Vulnerability-Lookup

CVE-2026-55447 (GCVE-0-2026-55447)

Vulnerability from cvelistv5 – Published: 2026-06-23 16:21 – Updated: 2026-06-24 15:47

Title

Langflow: BaseFileComponent-based nodes arbitrary file read with RCE exploit

Summary

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.

Severity

9.6 (Critical)


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

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-06-24 03:56 UTC

CWE

CWE-61 - UNIX Symbolic Link (Symlink) Following
CWE-200 - Exposure of Sensitive Information to an Unauthorized Actor

Assigner

GitHub_M CNA under the mitre root

CNA scorecard C 72/100 over 7879 records in the last 180 days details

References

2 references

URL	Tags
https://github.com/langflow-ai/langflow/security/…	x_refsource_CONFIRM
https://github.com/langflow-ai/langflow/pull/12945	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version	CPE status
langflow-ai	langflow	Affected: < 1.9.2	guessed

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2026-55447

Vulnerability from fkie_nvd - Published: 2026-06-23 17:17 - Updated: 2026-10-25 23:10

Severity

9.6 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H

Summary

References

	URL	Tags
	security-advisories@github.com	https://github.com/langflow-ai/langflow/pull/12945	Issue Tracking, Patch
	security-advisories@github.com	https://github.com/langflow-ai/langflow/security/advisories/GHSA-ccv6-r384-xp75	Exploit, Patch, Vendor Advisory

Impacted products

	Vendor	Product	Version
	langflow	langflow	*

JSON

To clipboard

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GHSA-CCV6-R384-XP75

Vulnerability from github – Published: 2026-06-19 21:18 – Updated: 2026-06-19 21:18

Summary

Langflow: BaseFileComponent-based nodes arbitrary file read with RCE exploit

Details

Summary

All components based on BaseFileComponent are vulnerable to the following vulnerability: 1. Docling (DoclingInlineComponent) 2. Docling Serve (DoclingRemoteComponent) 3. Read File (FileComponent) 4. NVIDIA Retriever Extraction (NvidiaIngestComponent) 5. Video File (VideoFileComponent) 6. Unstructured API (UnstructuredComponent)

For clarity, from now on I'll only refer to Read File component.

The Read File node processes user-controlled files. Example scenario is a RAG chatbot - a system that allows users of an organization to ask questions about documents saved in the organizations.

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.

Using this vulnerability an attacker can acheive RCE: 1. Upload a file that directs the node to read Langflow's secret_key file containing the JWT token secret. 2. This would allow the attacker then to simply task the Chatbot for the JWT secret. 3. Using this secret, the attacker then crafts a JWT token for any user-id, bypassing authentication. 4. Code execution is then trivial - simply create a new flow with "Python Interpreter" node, fill it with arbitrary Python code and execute it.

Tested on commit 2d67402b1dbaefcbce85a244d4a6cd5e4bda1cfe

Details

The vulnerability is in: langflow/src/lfx/src/lfx/base/data/base_file.py Specifically in _unpack_bundle. This function extracts tar files, which can contain a symlink. This symlink can point to any file in the filesystem. Then, in self.process_files(), the file pointed by the symlink will be parsed and saved into the RAG. This can be done with unlimited number of symlinks in the same tar which can also be useful in some scenarios.

Suggestd fix - iterate over the files and make sure all are regular files or directories.

PoC

Reproduction: 1. Create a flow with Read File (or any other affected components), and connect its output to some storage such as Chroma DB. 2. Create a symlink pointing to any file. For the above exploit, point the symlink to langflow's JWT token file. 3. Compress this symlink with tar. 4. Upload it to the Read File component. 5. Check the database, or ask a Chatbot connected to this vector database for the contents of the file.

Concrete PoC:

Flow with RAG ingestion and a Chatbot around it: Vector Store RAG.json
Exploit tar: archive.tar.txt (remove .txt, GitHub blocked .tar)
Create a file /tmp/trip.docx with any contents in it
Ingest the file in the flow above, and ask the Chatbot a question about this file.

A demo showing the attack: https://github.com/user-attachments/assets/af00f700-f13f-4eac-848e-8afd11fb9297 In the demo the attacker steals Langflow secret key used to sign JWTs. The second stage of the attack, not shown in the demo, is using this key to sign a JWT token and executing Python code on the server using the Python code interpreter node.

Impact

Any Langflow user using any of the above mentioned components to ingest user-controlled data is affected. Depending on exact scenario, the user can also be exposed to an RCE risk.

Patches

Fixed in 1.9.2 via PR #12945. BaseFileComponent._unpack_bundle now rejects symlink and hardlink members (and any non-regular entries) during TAR extraction, with additional defensive symlink filtering during directory recursion and after extraction. Upgrade to 1.9.2 or later.

Ori Lahav Security Researcher @ Rubrik Inc.

