CVE-2026-28288
Dify ≤ 1.9.0
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:N/VA:N/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-2026-28288 is a medium-severity Observable Response Discrepancy (CWE-204) vulnerability in Dify Dify. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Account Discovery (T1087); 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 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-2026-9068
Vulnerability Data
Dify is an open-source LLM app development platform. Prior to 1.9.0, responses from the Dify API to existing and non-existent accounts differ, allowing an attacker to enumerate email addresses registered with Dify. Version 1.9.0 fixes the 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: dify, llm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V13.4.5
Mitigating Controls (NIST 800-53 r5) AI
Obscuring authentication feedback directly stops one common source of observable response discrepancies.
Error handling explicitly requires messages that avoid revealing exploitable internal information.
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.