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CVE-2026-33873 langflow

CVE-2026-33873 is a Code Injection vulnerability in langflow. A fix is available for langflow — see the affected versions and patch details below.

Langflow has Authenticated Code Execution in Agentic Assistant Validation

Also known asGHSA-v8hw-mh8c-jxfcPYSEC-2026-82
Published
Mar 27, 2026
Updated
Aug 12, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Sep 21, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • A successful exploit gives an attacker total control of the affected component, not partial access.
  • CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for CVE-2026-33873.

EPSS Exploitation Probability

via FIRST.org ↗
1.4%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs71th percentile — riskier than 71% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

Real-World Exposure

1 pkg affected
🐍langflow

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

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:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/router.py#L252-L297

The request model accepts attacker-influenceable fields such as input_value, flow_id, provider, model_name, session_id, and max_retries:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/schemas.py#L20-L31

2.2 Root Cause

In the affected code path, Langflow processes model output through the following chain:

/assistexecute_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:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L58-L79

The code extraction step occurs here:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/code_extraction.py#L11-L53

The validation entry point is here:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/validation.py#L27-L47

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.

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L241-L272

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L394-L399

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L441-L443

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:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L142-L156

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L259-L300

3. Proof of Concept (PoC)

  1. Send a request to the Agentic Assistant endpoint.
  2. Provide input that causes the model to return malicious component code.
  3. The returned code reaches the validation path.
  4. During validation, the server dynamically executes the generated Python.
  5. 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:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/utils/core.py#L38

Authentication sources include bearer token, cookie, or API key:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L39-L53

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L156-L163

Default deployment settings may widen exposure, including AUTO_LOGIN=true and the /api/v1/auto_login endpoint:

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/services/settings/auth.py#L71-L87

https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/v1/login.py#L96-L135

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)

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIlangflowall versions1.9.0pip install --upgrade 'langflow==1.9.0'

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for langflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update langflow to 1.9.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-33873 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like CVE-2026-33873 can be triaged on real exposure rather than presence alone.

Tailored to CVE-2026-33873. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

## 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 gen
O3 Security · Impact-Aware SCA

Is CVE-2026-33873 in your dependencies?

O3 Security finds CVE-2026-33873 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

CVE-2026-33873: langflow Command Injection | O3 Security