Severity

9.6 (Critical)


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

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "langflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.9.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-55447"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200",
      "CWE-61"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-06-19T21:18:24Z",
    "nvd_published_at": null,
    "severity": "CRITICAL"
  },
  "details": "### Summary\nAll components based on `BaseFileComponent` are vulnerable to the following vulnerability:\n1. Docling (`DoclingInlineComponent`)\n2. Docling Serve (`DoclingRemoteComponent`)\n3. Read File (`FileComponent`)\n4. NVIDIA Retriever Extraction (`NvidiaIngestComponent`)\n5. Video File (`VideoFileComponent`)\n6. Unstructured API (`UnstructuredComponent`)\n\nFor clarity, from now on I\u0027ll only refer to Read File component.\n\nThe Read File node processes user-controlled files.\nExample scenario is a RAG chatbot - a system that allows users of an organization to ask questions about documents saved in the organizations.\n\nBy 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.\n\nUsing this vulnerability an attacker can acheive RCE:\n1. Upload a file that directs the node to read Langflow\u0027s `secret_key` file containing the JWT token secret.\n2. This would allow the attacker then to simply task the Chatbot for the JWT secret.\n3. Using this secret, the attacker then crafts a JWT token for any user-id, bypassing authentication.\n4. Code execution is then trivial - simply create a new flow with \"Python Interpreter\" node, fill it with arbitrary Python code and execute it.\n\nTested on commit 2d67402b1dbaefcbce85a244d4a6cd5e4bda1cfe\n\n### Details\nThe vulnerability is in:\n`langflow/src/lfx/src/lfx/base/data/base_file.py`\nSpecifically in `_unpack_bundle`. This function extracts tar files, which can contain a symlink.\nThis symlink can point to any file in the filesystem. Then, in `self.process_files()`, the file pointed by the symlink will be parsed and saved into the RAG.\nThis can be done with unlimited number of symlinks in the same tar which can also be useful in some scenarios.\n\nSuggestd fix - iterate over the files and make sure all are regular files or directories.\n\n\n### PoC\nReproduction:\n1. Create a flow with Read File (or any other affected components), and connect its output to some storage such as Chroma DB.\n2. Create a symlink pointing to any file. For the above exploit, point the symlink to langflow\u0027s JWT token file.\n3. Compress this symlink with tar.\n4. Upload it to the Read File component.\n5. Check the database, or ask a Chatbot connected to this vector database for the contents of the file.\n\n\nConcrete PoC:\n------------\n\n- Flow with RAG ingestion and a Chatbot around it: [Vector Store RAG.json](https://github.com/user-attachments/files/25159960/Vector.Store.RAG.json)\n- Exploit tar: [archive.tar.txt](https://github.com/user-attachments/files/25159954/archive.tar.txt) (remove .txt, GitHub blocked .tar)\n- Create a file `/tmp/trip.docx` with any contents in it\n- Ingest the file in the flow above, and ask the Chatbot a question about this file.\n\nA demo showing the attack:\nhttps://github.com/user-attachments/assets/af00f700-f13f-4eac-848e-8afd11fb9297\nIn the demo the attacker steals `Langflow` secret key used to sign JWTs. The second stage of the attack, not shown in the demo, is using this key to sign a JWT token and executing Python code on the server using the Python code interpreter node.\n\n### Impact\nAny Langflow user using any of the above mentioned components to ingest user-controlled data is affected. Depending on exact scenario, the user can also be exposed to an RCE risk.\n\n\n### Patches\nFixed in **1.9.2** via PR [#12945](https://github.com/langflow-ai/langflow/pull/12945). `BaseFileComponent._unpack_bundle` now rejects symlink and hardlink members (and any non-regular entries) during TAR extraction, with additional defensive symlink filtering during directory recursion and after extraction. Upgrade to **1.9.2 or later**.\n\n\nOri Lahav\nSecurity Researcher @ Rubrik Inc.",
  "id": "GHSA-ccv6-r384-xp75",
  "modified": "2026-06-19T21:18:24Z",
  "published": "2026-06-19T21:18:24Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/langflow-ai/langflow/security/advisories/GHSA-ccv6-r384-xp75"
    },
    {
      "type": "WEB",
      "url": "https://github.com/langflow-ai/langflow/pull/12945"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/langflow-ai/langflow"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Langflow: BaseFileComponent-based nodes arbitrary file read with RCE exploit"
}

PYSEC-2026-378

Vulnerability from pysec - Published: 2026-06-29 11:50 - Updated: 2026-07-01 20:22

Details

Summary

For clarity, from now on I'll only refer to Read File component.

The Read File node processes user-controlled files. Example scenario is a RAG chatbot - a system that allows users of an organization to ask questions about documents saved in the organizations.

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.

Tested on commit 2d67402b1dbaefcbce85a244d4a6cd5e4bda1cfe

Details

Suggestd fix - iterate over the files and make sure all are regular files or directories.

PoC

Concrete PoC:

Flow with RAG ingestion and a Chatbot around it: Vector Store RAG.json
Exploit tar: archive.tar.txt (remove .txt, GitHub blocked .tar)
Create a file /tmp/trip.docx with any contents in it
Ingest the file in the flow above, and ask the Chatbot a question about this file.

Impact

Any Langflow user using any of the above mentioned components to ingest user-controlled data is affected. Depending on exact scenario, the user can also be exposed to an RCE risk.

