CVE-2026-70477 in Flowise
Summary
by MITRE • 08/04/2026
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.
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Analysis
by VulDB Data Team • 08/04/2026
The vulnerability in Flowise versions prior to 3.1.3 represents a critical prompt injection flaw within the CSV Agent node functionality that enables remote code execution through improper input validation. This security weakness exists in the run method of the CSV_Agents class where user-supplied data is directly incorporated into LLM prompts without adequate sanitization or validation mechanisms. The flaw allows attackers to inject malicious payloads that bypass existing blocklist validators, creating a dangerous pathway for arbitrary code execution within the application's environment.
The technical implementation of this vulnerability stems from insufficient input validation processes that fail to properly sanitize user-provided data before it is processed by the LLM engine. When a prompt injection occurs through the CSV Agent node, the system constructs an LLM prompt using untrusted input while the resulting pythonCode undergoes validation through validatePythonCodeForDataFrame function. However, this validation mechanism proves inadequate in detecting malicious payloads that can exploit the unsandboxed Pyodide environment where code executes with elevated privileges.
The operational impact of this vulnerability extends beyond simple code execution to encompass full system compromise through the service account context. Attackers can leverage this flaw to execute arbitrary Python scripts with the same permissions as the Flowise service, potentially enabling data exfiltration, system reconnaissance, or further lateral movement within the compromised environment. The unsandboxed nature of the Pyodide execution environment amplifies the risk significantly, as malicious code operates without the typical restrictions that would normally prevent harmful system interactions.
This vulnerability aligns with CWE-94, which describes improper validation of dangerous commands in interpreted languages, and maps to ATT&CK technique T1059.001 for command and scripting interpreter. The flaw demonstrates a classic injection vulnerability where untrusted data flows directly into execution contexts without proper sanitization. The specific implementation in the CSV_Agents class represents a failure in input validation and output encoding practices that should prevent such dangerous code execution patterns from occurring.
Organizations using Flowise versions prior to 3.1.3 should immediately implement mitigations including thorough input validation, enhanced prompt sanitization, and restricted execution environments for code generation processes. The fix in version 3.1.3 addresses the core issue by strengthening the validation mechanisms within the validatePythonCodeForDataFrame function and ensuring proper isolation of potentially dangerous code execution paths. Security teams should also consider implementing network segmentation, monitoring for unusual code execution patterns, and regular security assessments to prevent similar vulnerabilities from emerging in other components of their AI-powered applications.