CVE-2026-40351
Fastgpt ≤ 4.14.9.5
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-40351 is a critical-severity Improper Neutralization of Special Elements in Data Query Logic (CWE-943) vulnerability in Fastgpt Fastgpt. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 47th 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 LLM Application Platforms; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to 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-2026-40351 is a NoSQL injection vulnerability in FastGPT, an AI Agent building platform, affecting versions prior to 4.14.9.5. The issue occurs in the password-based login endpoint, where TypeScript type assertion is applied without runtime validation of the input. This flaw enables an attacker to submit a MongoDB query operator object, such as {"$ne": ""}, in the password field, bypassing the authentication check entirely. The vulnerability is classified under CWE-943 and carries a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H).
An unauthenticated attacker can exploit this vulnerability remotely over the network with low complexity and no user interaction or privileges required. By crafting a malicious password payload, the attacker bypasses login validation and gains unauthorized access as any user account, including the root administrator, potentially compromising the entire platform.
The vulnerability has been fixed in FastGPT version 4.14.9.5. Security advisories recommend upgrading to this patched version to mitigate the issue. Key references include the fixing commit at https://github.com/labring/FastGPT/commit/bd966d479fbe414d02679cf79f9eaaab3d100a2d, the release notes at https://github.com/labring/FastGPT/releases/tag/v4.14.9.5, and the GitHub security advisory at https://github.com/labring/FastGPT/security/advisories/GHSA-x8mx-2mr7-h9xg.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-23557
Vulnerability Data
FastGPT is an AI Agent building platform. In versions prior to 4.14.9.5, the password-based login endpoint uses TypeScript type assertion without runtime validation, allowing an unauthenticated attacker to pass a MongoDB query operator object (e.g., {"$ne": ""}) as the password…
more
field. This NoSQL injection bypasses the password check, enabling login as any user including the root administrator. This issue has been fixed in version 4.14.9.5.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly stops unneutralized special elements from reaching query logic.
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 parameterized queries and input neutralization to prevent query-logic injection.
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 but does not itself implement the fix.
Secure development life cycle mandates input validation and query parameterization that directly prevent injection flaws.
Application security requirements explicitly call for controls against injection and improper query construction.
Secure architecture principles reduce the likelihood of query-logic flaws but do not prescribe the specific coding practice.
Secure coding standards require proper neutralization of special elements in all data queries.
Outsourced development agreements can require secure coding practices, indirectly mitigating the weakness.