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fabric-sdk-java has ObjectInputStream.readObject() without ObjectInputFilter, which allows Java deserialization RCE

Critical severity GitHub Reviewed Published Apr 22, 2026 in hyperledger/fabric • Updated Apr 29, 2026

Package

maven org.hyperledger.fabric-sdk-java:fabric-sdk-java (Maven)

Affected versions

>= 1.0.0, <= 2.2.26

Patched versions

None

Description

Summary

This advisory covers the deprecated fabric-sdk-java client SDK. Channel.java implements readObject() and exposes deSerializeChannel() which call ObjectInputStream.readObject() on untrusted byte arrays without configuring an ObjectInputFilter. This is the classic Java deserialization RCE pattern.

Note: fabric-sdk-java is deprecated and maintained in https://github.com/hyperledger/fabric-sdk-java. Filing here as that repo does not have private vulnerability reporting enabled.

Affected Code

// src/main/java/org/hyperledger/fabric/sdk/Channel.java
private void readObject(ObjectInputStream in) throws IOException, ClassNotFoundException {
    in.defaultReadObject();  // No ObjectInputFilter configured
}

public Channel deSerializeChannel(byte[] channelBytes)
        throws IOException, ClassNotFoundException, InvalidArgumentException {
    ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(channelBytes));
    Channel channel = (Channel) ois.readObject();  // Untrusted bytes deserialized
    return channel;
}

Attack Vector

An attacker who can supply crafted serialized Channel bytes to the client application — for example, by compromising a local channel file, injecting data through an application that accepts Channel bytes from external sources, or exploiting a separate write primitive — can achieve RCE via gadget chain exploitation when deSerializeChannel() processes those bytes. The risk is highest in deployments that accept Channel data from sources outside the client's direct control. Note: channel data is not transmitted from Fabric peers; this is a client-side deserialization surface.

Proof of Concept

// Generate malicious payload with ysoserial:
// java -jar ysoserial.jar CommonsCollections6 "touch /tmp/pwned" > malicious_channel.ser

// Victim code:
byte[] maliciousBytes = Files.readAllBytes(Paths.get("malicious_channel.ser"));
Channel channel = client.deSerializeChannel(maliciousBytes);  // RCE fires here

Notes on Deprecation

fabric-sdk-java is deprecated as of Hyperledger Fabric v2.5 (replaced by org.hyperledger.fabric:fabric-gateway). However, organizations that have not yet migrated remain fully exposed. Automated dependency scanners (Snyk, Dependabot) cannot alert users without a published GHSA. This advisory is filed to ensure those users are notified and directed to migrate.

Fix

For the deprecated SDK: add ObjectInputFilter to whitelist only expected classes:

ObjectInputFilter filter = ObjectInputFilter.Config.createFilter(
    "org.hyperledger.fabric.sdk.*;java.util.*;java.lang.*;!*"
);
ois.setObjectInputFilter(filter);

The recommended remediation is migration to org.hyperledger.fabric:fabric-gateway, which does not use Java serialization.

Resources

Credits

Found via independent security research.

References

@ryjones ryjones published to hyperledger/fabric Apr 22, 2026
Published to the GitHub Advisory Database Apr 29, 2026
Reviewed Apr 29, 2026
Last updated Apr 29, 2026

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(17th percentile)

Weaknesses

Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid. Learn more on MITRE.

CVE ID

CVE-2026-41586

GHSA ID

GHSA-prf8-cf2x-rhx7

Source code

Credits

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