vulnerability / NVI-00013031 / Revision 1
Langflow: Prompt injection in Langflow Smart Transform can lead to code execution
GitHub lists this reviewed advisory for langflow. Published version ranges and source prerequisites are shown below; this is not an inspection of a visitor’s application.
This report has not inspected your inventory, configuration, deployed build, reachability or evidence. A catalog listing and a severity score do not establish exposure or compromise.
01 / Meaning
Why it matters
Published affected context helps an owner decide what evidence to gather. This report has not examined your application.
8.8 high
GitHub source severity; exploitability and local consequence require separate evidence.
As of · GitHub Advisory Database
02 / Applicability
The affected context
Affected versions, configuration prerequisites and known exclusions are public. Confirm each one against the deployed service.
langflow
source listed>= 1.3.0, < 1.10.3
## 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) ```python 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 examp
03 / Response
What to do next
- investigate
Identify the exact component and deployed version, examine the linked publisher’s conditions, and retain evidence of any mitigation.
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04 / Limits
What this report cannot establish
- Customer inventory, deployment, reachability and exploitation have not been inspected.
05 / Provenance
Sources you can inspect
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- GHSA-9fpm-3445-2vx4 ↗
GitHub Advisory Database · vendor advisory
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Public revision history
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- Revision 1 ·
First indexed by Null; source disclosure date is shown separately.