CVE-2026-52759
Received Received - Intake
Memory Allocation Flaw in Ghidra Causes DoS

Publication date: 2026-06-10

Last updated on: 2026-06-10

Assigner: VulnCheck

Description
Ghidra before 12.1.1 contains an uncontrolled memory allocation vulnerability in the Mach-O binary parser that allows attackers to cause denial of service. An attacker can supply a crafted Mach-O binary with an arbitrarily large ncmds load command count value, forcing the parser to allocate excessive heap memory without validating file size, crashing the Ghidra JVM.
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Published
2026-06-10
Last Modified
2026-06-10
Generated
2026-06-10
AI Q&A
2026-06-10
EPSS Evaluated
N/A
NVD
EUVD
Affected Vendors & Products
Showing 1 associated CPE
Vendor Product Version / Range
national_security_agency ghidra to 12.1.1 (exc)
Helpful Resources
Exploitability
CWE
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KEV
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CWE ID Description
CWE-789 The product allocates memory based on an untrusted, large size value, but it does not ensure that the size is within expected limits, allowing arbitrary amounts of memory to be allocated.
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Executive Summary

CVE-2026-52759 is a denial of service vulnerability in Ghidra versions before 12.1.1, specifically in its Mach-O binary parser.

The vulnerability occurs because the parser reads an attacker-controlled value called ncmds (load command count) from a Mach-O file header and uses it to allocate memory without verifying if the file size is sufficient.

An attacker can craft a Mach-O binary with an excessively large ncmds value, causing the parser to allocate an extremely large amount of heap memory, which leads to rapid exhaustion of the Java heap and crashes the Ghidra JVM.

This flaw exists in the MachHeader.parse() method where the loop iterates based on the unvalidated ncmds value, resulting in uncontrolled memory allocation.

Impact Analysis

Exploitation of this vulnerability causes Ghidra to crash due to an OutOfMemoryError, resulting in denial of service.

This leads to immediate application crashes, loss of unsaved work, and disruption of automated malware analysis pipelines.

Both the GUI and headless analyzer modes of Ghidra are affected, so any use of the tool to analyze Mach-O binaries can be interrupted.

Detection Guidance

This vulnerability manifests when Ghidra attempts to parse a Mach-O binary with an excessively large ncmds load command count value, causing the Java Virtual Machine (JVM) to crash due to uncontrolled memory allocation.

Detection can be performed by monitoring for unexpected crashes or OutOfMemoryError exceptions in Ghidra logs or during automated analysis pipelines when processing Mach-O files.

Since the issue is triggered by crafted Mach-O files with large ncmds values, you can detect potential exploitation attempts by inspecting Mach-O binaries before analysis.

  • Use tools like 'otool -l <file>' on macOS or 'readelf' on Linux to inspect the load commands count (ncmds) in Mach-O binaries.
  • Check Ghidra logs for JVM crashes or OutOfMemoryError messages during Mach-O file parsing.
  • Monitor automated analysis systems for sudden failures or crashes when processing Mach-O files.
Mitigation Strategies

The primary mitigation is to upgrade Ghidra to version 12.1.1 or later, where this vulnerability has been fixed.

Until an upgrade is possible, avoid analyzing untrusted or suspicious Mach-O binaries with Ghidra, especially those that may contain manipulated ncmds values.

Implement input validation or pre-processing steps to verify the ncmds field in Mach-O files before feeding them into Ghidra.

Monitor Ghidra usage and logs for signs of crashes or memory exhaustion to quickly detect exploitation attempts.

Compliance Impact

The vulnerability in Ghidra causes a denial of service by crashing the application through uncontrolled memory allocation. This results in loss of availability of the software and disruption of automated malware analysis pipelines.

However, there is no information provided in the available resources or CVE description about any direct impact on data confidentiality, integrity, or privacy that would relate to compliance with standards such as GDPR or HIPAA.

Therefore, based on the provided information, this vulnerability primarily affects availability but does not explicitly affect compliance with common data protection regulations.

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