CVE-2026-2008
Abhiphile Fermat ≤ 2025-10-08
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-2008 is a medium-severity Injection (CWE-74) vulnerability in Abhiphile Fermat. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 31th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Protocol-Specific Risks risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-2008 is a code injection vulnerability (CWE-74, CWE-94) in the abhiphile/fermat-mcp project, affecting the eqn_chart function in the file fmcp/mpl_mcp/core/eqn_chart.py. The flaw arises from manipulation of the 'equations' argument and impacts commits up to 47f11def1cd37e45dd060f30cdce346cbdbd6f0a. Published on 2026-02-06, it carries a CVSS v3.1 base score of 6.3 (AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L).
An attacker with low privileges can exploit this vulnerability remotely with low complexity and no user interaction. Successful exploitation enables code injection, resulting in limited impacts to confidentiality, integrity, and availability.
The project employs a rolling release model, providing no specific details on affected or updated versions. It was informed early via GitHub issue #9 but has not responded. An exploit is public, with further details available in the repository at https://github.com/abhiphile/fermat-mcp/, the issue tracker at https://github.com/abhiphile/fermat-mcp/issues/9 and https://github.com/abhiphile/fermat-mcp/issues/9#issue-3837794397, and VulDB at https://vuldb.com/?ctiid.344590 and https://vuldb.com/?id.344590. No patches or mitigations are documented.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-5692
Vulnerability Data
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…
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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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Protocol-Specific Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: mcp
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly require input validation and output encoding that prevent injection flaws.
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development catches injection vulnerabilities before release.
Logging supports detection of injection attempts but does not prevent the weakness.
Monitoring activities can identify active injection attacks after they occur.
Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.
Application security requirements explicitly call for controls against injection attacks in software design.
Secure architecture principles reduce injection surfaces but do not prescribe specific neutralization techniques.