---
id: agentsope/SkillAlchemy/agentsop-multi-tenant-rag
version: "7d584eb3"
license: MIT
install: manual
updated: 2026-06-30
---

# agentsop-multi-tenant-rag — This skill encodes security-first isolation patterns for RAG systems serving multiple tenants, customers, or permission scopes. It teaches agents to enforce tenant filtering at the vector store query boundary—the only place where isolation truly matters—and pairs vendor-specific primitives (Pinecone namespaces, Weaviate multiTenancy, Qdrant payload filters, pgvector RLS) with framework adapters like LlamaIndex and LangChain. Activate when designing, reviewing, or debugging any retrieval pipeline where the corpus spans more than one tenant.

Publisher: agentsope · Stars: 219 · Updated: 2026-06-30

Install (manual): `git clone https://github.com/agentsope/SkillAlchemy`

## SKILL.md

# Multi-Tenant RAG · Security-First Isolation SOP

> Third-person operating model for a coder agent that owns retrieval correctness
> across tenant boundaries. The audience is the LLM agent writing or reviewing
> the code — not the end user.

> **One sentence**: *Isolation lives at the vector store query boundary, not
> at the model. Anything that reaches the LLM's context window has already
> leaked.*

---

## 1. 何时激活 (Activation Rules)

Activate this skill whenever **any** of the following holds:

1. The codebase contains a retrieval call (`vector_store.query`,
   `query_points`, `similarity_search`, `as_retriever().retrieve(...)`, raw
   `pgvector` `ORDER BY embedding <-> $1`) **and** the corpus serves more than
   one tenant, customer, organisation, workspace, user, or permission scope.
2. The user mentions any of: multi-tenant RAG, namespace, tenant, workspace,
   `tenant_id`, `org_id`, `user_id`, "cross-customer", "shared index",
   "knowledge base per team".
3. A bug report says "User A saw User B's document", "wrong company's data
   surfaced", "the assistant cited a doc I don't have access to", or anything
   that smells like cross-context bleed.
4. PR review: any new code calling a vector store **without** a tenant-scoped
   filter argument, or filtering only on the returned `nodes` / `documents`
   list after retrieval.
5. A new RAG endpoint is about to ship and tenant scoping has not been
   explicitly audited.

Do **not** activate when:

- The corpus is fully public and there is no per-tenant view (e.g. open-data
  Q&A).
- The retrieval pipeline already enforces a **physically separate** index /
  collection per tenant via infrastructure the application code cannot
  override (e.g. one Pinecone index per customer with credentials issued
  per-tenant). In that case the isolation lives in IAM, not in this skill.

---

## 2. 核心心智模型 (Core Mental Model)

Three principles. If a design violates any of them the system is exploitable,
regardless of how good the LLM prompt is.

### Principle 1 — Isolation lives at the boundary, not at the model

The boundary is the vector store query call. **Whatever crosses that boundary
is in the trust set.** An LLM, a reranker, or a postprocessor that "filters
out" foreign-tenant chunks is operating *inside* the breach: those tokens have
already been embedded, retrieved, scored, and exposed to attacker-controlled
prompts. CVE-2024-41892 (Pinecone, 2024) is the canonical demonstration —
RBAC checks executed *after* retrieval allowed sentinel data to cross
namespace boundaries before the access check fired. (See *we45*, *CSO Online*
references.)

> **Operational corollary**: any code shaped `results = vs.query(...); results
> = [r for r in results if r.metadata["tenant"] == ctx.tenant]` is a security
> defect, not a style nit. The leak already happened — you only hid it from
> the user.

### Principle 2 — Embed the tenant key at ingestion, not just at query

A query-time filter is only enforceable if every vector carries the key. The
two failure shapes:

- *Forgotten field*: a single document ingested without `tenant_id` becomes a
  shared global record returned to all tenants — it matches no filter
  predicate that requires equality.
- *Mutable field*: `tenant_id` derived from the document body / LLM extraction
  rather than from the request's authenticated session — attacker can craft
  document content that re-labels itself.

