CVE-2026-123203 in NEURON
Summary
by MITRE • 11/05/2026
NEURON (CoreNEURON) through 9.0.2 contains a stack buffer overflow vulnerability that allows attackers to corrupt stack memory by supplying over-long tokens in a crafted model dataset. Attackers can place oversized version or mechanism-name tokens in bbcore_mech.dat, globals.dat, or files.dat loaded by nrniv-core to crash the process.
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Analysis
by VulDB Data Team • 11/05/2026
The NEURON simulation platform, specifically its CoreNEURON component through version 9.0.2, contains a critical stack buffer overflow vulnerability that poses significant risks to system stability and potential remote code execution. This flaw arises from insufficient validation of input data lengths when processing model dataset files such as bbcore_mech.dat, globals.dat, or files.dat. These files are loaded by the nrniv-core executable during simulation initialization, where they define mechanism parameters and global variables essential for neural network modeling. The vulnerability is triggered when an attacker supplies tokens that exceed the allocated stack buffer size, leading to memory corruption within the application's execution context.
From a technical perspective, this issue represents a classic CWE-121: Stack-based Buffer Overflow condition. When the parser encounters oversized version or mechanism-name tokens in the specified data files, it fails to perform adequate bounds checking before copying these strings into fixed-size stack buffers. This lack of input validation allows external entities to write beyond the boundaries of the intended memory space. In a typical execution environment, this overflow can overwrite adjacent stack variables, including return addresses and saved frame pointers. Such corruption disrupts the normal control flow of the program, potentially allowing an attacker to redirect execution to malicious shellcode or exploit existing gadgets within the binary for arbitrary code execution.
The operational impact of this vulnerability is severe, particularly in research environments where NEURON is used extensively for computational neuroscience simulations. An unauthenticated attacker who can provide a crafted model dataset could cause the simulation process to crash immediately due to segmentation faults resulting from memory corruption. Beyond denial of service, if the environment allows for controlled execution conditions or specific compiler configurations that do not mitigate stack overflows effectively, this flaw could be leveraged to execute arbitrary code with the privileges of the user running the NEURON application. This is particularly concerning in shared computing clusters where multiple users may submit jobs using potentially untrusted model files from external collaborators or public repositories.
This vulnerability aligns with MITRE ATT&CK technique T1203, which covers Exploitation for Client Execution, as attackers might distribute maliciously crafted NEURON model files to unsuspecting researchers. The attack vector is primarily local file inclusion or remote delivery via email or shared drives rather than direct network exploitation, but the impact remains high due to the potential for code execution in privileged research environments. Mitigation strategies should focus on immediate patching of the CoreNEURON component to version 9.0.3 or later where this issue has been addressed through improved input validation and buffer size checks. Additionally, organizations deploying NEURON should enforce strict data integrity policies, ensuring that only verified model datasets are loaded into production simulation environments. Implementing sandboxed execution contexts for untrusted inputs can further reduce the risk of successful exploitation by limiting the privileges available to the compromised process.