CVE-2026-2008 in fermat-mcp
Summary
by MITRE • 02/06/2026
A vulnerability was detected in abhiphile fermat-mcp up to 47f11def1cd37e45dd060f30cdce346cbdbd6f0a. This vulnerability affects the function eqn_chart of the file fmcp/mpl_mcp/core/eqn_chart.py. Performing a manipulation of the argument equations results in code injection. It is possible to initiate the attack remotely. The exploit is now public and may be used. This product is using a rolling release to provide continious delivery. Therefore, no version details for affected nor updated releases are available. The project was informed of the problem early through an issue report but has not responded yet.
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Analysis
by VulDB Data Team • 02/18/2026
The vulnerability identified as CVE-2026-2008 represents a critical code injection flaw within the abhiphile fermat-mcp software library, specifically targeting the eqn_chart function located in fmcp/mpl_mcp/core/eqn_chart.py. This vulnerability arises from inadequate input validation and sanitization mechanisms when processing equation arguments, creating a pathway for malicious code execution. The flaw exists in a component that handles mathematical equation processing, making it particularly dangerous as it could allow attackers to inject arbitrary code that would be executed within the context of the application. The vulnerability's remote exploitability means that attackers can leverage this weakness without requiring physical access to the system, significantly expanding the attack surface and potential impact. The issue affects a rolling release system where version information is not explicitly maintained, complicating the identification of vulnerable and patched states, which creates additional challenges for security teams attempting to assess risk and implement remediation measures.
The technical nature of this vulnerability aligns with CWE-94, which describes "Improper Control of Generation of Code ('Code Injection')" and is consistent with ATT&CK technique T1059.007 for "Command and Scripting Interpreter: Python." The flaw occurs when the eqn_chart function fails to properly validate or sanitize user-supplied equation parameters, allowing malicious input to be interpreted as executable code rather than data. This type of vulnerability typically stems from improper handling of dynamic code execution or string formatting operations where user input is directly incorporated into code execution contexts without adequate sanitization. The rolling release methodology employed by this project further compounds the issue as there are no clear version boundaries to identify when the vulnerability was introduced or fixed, making it difficult for users to determine their risk exposure or implement appropriate security controls.
The operational impact of this vulnerability extends beyond simple code execution, as it represents a potential gateway for more sophisticated attacks within the affected environment. Attackers could leverage this vulnerability to execute arbitrary commands, potentially leading to full system compromise, data exfiltration, or lateral movement within network environments where the software is deployed. The public availability of exploits increases the likelihood of successful attacks, as security teams face the challenge of defending against known attack vectors without clear version information to guide their response. Organizations using this software may be vulnerable to attacks targeting their mathematical equation processing capabilities, which could be particularly concerning in environments where precise mathematical calculations are critical for operations or security monitoring. The lack of response from the project maintainers after early notification creates a security gap that leaves users exposed to potential exploitation.
Mitigation strategies for this vulnerability should prioritize immediate action to address the code injection risk, particularly for organizations that have not yet updated their implementations. The most effective approach involves implementing strict input validation and sanitization measures within the eqn_chart function, ensuring that all user-supplied equation parameters are properly validated before any processing occurs. Organizations should consider applying input whitelisting techniques to restrict equation formats to known safe patterns, while also implementing proper parameter escaping mechanisms to prevent malicious code from being interpreted as executable instructions. Additionally, network segmentation and access controls should be implemented to limit the potential impact of exploitation, particularly in environments where the software is exposed to untrusted inputs. Regular monitoring for exploit attempts and implementing application-level firewalls or intrusion detection systems can help detect and prevent exploitation attempts. The rolling release nature of the project necessitates a proactive approach to security, where organizations should consider implementing their own version control or patch management strategies to ensure they are not running vulnerable code while waiting for official project updates that may not be forthcoming.