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Zebra Vulnerable to Consensus Divergence in Transparent Sighash Hash-Type Handling

Critical severity GitHub Reviewed Published Apr 17, 2026 in ZcashFoundation/zebra • Updated Apr 18, 2026

Package

cargo zebra-script (Rust)

Affected versions

< 5.0.1

Patched versions

5.0.1
cargo zebrad (Rust)
< 4.3.1
4.3.1

Description

Consensus Divergence in Transparent Sighash Hash-Type Handling

Summary

After a refactoring, Zebra failed to validate a consensus rule that restricted the possible values of sighash hash types for V5 transactions which were enabled in the NU5 network upgrade. Zebra nodes could thus accept and eventually mine a block that would be considered invalid by zcashd nodes, creating a consensus split between Zebra and zcashd nodes.

In a similar vein, for V4 transactions, Zebra mistakenly used the "canonical" hash type when computing the sighash while zcashd (correctly per the spec) uses the raw value, which could also crate a consensus split.

Severity

Critical - This is a Consensus Vulnerability that could allow a malicious party to induce network partitioning, service disruption, and potential double-spend attacks against affected nodes.

Note that the impact is currently alleviated by the fact that currently most miners run zcashd.

Affected Versions

All Zebra versions prior to version 4.3.1. (Some older versions are not impacted but are no longer supported by the network.)

Description

Verification of transparent transactions inherits the Bitcoin Script verification code in C++. Since it is consensus-critical, this code was called from Zebra through foreign function interface (FFI). That interface was clunky because it required parsing the whole transaction in C++ code, which would then pull Rust libraries which could get in conflict with Zebra code. A refactoring was done so that only the verification itself was done in C++, and the rest done by Rust code, using a callback. However, in this refactoring, it was not noticed that a particular consensus rule - only accepting known hash types in transparent transaction signatures - was being enforced in C++ code and thus had to be enforced by the Rust caller.

An attacker could exploit this by:

  • Submitting a V4 or V5 transaction with an invalid hash type
  • The V5 transaction would be accepted by Zebra nodes but not by zcashd nodes (and vice-versa for V4), creating a consensus split in the network.

Impact

Consensus Failure

  • Attack Vector: Network.
  • Effect: Network partition/consensus split.
  • Scope: Any Zebra affected Zebra node

Fixed Versions

This issue is fixed in Zebra 4.3.1.

The fix adds the consensus check in the caller of the C++ verification code. It also uses the raw hash type for V4 sighash computations.

Mitigation

Users should upgrade to Zebra 4.3.1 or later immediately.

There are no known workarounds for this issue. Immediate upgrade is the only way to ensure the node remains on the correct consensus path and is protected against malicious chain forks.

Credits

Thanks Alex “Scalar” Sol for finding and reporting the issue, and to @sangsoo-osec who independently found the same issue and demonstrated that the V4 variant was also exploitable.

References

@mpguerra mpguerra published to ZcashFoundation/zebra Apr 17, 2026
Published to the GitHub Advisory Database Apr 18, 2026
Reviewed Apr 18, 2026
Last updated Apr 18, 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 None
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality None
Integrity High
Availability High

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:N/VI:H/VA:H/SC:N/SI:H/SA:H

EPSS score

Weaknesses

Improper Following of Specification by Caller

The product does not follow or incorrectly follows the specifications as required by the implementation language, environment, framework, protocol, or platform. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-8m29-fpq5-89jj

Source code

Credits

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