The tenant key **must** come from the authenticated session at ingestion time
and be written into the immutable payload / metadata column. Treat it like a
foreign key to your tenants table.

### Principle 3 — Defence in depth: namespace **and** filter, not namespace **or** filter

Most production-grade stacks layer two mechanisms:

| Layer | Mechanism | What it stops |
|---|---|---|
| Storage | Per-tenant namespace / shard / collection / RLS policy | Operator bugs, mis-routed queries, ops mistakes |
| Query | `filter={tenant_id: $session.tenant}` on every call | App bugs, namespace selection mistakes, cross-shard joins |

Pinecone's official guidance is "namespace per tenant in serverless"; Weaviate
mandates a tenant handle on every CRUD when `multiTenancyConfig.enabled=true`;
Qdrant marks the field as `is_tenant=true` *and* requires `Filter.must` on
read; pgvector pairs schema partitioning with RLS. The principle is the same:
one layer is one bug away from failing open.

---

## 3. SOP 工作流 (Agentic Protocol)

Eight stages. Each gates the next. Stop and remediate at the first failure.

### Stage 0 — Threat-model the boundary

Before any code change, write down:

1. **Tenant key**: what identifier separates customers? (`tenant_id`,
   `org_id`, `workspace_id`, `user_id`, `acl_label`). Pick one — do not mix.
2. **Trust source**: where does the request-time tenant key come from?
   It must be from the authenticated session / JWT claim / signed header —
   **never from a query param, prompt, or document body**.
3. **Cardinality**: how many tenants? (<100 → namespace/collection viable;
   >10k → payload partition + index is mandatory). Drives Stage 2 choice.
4. **Blast radius**: if a single vector leaks, what's the worst document?
   (PII / regulated / IP / public). Raises bar on Stages 4-6.

Artifact: a 5-line `THREAT_MODEL.md` snippet co-located with the retriever.

### Stage 1 — Pick the isolation primitive (vendor-specific)

| Vendor | Recommended primitive | When |
|---|---|---|
| **Pinecone** | One **namespace** per tenant in a serverless index | Default; cheapest query (1 RU per 1 GB / namespace); offboarding = `delete_namespace` |
| **Weaviate** | `multiTenancyConfig.enabled=True` on the collection | Native, per-shard isolation; mandatory tenant handle on CRUD |
| **Qdrant** | Single collection, payload key with `is_tenant=true`, `keyword` index | Scales to millions of tenants; physical co-location, logical isolation |
| **Chroma** | `where={"tenant_id": "..."}` on every `query`/`get`/`update`/`delete` | Smallest stacks; cookbook calls this the "naive multi-tenancy strategy" — accept its limits |
| **pgvector** | Postgres schema or row-level security (RLS) on `tenant_id` column | When the rest of the app already lives in Postgres |
| **Milvus** | `partition_key` on tenant field | Same shape as Qdrant `is_tenant` |

Decision rule: **prefer the strongest physical primitive your tenant
cardinality can afford**, then always add the filter at query time anyway
(Principle 3).

### Stage 2 — Ingestion: write the key, lock the schema

```python
# Canonical shape — vendor-agnostic
node.metadata = {
    "tenant_id": session.tenant_id,        # MUST come from authenticated session
    "doc_id":    doc.id,
    "source":    doc.uri,
    "ingested_at": now_utc(),
    # ... domain fields
}
assert node.metadata["tenant_id"], "refuse to embed without tenant_id"
```

Hard rules:

- Refuse to write a vector if `tenant_id` is missing or empty — fail loud, do
  not default.
- `tenant_id` is **never** taken from document content; **never** mutable
  post-ingestion.
- Schema-validate metadata before embedding (Pydantic / JSON Schema) — type
  drift breaks `$eq` silently.
- For Qdrant: create the payload index *before* the first upsert
  (`create_payload_index(field="tenant_id", schema="keyword",
  is_tenant=True)`).
- For Weaviate: create the tenant (`collection.tenants.create([Tenant(name=t)])`)
  before any write.

