Summary
Langflow has Authenticated Code Execution in Agentic Assistant Validation
Description
1. Summary
The Agentic Assistant feature in Langflow executes LLM-generated Python code during its validation phase. Although this phase appears intended to validate generated component code, the implementation reaches dynamic execution sinks and instantiates the generated class server-side.
In deployments where an attacker can access the Agentic Assistant feature and influence the model output, this can result in arbitrary server-side Python execution.
2. Description
2.1 Intended Functionality
The Agentic Assistant endpoints are designed to help users generate and validate components for a flow. Users can submit requests to the assistant, which returns candidate component code for further processing.
A reasonable security expectation is that validation should treat model output as untrusted text and perform only static or side-effect-free checks.
The externally reachable endpoints are:
The request model accepts attacker-influenceable fields such as input_value, flow_id, provider, model_name, session_id, and max_retries:
2.2 Root Cause
In the affected code path, Langflow processes model output through the following chain:
/assist
→ execute_flow_with_validation()
→ execute_flow_file()
→ LLM returns component code
→ extract_component_code()
→ validate_component_code()
→ create_class()
→ generated class is instantiated
The assistant service reaches the validation path here:
The code extraction step occurs here:
The validation entry point is here:
The issue is that this validation path is not purely static. It ultimately invokes create_class() in lfx.custom.validate, where Python code is dynamically executed via exec(...), including both global-scope preparation and class construction.
As a result, LLM-generated code is treated as executable Python rather than inert data. This means the “validation” step crosses a trust boundary and becomes an execution sink.
The streaming path can also reach this sink when the request is classified into the component-generation branch:
3. Proof of Concept (PoC)
- Send a request to the Agentic Assistant endpoint.
- Provide input that causes the model to return malicious component code.
- The returned code reaches the validation path.
- During validation, the server dynamically executes the generated Python.
- Arbitrary server-side code execution occurs.
4. Impact
Attackers who can access the Agentic Assistant feature and influence model output may execute arbitrary Python code on the server.
This can lead to:
- OS command execution
- file read/write
- credential or secret disclosure
- full compromise of the Langflow process
5. Exploitability Notes
This issue is most accurately described as an authenticated or feature-reachable code execution vulnerability, rather than an unconditional unauthenticated remote attack.
Severity depends on deployment model:
- In local-only, single-user development setups, the issue may be limited to self-exposure by the operator.
- In shared, team, or internet-exposed deployments, it may be exploitable by other users or attackers who can reach the assistant feature.
The assistant feature depends on an active user context:
Authentication sources include bearer token, cookie, or API key:
Default deployment settings may widen exposure, including AUTO_LOGIN=true and the /api/v1/auto_login endpoint:
6. Patch Recommendation
- Remove all dynamic execution from the validation path.
- Ensure validation is strictly static and side-effect-free.
- Treat all LLM output as untrusted input.
- If code generation must be supported, require explicit approval and run it in a hardened sandbox isolated from the main server process.
Discovered by: @kexinoh (https://github.com/kexinoh, works at Tencent Zhuque Lab)
Impact
Untrusted input is evaluated as executable code within the application's runtime environment. Typical impact: arbitrary code execution within the application's privilege context.
Affected versions
Security releases
Kodem intelligence
Severity tells you how bad this could be in the worst case. It does not tell you whether you are exposed. Exploitability and impact are functions of runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A vulnerable package can sit in your dependency tree and never run.
Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter. Kodem's runtime-powered SCA identifies whether this CVE is reachable in your applications.
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Frequently Asked Questions
- What is CVE-2026-33873? CVE-2026-33873 is a critical-severity code injection vulnerability in langflow (pip), affecting versions <= 1.8.1. It is fixed in 1.9.0. Untrusted input is evaluated as executable code within the application's runtime environment.
- Which versions of langflow are affected by CVE-2026-33873? langflow (pip) versions <= 1.8.1 is affected.
- Is there a fix for CVE-2026-33873? Yes. CVE-2026-33873 is fixed in 1.9.0. Upgrade to this version or later.
- Is CVE-2026-33873 exploitable, and should I be worried? Whether CVE-2026-33873 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
- What actually determines whether CVE-2026-33873 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
- How do I fix CVE-2026-33873? Upgrade
langflowto 1.9.0 or later.