Raw vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-0769 is a critical-severity Eval Injection (CWE-95) vulnerability in Langflow Langflow. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique JavaScript (T1059.007); ranked in the top 2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment 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-2026-0769 is an eval injection vulnerability in the eval_custom_component_code function of Langflow that permits remote code execution. The flaw stems from insufficient validation of user-supplied input before it is passed to Python code execution, enabling an attacker to run arbitrary commands in the context of the affected process. The issue carries a CVSS 3.0 score of 9.8 and is tracked under CWE-95; it was originally reported as ZDI-CAN-26972.
Unauthenticated remote attackers can exploit the vulnerability over the network without user interaction to achieve full code execution on the target system. Successful exploitation grants the attacker the ability to read, modify, or delete data and potentially take full control of the Langflow instance.
The Zero Day Initiative advisory ZDI-26-035 provides further details on the issue. The EPSS score rose from a low baseline to a peak of 0.0295, indicating emerging exploitation interest after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-4475
Vulnerability Data
Langflow eval_custom_component_code Eval Injection Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Langflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the implementation of eval_custom_component_code function.…
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The issue results from the lack of proper validation of a user-supplied string before using it to execute python code. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-26972.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: langflow
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and code analysis can discover eval-injection flaws but does not stop their introduction.
Input validation explicitly requires neutralizing untrusted data before it reaches dynamic evaluation constructs such as eval.
Secure-development standards and tools can mandate safe coding patterns that avoid unsafe dynamic evaluation.
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 input neutralization and avoidance of unsafe dynamic evaluation.
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 can detect eval injection vulnerabilities before deployment.
Secure development life cycle mandates input validation and safe coding practices that directly prevent eval injection.
Application security requirements include rules against dynamic code execution of untrusted input.
Secure architecture principles discourage unsafe dynamic evaluation constructs.
Secure coding explicitly requires neutralization of input before dynamic evaluation, directly mitigating eval injection.
Separation of environments limits the blast radius if eval injection occurs in non-production.