EUVD-2026-38520
GHSA-rcjh-r59h-gq37
Severity by source
AV:N/AC:H/PR:N/UI:R/S:C/C:H/I:N/A:N
AC:H captures the public-flow prerequisite; UI:N preferred over vendor's UI:R since the attacker needs no victim interaction once a flow is shared; S:C reflects cross-boundary filesystem access; no integrity or availability impact.
Primary rating from Vendor (https://github.com/langflow-ai/langflow).
CVSS VectorVendor: https://github.com/langflow-ai/langflow
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:C/C:H/I:N/A:N
Lifecycle Timeline
2DescriptionCVE.org
Summary
The "Shareable Playground" (or "Public Flows" in code) contains a potential arbitrary file-read vulnerability, depending on the exact flow configuration used.
By making a flow public, public execution of the flow is allowed. The execution request can contain a list of files that gets read by Langflow and fed into the LLM. The files path can be any path supported by the storage - it can be either a local file or *S3 path* if supported by the local configuration
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. This request contains a files field that can contain a list of files. The files get read in LCModelComponent._get_chat_result in a call to to_lc_message. A detailed stacktrace:
...
File "/Users/ori/Work/research/langchain/langflow/src/backend/base/langflow/api/build.py", line 466, in build_vertices
vertex_build_response: VertexBuildResponse = await _build_vertex(vertex_id, graph, event_manager)
File "/Users/ori/Work/research/langchain/langflow/src/backend/base/langflow/api/build.py", line 324, in _build_vertex
vertex_build_result = await graph.build_vertex(
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/graph/base.py", line 1563, in build_vertex
await vertex.build(
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/vertex/base.py", line 770, in build
await step(user_id=user_id, event_manager=event_manager, **kwargs)
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/events/observability/lifecycle_events.py", line 95, in wrapper
result = await observed_method(self, *args, **kwargs)
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/vertex/base.py", line 411, in _build
await self._build_results(
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/graph/vertex/base.py", line 640, in _build_results
result = await initialize.loading.get_instance_results(
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/interface/initialize/loading.py", line 76, in get_instance_results
return await build_component(params=custom_params, custom_component=custom_component)
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/interface/initialize/loading.py", line 299, in build_component
build_results, artifacts = await custom_component.build_results()
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/custom/custom_component/component.py", line 1136, in build_results
return await self._build_with_tracing()
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/custom/custom_component/component.py", line 1118, in _build_with_tracing
results, artifacts = await self._build_results()
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/custom/custom_component/component.py", line 1163, in _build_results
result = await self._get_output_result(output)
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/custom/custom_component/component.py", line 1238, in _get_output_result
result = await method() if inspect.iscoroutinefunction(method) else await asyncio.to_thread(method)
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/base/models/model.py", line 88, in text_response
result = await self.get_chat_result(
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/base/models/model.py", line 180, in get_chat_result
return await self._get_chat_result(
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/base/models/model.py", line 232, in _get_chat_result
messages.append(input_value.to_lc_message(self.name))
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/schema/message.py", line 184, in to_lc_message
file_contents = self.get_file_content_dicts(model_name)
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/schema/message.py", line 256, in get_file_content_dicts
content_dicts.append(create_image_content_dict(file, None, model_name))
File "/Users/ori/Work/research/langchain/langflow/src/lfx/src/lfx/utils/image.py", line 96, in create_image_content_dict
...This triggers Langflow to feed the file into the LLM as an Image. Reading the files back depends on the specific LLM configuration.
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_tmproute and copy as cURL - Edit the
filesJSON field to point to any file.
Impact
Potential file read (local or S3) if shareable playground feature is used.
Ori Lahav Security Researcher @ Rubrik Inc.
AnalysisAI
Arbitrary file read in Langflow's Shareable Playground feature exposes local filesystem and S3 paths to unauthenticated attackers on instances where at least one public flow exists. The /api/v1/build_public_tmp endpoint accepts a user-controlled files field without path validation, causing Langflow to read attacker-specified files and pass their contents to the configured LLM. A proof-of-concept is publicly documented in the GitHub Security Advisory GHSA-rcjh-r59h-gq37; no active exploitation (CISA KEV) has been confirmed. Note: the 'RCE' tag associated with this CVE in source data is inconsistent with the described impact, which is limited to information disclosure via file read.
Technical ContextAI
CWE-73 (External Control of File Name or Path) describes the root cause: Langflow's public flow execution API at /api/v1/build_public_tmp passes the caller-supplied files list directly into LCModelComponent._get_chat_result, which forwards it to to_lc_message → get_file_content_dicts → create_image_content_dict in langflow/utils/image.py. No path sanitization, allowlist, or sandbox is applied before reading. The affected package is pkg:pip/langflow versions prior to 1.10.0. The Shareable Playground feature is a deliberate design capability in Langflow - an open-source AI workflow platform built atop LangChain - that enables unauthenticated users to execute shared flows via a public link. The vulnerability arises because the execution payload's files parameter was never restricted to pre-approved upload paths, allowing references to arbitrary local paths (e.g., /etc/passwd, /root/.ssh/id_rsa) or S3 URIs accessible under the server's cloud identity.
RemediationAI
Upgrade Langflow to version 1.10.0 or later, which is the vendor-confirmed fix per GHSA-rcjh-r59h-gq37 (https://github.com/langflow-ai/langflow/security/advisories/GHSA-rcjh-r59h-gq37); run pip install --upgrade langflow==1.10.0 or higher. If immediate upgrade is not possible, disable the Shareable Playground / Public Flows feature entirely to remove unauthenticated access to /api/v1/build_public_tmp - this is the most effective compensating control and eliminates the attack surface at the cost of losing the public-sharing capability. As a secondary control, restrict network access to the Langflow API via firewall or reverse proxy to trusted IP ranges, which reduces exposure but does not protect against insider threats or compromised internal hosts. If S3 integration is not required, revoke or restrict the server's cloud IAM permissions to minimize the blast radius of any file read exploitation. No vendor-documented workaround short of disabling the feature has been confirmed.
Same weakness CWE-73 – External Control of File Name or Path
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External POC / Exploit Code
Leaving vuln.today