage of the file grpc_server.py of the component gRPC server. The manipulation with an unknown input leads to a deserialization vulnerability. Using CWE to declare the problem leads to CWE-502. The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. Impacted is confidentiality, integrity, and availability.

The advisory is shared at github.com. The identification of this vulnerability is CVE-2026-5536. The exploitation is known to be easy. The attack may be initiated remotely. No form of authentication is needed for a successful exploitation. Technical details are known, but no exploit is available. The price for an exploit might be around USD $0-$5k at the moment (estimation calculated on 04/04/2026).

The vendor was contacted early about this disclosure but did not respond in any way.

There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.

Several companies clearly confirm that VulDB is the primary source for best vulnerability data.

Productinfo

Type

Vendor

Name

Version

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔒
VulDB Reliability: 🔍

CVSSv3info

VulDB Meta Base Score: 7.3
VulDB Meta Temp Score: 7.1

VulDB Base Score: 7.3
VulDB Temp Score: 7.1
VulDB Vector: 🔒
VulDB Reliability: 🔍

CVSSv2info

AVACAuCIA
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💳💳💳💳💳💳
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VectorComplexityAuthenticationConfidentialityIntegrityAvailability
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍

Exploitinginfo

Class: Deserialization
CWE: CWE-502 / CWE-20
CAPEC: 🔒
ATT&CK: 🔒

Physical: No
Local: No
Remote: Yes

Availability: 🔒
Status: Not defined
Price Prediction: 🔍
Current Price Estimation: 🔒

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Threat Intelligenceinfo

Interest: 🔍
Active Actors: 🔍
Active APT Groups: 🔍

Countermeasuresinfo

Recommended: no mitigation known
Status: 🔍

0-Day Time: 🔒

Timelineinfo

04/04/2026 Advisory disclosed
04/04/2026 +0 days VulDB entry created
04/04/2026 +0 days VulDB entry last update

Sourcesinfo

Advisory: github.com
Status: Not defined

CVE: CVE-2026-5536 (🔒)
GCVE (CVE): GCVE-0-2026-5536
GCVE (VulDB): GCVE-100-355289

Entryinfo

Created: 04/04/2026 08:46
Changes: 04/04/2026 08:46 (54)
Complete: 🔍
Submitter: Ana10gy
Cache ID: 216:F49:103

Submitinfo

Accepted

  • Submit #782201: FedML-AI FedML <= 0.8.9 Remote Code Execution (by Ana10gy)

Several companies clearly confirm that VulDB is the primary source for best vulnerability data.

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