### Stage 3 — Query-time filter (the only place the rule applies)

**Every** retrieval call must accept a tenant key from the session and pass
it into the vendor's filter argument. Templates in OP-04 through OP-08.

```python
# Generic LlamaIndex shape — works across Pinecone, Qdrant, Weaviate, Chroma
from llama_index.core.vector_stores import (
    MetadataFilter, MetadataFilters, FilterOperator,
)

filters = MetadataFilters(filters=[
    MetadataFilter(key="tenant_id", value=session.tenant_id,
                   operator=FilterOperator.EQ),
])
retriever = index.as_retriever(similarity_top_k=8, filters=filters)
```

If the framework or vendor SDK does not expose a query-time filter, **switch
the framework or vendor — do not patch with a post-filter**.

### Stage 4 — Cross-tenant property test (the gate)

Before merging, the PR must include — and CI must run — a test that:

1. Ingests N≥3 documents per tenant for at least 2 tenants (A and B).
2. Issues a query as tenant A whose semantic top-1 in the *unfiltered* search
   would be a tenant-B document (craft the query against B's content).
3. Asserts zero tenant-B results returned.
4. Repeats symmetrically as tenant B.

If the unfiltered top-1 is *not* a foreign-tenant doc, the test is dishonest
— rewrite the corpus to make foreign-tenant semantically closer, otherwise
the test always passes vacuously.

See OP-09 for a runnable scaffold.

### Stage 5 — Defense in depth (Principle 3)

Layer at least one of:

- Per-tenant namespace / shard / collection on top of the metadata filter.
- Per-tenant API key / scoped credential at the vector store layer (Pinecone
  API key project scope, Postgres role per tenant).
- DB-level enforcement (pgvector RLS policy `USING (tenant_id =
  current_setting('app.current_tenant')::uuid)`).

A single layer is one config bug away from open.

### Stage 6 — Audit logging

Every retrieval call logs:

```
{ts, request_id, session.tenant_id, query_hash,
 vs.namespace_or_collection, filter_clause,
 returned_count, returned_tenant_ids_set}
```

Then add a synchronous assertion in the request path:

```python
assert returned_tenant_ids_set.issubset({session.tenant_id, GLOBAL_TENANT})
```

This converts a silent leak into a loud 500 — the right failure mode.

### Stage 7 — Cache / rerank / postprocessing audit

Walk every component **after** retrieval and confirm none of them:

- Caches results across tenants under a tenant-agnostic key (e.g.
  `cache_key = hash(query)` is a leak; correct is `cache_key = hash((tenant_id,
  query))`).
- Returns "fallback" results from a global pool when the tenant pool is
  empty.
- Logs full retrieved content to a shared monitoring store readable by other
  tenants' staff.

Reranker / NodePostprocessor / synthesizer can only safely *narrow* the set;
they never restore foreign tenants and never invent context — confirm by
reading the code path.

### Stage 8 — Ongoing verification

- Add the cross-tenant property test to nightly CI, not just merge gate.
- On schema migration: re-run with the new chunking / embedding model.
- On vendor upgrade: re-read the changelog for filter / namespace semantics
  (this changes more than vendors admit — see Pinecone disk-based metadata
  filtering, Qdrant 1.16 tiered multitenancy).

---

## 4. 操作模型 (Operation Models)

Format: **Trigger / Action / Output / Evidence**. Vendor-specific filter
syntax canonicalised against current docs (May 2026).

### OP-01 EmbedTenantAtIngest

- **Trigger**: New ingestion pipeline; any system not currently writing
  `tenant_id` into metadata.
- **Action**: Add `tenant_id` (from authenticated session) to every node's
  metadata at chunk creation. Validate non-empty before `add()` /
  `upsert()`. Make the field part of the ingestion schema.
- **Output**: 100% of vectors carry the immutable tenant key; ingestion
  refuses to write otherwise.
- **Evidence**: Qdrant *Multitenancy* docs; Pinecone *Implement multitenancy*
  guide; we45 RAG leakage post-mortem.

