CVE-2026-9077
Langflow 1.0.0 – 1.11.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:L/I:H/A:NSummary
CVE-2026-9077 is a high-severity Reliance on Untrusted Inputs in a Security Decision (CWE-807) vulnerability in Langflow Langflow. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 23th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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 AC-24 (Access Control Decisions) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-53463
Vulnerability Data
IBM Langflow OSS 1.0.0 through 1.10.3 Langflow allows remote authenticated attackers to bypass localhost-only restrictions and write arbitrary MCP server configurations to IDE configuration files on the host system.
- 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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- 1 hardening rule · 1 OS baseline
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Mitigating Controls (NIST 800-53 r5) AI
Explicitly requires that every access decision be based on authoritative, protected decision data rather than caller-supplied inputs.
Input validation can reject malformed or attacker-controlled values before they reach a security decision point.
Access enforcement requires decisions to be made from trusted policy data rather than modifiable client-supplied inputs.
Information-flow enforcement applies rules to validated, internal attributes instead of untrusted external values.
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.
Strong authentication mechanisms directly prevent security decisions from depending on modifiable, untrusted inputs.
Verification and protection of identity assertions stops reliance on attacker-controlled values for authorization decisions.
Proper management of identities/credentials reduces the chance that security decisions will be driven by untrusted inputs.
Policy-driven, least-privilege authorization reduces opportunities to bypass controls via tampered inputs.
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.
Secure coding standards directly prohibit using untrusted inputs for security-critical decisions.
Security testing in development catches input-validation flaws before deployment.
Monitoring can detect exploitation but does not stop the underlying weakness.
Application security requirements explicitly call for validation of inputs used in security decisions.
Secure architecture principles mandate treating all external inputs as untrusted.
Access-control rules can be bypassed if they rely on untrusted inputs.