Langflow: Prompt injection in Langflow Smart Transform can lead to code execution

Summary

Langflow versions 1.3.0 through 1.10.2 contain a code-injection vulnerability in the Smart Transform (LambdaFilterComponent) component.

Smart Transform places flow-author instructions and a preview of its input data into a prompt asking an LLM to generate a Python lambda. It then extracts a one-line lambda from the model response, applies only syntactic format checks, evaluates it with Python's full builtins, and invokes the resulting function inside the Langflow process.

A malicious flow author can exploit this directly through the Instructions field. In deployments where an exposed flow passes attacker-controlled content into Smart Transform, an attacker may also exploit it indirectly through prompt injection, subject to the configured model following the injected instruction.

Vulnerability details

Vulnerable Code Location: src/lfx/src/lfx/components/llm_operations/lambda_filter.py (line 242 in v1.10.2)

def _validate_lambda(self, lambda_text: str) -> bool:
    """Validate the provided lambda function text."""
    return lambda_text.strip().startswith("lambda") and ":" in lambda_text

# ...
return eval(lambda_text)  # noqa: S307

For example, an attacker can attempt to make the model return:

lambda x: __import__("os").system("id")

This expression satisfies the vulnerable format checks. eval() creates the lambda with access to Python's default builtins, and the subsequent fn(data) invocation (in _execute_lambda) executes the command.

Successful exploitation allows code execution with the privileges of the Langflow service process. This can expose or modify credentials, files, application data, and network resources accessible to that process, and may affect other tenants in shared deployments.

PoC

https://github.com/user-attachments/assets/13c48fe1-7225-4e0d-9687-2d2df1e87f0e