CVE-2026-21484
Mintplexlabs Anythingllm ≤ 1.10.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:NSummary
CVE-2026-21484 is a medium-severity Observable Discrepancy (CWE-203) vulnerability in Mintplexlabs Anythingllm. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Account Discovery (T1087); ranked in the top 50% of CVEs by exploit likelihood; 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 Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to IA-6 (Authentication Feedback) and SI-11 (Error Handling) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-0774
Vulnerability Data
AnythingLLM is an application that turns pieces of content into context that any LLM can use as references during chatting. Prior to commit e287fab56089cf8fcea9ba579a3ecdeca0daa313, the password recovery endpoint returns different error messages depending on whether a username exists, so enabling…
more
username enumeration. Commit e287fab56089cf8fcea9ba579a3ecdeca0daa313 fixes this issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: anythingllm, llm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 1 hardening rule · 1 OS baseline
V13.4.5
Mitigating Controls (NIST 800-53 r5) AI
Obscures authentication feedback so that success/failure differences are not observable to attackers.
Requires error messages to avoid revealing exploitable details, directly stopping observable response discrepancies.
Information flow enforcement can block responses that would otherwise disclose internal state to unauthorized parties.
Boundary protection monitors and filters outbound responses that could leak internal state.
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 prevent introduction of inconsistent response behavior that leaks internal state.
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 can detect observable response discrepancies before deployment.
Network security controls can enforce uniform responses and suppress observable discrepancies.
Secure SDLC practices include error-handling and response standardization to avoid information disclosure.
Application security requirements typically mandate consistent, non-revealing error messages.
Secure architecture principles discourage designs that leak internal state via differing responses.
Secure coding standards explicitly require uniform error handling to prevent information leakage.