CVE-2026-33873: Langflow Langflow Has Authenticated Code Execution Vulnerability
CRITICALCVE-2026-33873 is a CRITICAL langflow has authenticated code execution vulnerability (CWE-94: Improper Control of Generation of Code ('Code Injection')) affecting Langflow libraries in the PyPI ecosystem. The issue involves langflow has authenticated code execution in agentic assistant validation. Remediation is available by upgrading affected packages to version 1.9.0.
CVE-2026-33873 at a Glance
How to Fix CVE-2026-33873
To remediate this vulnerability, upgrade all affected Langflow dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.
pip install langflow>=1.9.0
Run the following command in your project directory to verify that all resolved dependencies use the patched release:
pip show langflow
How to Check if You Are Affected
To determine whether your Python environment is affected by CVE-2026-33873, inspect your installed packages using pip:
pip show langflow
Analyze the terminal output to locate any version of Langflow in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 1.9.0), your application is affected.
Note: The pip show langflow command checks currently installed packages in your active Python environment.
Technical Details
Vulnerability Mechanism & Technical Analysis
This vulnerability is classified under CWE-94: Improper Control of Generation of Code ('Code Injection'), where the product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment. In Langflow, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.
Source Advisory Statement
CVE-2026-33873 Affected Langflow Versions
1 component(s)| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
langflow
|
PyPI |
< 1.9.0
|
1.9.0
|
References
Frequently Asked Questions
What is CVE-2026-33873?
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What version fixes CVE-2026-33873?
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Machine-readable OSV JSON
OSV v1.5 Schema
{
"schema_version" : "1.4.0",
"id" : "GHSA-v8hw-mh8c-jxfc",
"modified" : "2026-06-06T00:56:57Z",
"published" : "2026-03-26T18:31:36Z",
"aliases" : [ "CVE-2026-33873" ],
"summary" : "Langflow has Authenticated Code Execution in Agentic Assistant Validation",
"details" : "## Description\n\n### 1. Summary\n\nThe 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.\n\nIn deployments where an attacker can access the Agentic Assistant feature and influence the model output, this can result in arbitrary server-side Python execution.\n\n### 2. Description\n\n#### 2.1 Intended Functionality\n\nThe 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.\n\nA reasonable security expectation is that validation should treat model output as **untrusted text** and perform only static or side-effect-free checks.\n\nThe externally reachable endpoints are:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/router.py#L252-L297](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/router.py#L252-L297)\n\nThe request model accepts attacker-influenceable fields such as `input_value`, `flow_id`, `provider`, `model_name`, `session_id`, and `max_retries`:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/schemas.py#L20-L31](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/schemas.py#L20-L31)\n\n#### 2.2 Root Cause\n\nIn the affected code path, Langflow processes model output through the following chain:\n\n`/assist`\n→ `execute_flow_with_validation()`\n→ `execute_flow_file()`\n→ LLM returns component code\n→ `extract_component_code()`\n→ `validate_component_code()`\n→ `create_class()`\n→ generated class is instantiated\n\nThe assistant service reaches the validation path here:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L58-L79](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L58-L79)\n\nThe code extraction step occurs here:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/code_extraction.py#L11-L53](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/code_extraction.py#L11-L53)\n\nThe validation entry point is here:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/validation.py#L27-L47](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/validation.py#L27-L47)\n\nThe 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.\n\n[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#L241-L272)\n\n[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#L394-L399)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L441-L443](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L441-L443)\n\nAs 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.\n\nThe streaming path can also reach this sink when the request is classified into the component-generation branch:\n\n[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#L142-L156)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L259-L300](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L259-L300)\n\n### 3. Proof of Concept (PoC)\n\n1. Send a request to the Agentic Assistant endpoint.\n2. Provide input that causes the model to return malicious component code.\n3. The returned code reaches the validation path.\n4. During validation, the server dynamically executes the generated Python.\n5. Arbitrary server-side code execution occurs.\n\n### 4. Impact\n\n* Attackers who can access the Agentic Assistant feature and influence model output may execute arbitrary Python code on the server.\n* This can lead to:\n\n * OS command execution\n * file read/write\n * credential or secret disclosure\n * full compromise of the Langflow process\n\n### 5. Exploitability Notes\n\nThis issue is most accurately described as an **authenticated or feature-reachable code execution vulnerability**, rather than an unconditional unauthenticated remote attack.\n\nSeverity depends on deployment model:\n\n* In **local-only, single-user development setups**, the issue may be limited to self-exposure by the operator.\n* In **shared, team, or internet-exposed deployments**, it may be exploitable by other users or attackers who can reach the assistant feature.\n\nThe assistant feature depends on an active user context:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/utils/core.py#L38](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/utils/core.py#L38)\n\nAuthentication sources include bearer token, cookie, or API key:\n\n[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#L39-L53)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L156-L163](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L156-L163)\n\nDefault deployment settings may widen exposure, including `AUTO_LOGIN=true` and the `/api/v1/auto_login` endpoint:\n\n[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/lfx/src/lfx/services/settings/auth.py#L71-L87)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/v1/login.py#L96-L135](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/v1/login.py#L96-L135)\n\n### 6. Patch Recommendation\n\n* Remove all dynamic execution from the validation path.\n* Ensure validation is strictly static and side-effect-free.\n* Treat all LLM output as untrusted input.\n* If code generation must be supported, require explicit approval and run it in a hardened sandbox isolated from the main server process.\n\nDiscovered by: @kexinoh ([https://github.com/kexinoh](https://github.com/kexinoh), works at Tencent Zhuque Lab)",
"severity" : [ {
"type" : "CVSS_V4",
"score" : "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:H/SI:H/SA:N"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "langflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "1.9.0"
} ]
} ],
"database_specific" : {
"last_known_affected_version_range" : "<= 1.8.1"
}
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/langflow-ai/langflow/security/advisories/GHSA-v8hw-mh8c-jxfc"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2026-33873"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/langflow/PYSEC-2026-82.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/services/settings/auth.py#L71-L87"
}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L441-L443"
}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L394-L399"
}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L241-L272"
}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L39-L53"
}, {
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}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/v1/login.py#L96-L135"
}, {
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}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L58-L79"
}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L259-L300"
}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L142-L156"
}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/validation.py#L27-L47"
}, {
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"url" : "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/code_extraction.py#L11-L53"
}, {
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}, {
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}, {
"type" : "PACKAGE",
"url" : "https://github.com/langflow-ai/langflow"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-94" ],
"severity" : "CRITICAL",
"github_reviewed" : true,
"github_reviewed_at" : "2026-03-26T18:31:36Z",
"nvd_published_at" : "2026-03-27T21:17:23Z"
}
}