CVE-2026-64201 in LabVIEW
Summary
by MITRE • 08/25/2026
There is a memory corruption vulnerability recently discovered in NI LabVIEW that may result in information disclosure or arbitrary code execution. Successful exploitation requires an attacker to get a user to open a specially crafted VI. This vulnerability affects NI LabVIEW 2026 Q3 (26.3.0) and prior versions.
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Analysis
by VulDB Data Team • 08/25/2026
The National Instruments LabVIEW platform, widely utilized in engineering and scientific applications for graphical programming and data acquisition, contains a critical memory corruption flaw that poses significant risks to system integrity and confidentiality. This vulnerability stems from improper handling of specific file structures within Virtual Instrument (VI) files, which are the native project format for LabVIEW environments. When an application processes these specially crafted VI files, it fails to correctly validate or bound-check data during parsing operations, leading to a state where memory buffers are written beyond their allocated limits. This type of error is characteristic of CWE-120 Buffer Overflow and potentially CWE-787 Out-of-bounds Write vulnerabilities, depending on the specific internal mechanics of the heap or stack manipulation involved in the processing routine.
The operational impact of this flaw is severe because it allows for two distinct attack vectors: information disclosure and arbitrary code execution. In scenarios where memory corruption results in reading unintended memory regions, an attacker can extract sensitive data such as cryptographic keys, user credentials, or proprietary intellectual property stored within the application's address space. More critically, if the corrupted memory location controls program flow pointers or function return addresses, a sophisticated exploit could redirect execution to malicious shellcode injected into the process memory. This capability effectively grants the attacker full control over the affected system, assuming they can bypass modern mitigations such as Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR), which are standard in contemporary operating systems but may be circumvented through advanced techniques like return-oriented programming or information leakage to determine memory layouts.
Exploitation of this vulnerability is not fully automated against a remote target due to the specific interaction model required. An attacker must persuade a user with access to LabVIEW to open a maliciously constructed VI file. This reliance on social engineering places the attack within the ATT&CK framework under techniques such as T1566 Phishing or T1059 Command and Scripting Interpreter if the payload involves script-based execution post-exploitation. The requirement for user interaction means that while the technical severity is high, the likelihood of exploitation depends heavily on the target's security awareness and the context in which they receive the file. However, given the professional nature of LabVIEW users who often work with shared or downloaded modules from third parties, this risk factor remains significant in enterprise environments where supply chain integrity might be compromised.
To mitigate these risks, organizations must immediately apply vendor-provided patches to update NI LabVIEW to version 26.3.1 or later, which addresses the underlying memory management errors. Until patching is feasible, administrators should enforce strict application whitelisting policies that prevent unauthorized execution of untrusted binaries and restrict the ability of user applications to write executable code in non-standard memory regions. Additionally, implementing network segmentation can limit lateral movement if a single endpoint is compromised. Security teams should also monitor for anomalous process behavior, such as unexpected child processes spawned by LabVIEW or unusual memory allocation patterns, which may indicate an active exploitation attempt. Regular security awareness training focused on identifying suspicious file attachments and verifying the source of downloaded engineering modules is essential to reduce the success rate of this social engineering prerequisite.