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GHSA-9fpm-3445-2vx4 CVE-2026-7700 PYSEC-2026-4232

Langflow: Prompt injection in Langflow Smart Transform can lead to code execution

Low PyPI
Severity
2.1 / 10
Low · CVSS 4.0
Exploit chance (EPSS)
0.31%
Chance of exploitation in the next 30 days. Higher than 24.35% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix
1.10.3
Upgrade langflow to this version or later

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 versions

PyPI

PackageVulnerableFixed in
langflow
>= 1.3.0, < 1.10.3
1.10.3

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

langflow PyPI → 1.10.3

pip
pip install --upgrade "langflow>=1.10.3"
poetry
poetry add "langflow>=1.10.3"
uv
uv add "langflow>=1.10.3"

Severity breakdown

CVSS 4.0 2.1

CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Attack vector
Network
Attack complexity
Low
Attack requirements
None
Privileges required
Low
User interaction
None
Confidentiality (vulnerable system)
Low
Integrity (vulnerable system)
Low
Availability (vulnerable system)
Low
Confidentiality (subsequent systems)
None
Integrity (subsequent systems)
None
Availability (subsequent systems)
None
Exploit maturity
Proof of concept
Scope
X

CVSS 3.1 8.8

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Attack vector
Network
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Weakness

CWE-94: Improper Control of Generation of Code ('Code Injection')

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.

How to prevent it
  • Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
  • Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
  • Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-qrpv-q767-xqq2
Langflow: IDOR Vulnerability in `/api/v1/responses` Endpoint Allows Authenticated Attackers to Access Another User's Flow
High Known exploited Also affects langflow
GHSA-vwmf-pq79-vjvx
Unauthenticated Remote Code Execution in Langflow via Public Flow Build Endpoint
Critical Known exploited Also affects langflow
GHSA-g22f-v6f7-2hrh
Langflow affected by Remote Code Execution via validate_code() exec()
High Known exploited Also affects langflow
GHSA-577h-p2hh-v4mv
Langflow CORS misconfiguration enables Account Takeover and RCE
Critical Known exploited Also affects langflow
GHSA-rvqx-wpfh-mfx7
Langflow Unauth RCE
Critical Known exploited Also affects langflow
GHSA-j8f7-x8jm-wmm4
Langflow: SSRF in URL-taking components (protection disabled by default / warn-only, not applied to RSS, SearXNG, Web Search, Home Assistant, Glean, Docling)
Medium Also affects langflow
GHSA-4f6c-2vvp-gw82
Langflow: IP Spoofing Bypass via `X-Forwarded-For` Allowing Remote Configuration Write
High Also affects langflow
GHSA-4hmc-cfm3-w43c
Langflow has Authenticated Cross-Project File Disclosure via Unscoped MCP Resource Handlers
High Also affects langflow
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