### OP-02 ChooseIsolationPrimitive

- **Trigger**: Greenfield RAG, or migration from single-tenant.
- **Action**: Use the cardinality / latency / cost matrix in Stage 1. Default
  ladder: <100 tenants → namespace/collection per tenant; 100-10k → payload
  partition + index (`is_tenant`); >10k → tiered (hot collection +
  cold archive). Pair with query filter (Principle 3).
- **Output**: A documented primitive + the one-line justification.
- **Evidence**: Pinecone *Namespaces vs. metadata filtering*; Qdrant
  *Multitenancy and custom sharding*; Weaviate *Multi-tenancy operations*.

### OP-03 LockTenantSourceToSession

- **Trigger**: Code review reveals `tenant_id = request.json["tenant_id"]`
  or similar untrusted source.
- **Action**: Bind `tenant_id` to the authenticated principal (JWT claim,
  session row, signed header). Pass through one chokepoint (`Context` /
  `RequestState`) — never read user input again downstream.
- **Output**: A single `get_tenant_id(ctx)` accessor used everywhere; no
  string-typed tenant ids floating through function args.
- **Evidence**: OWASP LLM01 (prompt injection) + LLM06 (sensitive info
  disclosure); Christian Schneider *RAG security: the forgotten attack
  surface*.

### OP-04 PineconeFilterAtQuery

- **Trigger**: Pinecone-backed RAG; multi-tenant.
- **Action**: One namespace per tenant; **and** pass `filter={"tenant_id":
  {"$eq": tenant_id}}`. Avoid `$in` lists >10,000 (hard cap).
  ```python
  index.query(
      namespace=tenant_id,                       # primary isolation
      vector=embedding,
      top_k=8,
      filter={"tenant_id": {"$eq": tenant_id}},  # defence-in-depth
      include_metadata=True,
  )
  ```
- **Output**: Two-layer isolation; `delete_namespace(tenant_id)` for
  offboarding.
- **Evidence**: `docs.pinecone.io/guides/index-data/implement-multitenancy`,
  `docs.pinecone.io/troubleshooting/namespaces-vs-metadata-filtering`.

### OP-05 WeaviateTenantHandle

- **Trigger**: Weaviate-backed RAG.
- **Action**: Enable `multiTenancyConfig(enabled=True)` on the collection;
  create one tenant per customer; pass `tenant=` on every read/write.
  ```python
  coll = client.collections.get("Docs").with_tenant(session.tenant_id)
  coll.query.near_vector(vector=emb, limit=8)
  ```
  Optionally `auto_tenant_activation=True` if tenant set is sparse.
- **Output**: Each tenant on a separate shard; cross-tenant reads physically
  impossible from a single client handle.
- **Evidence**: `docs.weaviate.io/weaviate/manage-collections/multi-tenancy`;
  *Rethinking Vector Search at Scale* (Weaviate blog).

### OP-06 QdrantIsTenantPayload

- **Trigger**: Qdrant-backed RAG with ≥hundreds of tenants.
- **Action**: Single collection; create payload index with `is_tenant=True`;
  filter on every search.
  ```python
  client.create_payload_index(
      collection_name="docs",
      field_name="tenant_id",
      field_schema=models.KeywordIndexParams(
          type="keyword", is_tenant=True))

  client.query_points(
      collection_name="docs",
      query=emb, limit=8,
      query_filter=models.Filter(must=[
          models.FieldCondition(
              key="tenant_id",
              match=models.MatchValue(value=session.tenant_id))]),
  )
  ```
- **Output**: Per-tenant sub-indexes co-located on shared shards; scales to
  millions of tenants.
- **Evidence**: `qdrant.tech/documentation/manage-data/multitenancy/`;
  `qdrant.tech/articles/multitenancy/`;
  `qdrant.tech/documentation/examples/llama-index-multitenancy/`.

