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CVE-2026-13442 exposes a critical design flaw in IBM Langflow OSS versions 1.0.0 through 1.10.1, where the platform fails to enforce proper user isolation within its FAISS (Facebook AI Similarity Search) vector store implementation. FAISS is a library used for efficient similarity search and clustering of dense vectors, commonly employed in retrieval-augmented generation (RAG) pipelines to store and query embedded document fragments. In Langflow, each user’s vector content is organized under a logical namespace, which is supposed to act as a private container for that user’s indexed data. However, the vulnerability arises from the absence of ownership validation when a client specifies a FAISS namespace during vector store operations. An attacker with authenticated access—possessing only low-privileged credentials—can craft API requests that reference any existing namespace belonging to another user. Because Langflow does not verify whether the requesting principal is the legitimate owner of the targeted namespace, the server blindly serves or modifies the vector content associated with that namespace.
The attack vector is straightforward: the attacker sends a query to the vector store endpoint, supplying the victim’s namespace identifier instead of their own. The backend, lacking a user-to-namespace mapping table or ownership checks, processes the request as if it were legitimate. This allows the attacker to read all vector embeddings and associated metadata that the victim has stored—potentially including sensitive documents, proprietary knowledge bases, or personal conversation histories. More dangerously, the attacker can also inject or modify vectors within that namespace, poisoning the vector index. Subsequent queries performed by the victim (or any other user relying on that namespace) will return tampered results, leading to persistent misinformation or denial of service through corrupted retrieval. The flaw is rooted in CWE-520: .NET Misconfiguration – Use of Impersonation, though in this context it manifests as improper authorization in a vector store context.
IBM has assigned a CVSS base score of 7.1 (High) with the vector CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N, reflecting the network accessibility, low attack complexity, low privileges required, and the significant confidentiality impact with limited integrity degradation. The vulnerability has been observed in production deployments where Langflow is used as a backend for multi-tenant AI applications, making it a serious concern for organizations sharing a single instance across multiple teams or customers. The fix, implemented in version 1.10.2, introduces per-user namespace scoping by associating each namespace with the authenticated user’s identity at creation time and enforcing ownership checks on every read/write operation. Administrators are urged to upgrade immediately, as no workarounds exist.
DailyCVE Form:
Platform: IBM Langflow OSS
Version: 1.0.0–1.10.1
Vulnerability: FAISS namespace reuse
Severity: High (7.1)
date: 2026-07-28
Prediction: 2026-07-14
Analytics under heading What Undercode Say:
Enumerate existing FAISS namespaces via Langflow API
curl -X GET "http://target:7860/api/v1/vectorstores/namespaces" -H "Authorization: Bearer $ATTACKER_TOKEN"
Attempt to read victim's vector content by specifying their namespace
curl -X POST "http://target:7860/api/v1/vectorstores/query" \
-H "Authorization: Bearer $ATTACKER_TOKEN" \
-H "Content-Type: application/json" \
-d '{"namespace": "victim_namespace", "query": "sensitive topic", "top_k": 10}'
Poison the victim's namespace by injecting fake vectors
curl -X POST "http://target:7860/api/v1/vectorstores/ingest" \
-H "Authorization: Bearer $ATTACKER_TOKEN" \
-H "Content-Type: application/json" \
-d '{"namespace": "victim_namespace", "documents": [{"text": "false information", "embedding": [0.1, 0.2, ...]}]}'
Check for lack of ownership enforcement in server logs (if accessible)
grep -i "FAISS namespace" /var/log/langflow/app.log | grep -v "authorized"
Exploit:
An authenticated attacker with any valid account can exploit this vulnerability by:
1. Identifying target usernames or namespace identifiers through information disclosure or brute-force enumeration (namespace IDs are often predictable or exposed in error messages).
2. Sending authenticated API requests to the vector store endpoints (/api/v1/vectorstores/query, /api/v1/vectorstores/ingest, /api/v1/vectorstores/delete) with the `namespace` parameter set to the victim’s namespace.
3. Because Langflow does not validate that the authenticated user owns the requested namespace, the server processes the request without authorization checks.
4. The attacker can then read all vectorized documents belonging to the victim (confidentiality breach) and insert, update, or delete vectors to alter future retrieval results (integrity impact).
5. This poisoning persists until the victim or an administrator manually cleans the namespace, as no audit trail or rollback mechanism exists for vector modifications.
Protection:
- Immediate Upgrade: Upgrade Langflow OSS to version 1.10.2 or later, which introduces proper user-to-namespace ownership mapping and enforces authorization checks on all vector store operations.
- Network Segmentation: Restrict access to the Langflow API endpoints to trusted networks or VPNs to reduce exposure.
- Monitoring: Implement logging and alerting for anomalous namespace access patterns, such as a single user querying multiple distinct namespaces in a short time window.
- Least Privilege: Enforce strict role-based access control (RBAC) and avoid using shared service accounts; each user should have a unique identity.
- Periodic Audits: Regularly review vector store contents and access logs to detect unauthorized namespace usage or data poisoning attempts.
Impact:
- Confidentiality (High): Attackers can read any vectorized content stored by other users, including proprietary documents, personal data, intellectual property, and sensitive internal knowledge bases. This constitutes a direct breach of data confidentiality in multi-tenant deployments.
- Integrity (Limited): By injecting or modifying vectors in a victim’s namespace, attackers can poison the retrieval results. Subsequent queries will return tampered or misleading information, potentially affecting decision-making, automated workflows, or AI-generated responses that rely on the vector store.
- Availability (Indirect): While not directly impacting system uptime, persistent poisoning can degrade the quality of service and force administrators to spend significant effort cleaning corrupted indices.
- Compliance Risk: Organizations subject to GDPR, HIPAA, or other data protection regulations may face regulatory penalties due to unauthorized access to personal or sensitive data.
- Lateral Movement: The vulnerability can be chained with other Langflow flaws (e.g., RCE, SSRF) to escalate privileges or pivot to internal networks, as multiple CVEs affect the same version range.
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Sources:
Reported By: nvd.nist.gov
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