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CVE-2026-7700 — langflow

Fix: langflow-ai/langflow#13530

CVE-2026-7700 is a CWE-74 vulnerability in langflow. A fix is available for langflow — see the affected versions and patch details below.

langflow-ai langflow LambdaFilterComponent lambda_filter.py eval code injection

Also known asGHSA-9fpm-3445-2vx4PYSEC-2026-4232
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Oct 22, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • 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-2026-7700.

EPSS Exploitation Probability

via FIRST.org ↗
0.3%probability of exploitation in next 30 days
Lower Risk-0.08%
Lower risk than most CVEs23th percentile — riskier than 23% of all scored CVEsHighest risk
0.00%0.30%0.60%0.89%0.0%0.3%Jun 26Sep 26Oct 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

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

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)

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 example, an attacker can attempt to make the model return:

lambda x: __import__("os").system("id")

This 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.

Successful 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.

PoC

https://github.com/user-attachments/assets/13c48fe1-7225-4e0d-9687-2d2df1e87f0e

Fix

The reported path was addressed by validating the generated code's AST and evaluating it with a restricted builtins mapping.

The 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.

Workarounds

Until an upgrade is possible:

  • Remove Smart Transform from runnable flows.
  • Restrict flow creation, editing, and execution to trusted users.
  • Do not route untrusted or externally controlled data through Smart Transform.
  • Limit the Langflow process's filesystem, network, and credential access.

Credit

  • Peyton Kennedy (p80n-sec) of Endor Labs — reporter (original finder)
  • SZXSec — reporter (duplicate report)
  • cyjhhh — reporter (duplicate report)
  • 0gur1 — reporter (duplicate report)
  • ajm4n — reporter (duplicate report, Finding 1 of a multi-finding submission)
  • andifilhohub — analyst
  • Jordan Frazier (jordanrfrazier) — remediation developer

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIlangflow≥ 1.3.0&&< 1.10.31.10.3pip install --upgrade 'langflow==1.10.3'

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 1.10.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-7700 is resolved across your whole dependency graph.

  3. Workarounds

    Stop passing untrusted input into the interpreter or shell: call the affected binary with an argument array rather than a composed command string, reject anything outside a strict allowlist of expected values, and run the component under an account that cannot reach beyond the work it legitimately does.

Frequently Asked Questions

## 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 e
O3 Security · Impact-Aware SCA

Is CVE-2026-7700 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2026-7700: langflow — Fixed in 1.10.3