Patches

Ori Lahav Security Researcher @ Rubrik Inc.

Severity

9.6 (Critical)


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

Impacted products

Name	purl
langflow	pkg:pypi/langflow

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "langflow",
        "purl": "pkg:pypi/langflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.9.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
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      ]
    }
  ],
  "aliases": [
    "CVE-2026-55447",
    "GHSA-ccv6-r384-xp75"
  ],
  "details": "### Summary\nAll components based on `BaseFileComponent` are vulnerable to the following vulnerability:\n1. Docling (`DoclingInlineComponent`)\n2. Docling Serve (`DoclingRemoteComponent`)\n3. Read File (`FileComponent`)\n4. NVIDIA Retriever Extraction (`NvidiaIngestComponent`)\n5. Video File (`VideoFileComponent`)\n6. Unstructured API (`UnstructuredComponent`)\n\nFor clarity, from now on I\u0027ll only refer to Read File component.\n\nThe Read File node processes user-controlled files.\nExample scenario is a RAG chatbot - a system that allows users of an organization to ask questions about documents saved in the organizations.\n\nBy 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.\n\nUsing this vulnerability an attacker can acheive RCE:\n1. Upload a file that directs the node to read Langflow\u0027s `secret_key` file containing the JWT token secret.\n2. This would allow the attacker then to simply task the Chatbot for the JWT secret.\n3. Using this secret, the attacker then crafts a JWT token for any user-id, bypassing authentication.\n4. Code execution is then trivial - simply create a new flow with \"Python Interpreter\" node, fill it with arbitrary Python code and execute it.\n\nTested on commit 2d67402b1dbaefcbce85a244d4a6cd5e4bda1cfe\n \n### Details\nThe vulnerability is in:\n`langflow/src/lfx/src/lfx/base/data/base_file.py`\n Specifically in `_unpack_bundle`. This function extracts tar files, which can contain a symlink.\nThis symlink can point to any file in the filesystem. Then, in `self.process_files()`, the file pointed by the symlink will be parsed and saved into the RAG.\nThis can be done with unlimited number of symlinks in the same tar which can also be useful in some scenarios.\n\nSuggestd fix - iterate over the files and make sure all are regular files or directories.\n\n\n### PoC\nReproduction:\n1. Create a flow with Read File (or any other affected components), and connect its output to some storage such as Chroma DB.\n2. Create a symlink pointing to any file. For the above exploit, point the symlink to langflow\u0027s JWT token file.\n3. Compress this symlink with tar.\n 4. Upload it to the Read File component.\n5. Check the database, or ask a Chatbot connected to this vector database for the contents of the file.\n\n\nConcrete PoC:\n ------------\n\n- Flow with RAG ingestion and a Chatbot around it: [Vector Store RAG.json](https://github.com/user-attachments/files/25159960/Vector.Store.RAG.json)\n - Exploit tar: [archive.tar.txt](https://github.com/user-attachments/files/25159954/archive.tar.txt) (remove .txt, GitHub blocked .tar)\n- Create a file `/tmp/trip.docx` with any contents in it\n- Ingest the file in the flow above, and ask the Chatbot a question about this file.\n\nA demo showing the attack:\nhttps://github.com/user-attachments/assets/af00f700-f13f-4eac-848e-8afd11fb9297\n In the demo the attacker steals `Langflow` secret key used to sign JWTs. The second stage of the attack, not shown in the demo, is using this key to sign a JWT token and executing Python code on the server using the Python code interpreter node.\n \n### Impact\nAny Langflow user using any of the above mentioned components to ingest user-controlled data is affected. Depending on exact scenario, the user can also be exposed to an RCE risk.\n\n\n### Patches\nFixed in **1.9.2** via PR [#12945](https://github.com/langflow-ai/langflow/pull/12945). `BaseFileComponent._unpack_bundle` now rejects symlink and hardlink members (and any non-regular entries) during TAR extraction, with additional defensive symlink filtering during directory recursion and after extraction. Upgrade to **1.9.2 or later**.\n\n\nOri Lahav\nSecurity Researcher @ Rubrik Inc.",
  "id": "PYSEC-2026-378",
  "modified": "2026-07-01T20:22:55.869683Z",
  "published": "2026-06-29T11:50:52.436774Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/langflow-ai/langflow/security/advisories/GHSA-ccv6-r384-xp75"
    },
    {
      "type": "WEB",
      "url": "https://github.com/langflow-ai/langflow/pull/12945"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/langflow-ai/langflow"
    },
    {
      "type": "PACKAGE",
      "url": "https://pypi.org/project/langflow"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-ccv6-r384-xp75"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55447"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Langflow: BaseFileComponent-based nodes arbitrary file read with RCE exploit"
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Vulnerability timeline

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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Action not permitted

CVE-2026-55447 (GCVE-0-2026-55447)

FKIE_CVE-2026-55447

GHSA-CCV6-R384-XP75

Summary

Details

PoC

Concrete PoC:

Impact

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PYSEC-2026-378

Summary

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