### OP-07 ChromaWhereFilter

- **Trigger**: Chroma-backed RAG (often local / smaller scale).
- **Action**: Filter on every call.
  ```python
  collection.query(
      query_embeddings=[emb], n_results=8,
      where={"$and": [
          {"tenant_id": {"$eq": session.tenant_id}},
          {"deleted":   {"$eq": False}},
      ]},
  )
  ```
  Same `where=` on `get`, `update`, `delete`.
- **Output**: Logical isolation. Pair with per-tenant collection for stronger
  isolation if cardinality permits.
- **Evidence**: `docs.trychroma.com/docs/querying-collections/metadata-filtering`;
  `cookbook.chromadb.dev/strategies/multi-tenancy/naive-multi-tenancy/`.

### OP-08 PgvectorRLS

- **Trigger**: pgvector-backed RAG inside an existing Postgres app.
- **Action**: Add a `tenant_id` column; enable RLS; create a policy.
  ```sql
  ALTER TABLE chunks ENABLE ROW LEVEL SECURITY;
  CREATE POLICY tenant_isolation ON chunks
      USING (tenant_id = current_setting('app.current_tenant')::uuid);

  -- per-request, before the ORDER BY embedding <-> $1:
  SET LOCAL app.current_tenant = '...';
  ```
  Application can never see other tenants' rows even with `SELECT *`.
- **Output**: Database-enforced isolation; survives ORM bugs and SQL typos.
- **Evidence**: Supabase pgvector multi-tenant RAG references; standard
  Postgres RLS docs.

### OP-09 CrossTenantPropertyTest

- **Trigger**: Any retrieval code change merges.
- **Action**:
  ```python
  def test_cross_tenant_isolation(retriever_factory):
      ingest("alpha", ["alpha-secret about widgets X1, X2"])
      ingest("beta",  ["beta confidential roadmap for Q3"])

      # Query as alpha for a topic beta owns
      r_alpha = retriever_factory(tenant="alpha").retrieve("Q3 roadmap")
      assert all(n.metadata["tenant_id"] == "alpha" for n in r_alpha)
      assert len(r_alpha) >= 0   # zero is fine; foreign is not

      # And the reverse
      r_beta = retriever_factory(tenant="beta").retrieve("widget X1")
      assert all(n.metadata["tenant_id"] == "beta" for n in r_beta)
  ```
- **Output**: Regression-proof guarantee that the filter is wired through.
  Gates merge.
- **Evidence**: Direct application of Principle 1; mirrors red-team test
  patterns from CSO Online *Securing RAG pipelines in enterprise SaaS*.

### OP-10 RuntimeAssertReturned

- **Trigger**: Production endpoint serving multi-tenant RAG.
- **Action**: After every retrieve, before passing to LLM:
  ```python
  bad = [n for n in nodes
         if n.metadata.get("tenant_id") not in {ctx.tenant, GLOBAL_TENANT}]
  if bad:
      log.critical("cross_tenant_leak", tenant=ctx.tenant, bad=bad)
      raise SecurityError("retrieval boundary violated")
  ```
- **Output**: Silent leak → loud 500; alert fires on the first occurrence.
- **Evidence**: Defence-in-depth with audit logging; mirrors EchoLeak /
  CVE-2024-41892 mitigations.

### OP-11 LlamaIndexAutoRetriever (advanced, with caveat)

- **Trigger**: Heterogeneous metadata; tenant + doc_type + date filters; want
  LLM to derive filter from natural language.
- **Action**: `VectorIndexAutoRetriever` with a `VectorStoreInfo` describing
  filterable fields. **Pin `tenant_id` server-side** — the auto-retriever
  decides other filters, but `tenant_id` is *always* injected from the
  session.
- **Output**: Flexible filter selection without exposing tenant to LLM.
- **Evidence**: LlamaIndex `MetadataFilters` + `AutoRetriever` docs;
  Principle 2 (tenant from session, not from content).

