CVE-2026-7700: Langflow Prompt Injection Vulnerability
HIGHCVE-2026-7700 is a HIGH (CVSS 8.8) prompt injection vulnerability (CWE-94: Improper Control of Generation of Code ('Code Injection')) affecting Langflow libraries in the PyPI ecosystem. The issue involves prompt injection in langflow smart transform can lead to code execution. Remediation is available by upgrading affected packages to version 1.10.3.
CVE-2026-7700 at a Glance
Security Impact
CVSS: 8.8
The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H).
How to Fix CVE-2026-7700
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>=1.10.3
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-2026-7700, 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 1.10.3), 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-2026-7700 Affected Langflow Versions
1 component(s)| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
langflow
|
PyPI |
≥ 1.3.0
< 1.10.3
|
1.10.3
|
References
Frequently Asked Questions
What is CVE-2026-7700?
Which Langflow versions are affected?
What version fixes CVE-2026-7700?
Is langflow affected?
What is the CVSS score of CVE-2026-7700?
What CWE is associated with CVE-2026-7700?
How can I check PyPI dependencies for CVE-2026-7700?
Machine-readable OSV JSON
OSV v1.5 Schema
{
"schema_version" : "1.4.0",
"id" : "GHSA-9fpm-3445-2vx4",
"modified" : "2026-10-05T22:30:41Z",
"published" : "2026-10-05T22:30:41Z",
"aliases" : [ "CVE-2026-7700" ],
"summary" : "Langflow: Prompt injection in Langflow Smart Transform can lead to code execution",
"details" : "## Summary\n\nLangflow versions 1.3.0 through 1.10.2 contain a code-injection vulnerability in the Smart Transform (`LambdaFilterComponent`) component.\n\nSmart 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.\n\nA 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.\n\n## Vulnerability details\n\n**Vulnerable Code Location**: `src/lfx/src/lfx/components/llm_operations/lambda_filter.py` (line 242 in v1.10.2)\n\n```python\ndef _validate_lambda(self, lambda_text: str) -> bool:\n \"\"\"Validate the provided lambda function text.\"\"\"\n return lambda_text.strip().startswith(\"lambda\") and \":\" in lambda_text\n\n# ...\nreturn eval(lambda_text) # noqa: S307\n```\n\nFor example, an attacker can attempt to make the model return:\n\n`lambda x: __import__(\"os\").system(\"id\")`\n\nThis 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.\n\nSuccessful 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.\n\n## PoC\n\nhttps://github.com/user-attachments/assets/13c48fe1-7225-4e0d-9687-2d2df1e87f0e\n\n## Fix\n\nThe reported path was addressed by validating the generated code's AST and evaluating it with a restricted builtins mapping.\n\nThe mainline fix is in PR #13530 (`1641b28f`) and shipped in Langflow 1.11.0. The 1.10.3 backport is in PR #14071 (`94859df3`). Users should upgrade to Langflow 1.10.3 or later.\n\n## Workarounds\n\nUntil an upgrade is possible:\n- Remove Smart Transform from runnable flows.\n- Restrict flow creation, editing, and execution to trusted users.\n- Do not route untrusted or externally controlled data through Smart Transform.\n- Limit the Langflow process's filesystem, network, and credential access.\n\n## Credit\n\n- **Peyton Kennedy ([p80n-sec](https://github.com/p80n-sec)) of Endor Labs** — reporter (original finder)\n- **[SZXSec](https://github.com/SZXSec)** — reporter (duplicate report)\n- **[cyjhhh](https://github.com/cyjhhh)** — reporter (duplicate report)\n- **[0gur1](https://github.com/0gur1)** — reporter (duplicate report)\n- **[ajm4n](https://github.com/ajm4n)** — reporter (duplicate report, Finding 1 of a multi-finding submission)\n- **[andifilhohub](https://github.com/andifilhohub)** — analyst\n- **Jordan Frazier ([jordanrfrazier](https://github.com/jordanrfrazier))** — remediation developer",
"severity" : [ {
"type" : "CVSS_V3",
"score" : "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "langflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "1.3.0"
}, {
"fixed" : "1.10.3"
} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/langflow-ai/langflow/security/advisories/GHSA-9fpm-3445-2vx4"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2026-7700"
}, {
"type" : "WEB",
"url" : "https://github.com/langflow-ai/langflow/pull/13530"
}, {
"type" : "WEB",
"url" : "https://github.com/langflow-ai/langflow/commit/1641b28f33e2c47b9a0c6855922d44d1b8418b9d"
}, {
"type" : "WEB",
"url" : "https://github.com/langflow-ai/langflow/commit/94859df33acd70b2a1f816e26d68f5e89a7e5639"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/langflow-ai/langflow"
}, {
"type" : "WEB",
"url" : "https://github.com/langflow-ai/langflow/releases/tag/v1.10.3"
}, {
"type" : "WEB",
"url" : "https://vuldb.com/submit/804305"
}, {
"type" : "WEB",
"url" : "https://vuldb.com/vuln/360869"
}, {
"type" : "WEB",
"url" : "https://www.yuque.com/mengnanbulalei/ognlsk/hte2a98ro5gf8tp9?singleDoc#%20%E3%80%8AFirst%20release%20of%20Langflow%201.8.3%20Smart%20Transform%20eval()/Lambda%20injection%20RCE%20vulnerability%20analysis+POC%E3%80%8B"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-94" ],
"severity" : "HIGH",
"github_reviewed" : true,
"github_reviewed_at" : "2026-10-05T22:30:41Z",
"nvd_published_at" : null
}
}