Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-27966 is a critical-severity Code Injection (CWE-94) vulnerability in Langflow Langflow. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 2% 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 LLM/Generative AI 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-2026-27966 is a critical remote code execution vulnerability in Langflow, an open-source tool for building and deploying AI-powered agents and workflows. In versions prior to 1.8.0, the CSV Agent node hardcodes the `allow_dangerous_code=True` parameter, which automatically exposes LangChain's Python REPL tool (`python_repl_ast`). This flaw, classified under CWE-94 (Code Injection) with 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), enables attackers to execute arbitrary Python code and operating system commands on the affected server through prompt injection attacks.
The vulnerability can be exploited by any unauthenticated attacker with network access to the Langflow instance, requiring no privileges, user interaction, or special complexity. Successful exploitation grants full remote code execution (RCE), allowing attackers to run malicious Python scripts or OS commands, potentially leading to complete server compromise, data theft, persistence, or lateral movement within the environment.
The Langflow security advisory (GHSA-3645-fxcv-hqr4) and the fixing commit (d8c6480daa17b2f2af0b5470cdf5c3d28dc9e508) confirm that upgrading to version 1.8.0 resolves the issue by addressing the hardcoded dangerous code allowance in the CSV Agent node.
This vulnerability highlights risks in AI/ML workflow tools like Langflow, where prompt injection can bypass safeguards in agentic systems relying on LangChain components. No public evidence of real-world exploitation has been reported as of the CVE publication on 2026-02-26.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-8819
Vulnerability Data
Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.8.0, the CSV Agent node in Langflow hardcodes `allow_dangerous_code=True`, which automatically exposes LangChain’s Python REPL tool (`python_repl_ast`). As a result, an attacker can execute arbitrary…
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Python and OS commands on the server via prompt injection, leading to full Remote Code Execution (RCE). Version 1.8.0 fixes the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- AI-specific weaknesses CR
- CWE-1426 — Hardcoded dangerous tool flag lets LLM/agent output reach code-execution sink; prompt injection is delivery vector.
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.- Classification Reason
- Matched keywords: ai, langchain, langflow, prompt injection
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
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.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.