### OP-12 NamespaceVsFilterDecision

- **Trigger**: Greenfield; one tenant per namespace vs payload partition.
- **Action**: Use the rubric:
  - Cross-tenant queries ever needed (admin search, support tooling)? →
    payload partition (filter); namespace per tenant blocks cross-namespace
    queries.
  - Per-tenant deletion / GDPR erasure frequent? → namespace per tenant
    (`delete_namespace` is O(1)).
  - Tenant count >10k? → payload partition (namespaces have administrative
    overhead at scale).
  - Read cost-sensitive on Pinecone? → namespace per tenant (RU billed per
    namespace size).
- **Output**: One documented choice + a written rationale.
- **Evidence**: `docs.pinecone.io/troubleshooting/namespaces-vs-metadata-filtering`.

---

## 5. 困境决策案例 (Dilemma Cases)

### Dilemma 1 — Shared embeddings vs per-tenant embeddings

**困境**: Re-embedding the same public chunk (e.g. shared regulation text)
once per tenant doubles cost and complicates updates. But sharing a vector
across tenants means it lives in a "global" pool that must be readable by
all — opening a path for poisoned global content to reach every tenant.

**约束**:
- Embeddings are deterministic given (model, text) — no cryptographic per-
  tenant signal in the vector itself.
- A shared vector with `tenant_id="GLOBAL"` is by definition *in scope* for
  every tenant's filter — it is not isolated, it is intentionally shared.
- Poisoning at ingestion time of shared content propagates to every tenant
  simultaneously (Slack AI 2024 incident pattern).

**决策步骤**:
1. Classify content: tenant-private vs shared-public.
2. Shared-public goes to a separate `global` namespace / collection with
   *write-restricted* ingestion (only platform admins, signed source) and is
   served via a *second retrieval call*, not by widening the tenant filter.
3. Per-query: retrieve from tenant pool + (optionally) from global pool,
   union and dedupe at the application layer with explicit provenance
   tagging in each node's metadata.
4. Never mix them by `filter={"tenant_id": {"$in": [tenant, "GLOBAL"]}}` —
   that pattern hides which pool a chunk came from in downstream logs.

**结果**: Two pools, two retrieval calls, one application-layer merge. Cost
deduplicated for public content; private content stays in private isolation;
provenance is auditable.

**可提取的操作**: OP-01, OP-04..08 (per-pool), OP-10.

### Dilemma 2 — Small tenants pollute large tenants' relevance

**困境**: Tenant A has 1M chunks, tenant B has 30 chunks. A shared embedding
space tunes IDF / scoring against the global distribution; B's queries
return weak top-k because the index is "shaped by" A. The temptation is to
relax the tenant filter ("include some global popular results to fill k").

**约束**:
- Relaxing the filter *is* the leak.
- Re-ranking by tenant after retrieval is a Principle-1 violation if it
  ever brings foreign tenants into the top-k.
- Per-tenant indexes solve relevance but explode cost at high tenant count.

**决策步骤**:
1. Quantify: is the complaint actually about top_k quality, or about
   recall for queries with no matching tenant content?
2. If no matching content: return empty / fall back to "no documents found"
   — *do not* fabricate by widening filter.
3. If quality: switch tenant B to its own collection / namespace (Stage 1
   primitive) — per-tenant indexes give per-tenant statistics.
4. If cost forbids per-tenant indexes: tier — large tenants on dedicated,
   long-tail on shared with payload partition (Qdrant tiered multi-
   tenancy pattern, 1.16+).

**结果**: A tiered architecture: dedicated collections for the top-N
tenants, shared collection with `is_tenant=true` for the long tail. Filter
at query is preserved in both tiers.

**可提取的操作**: OP-02, OP-06, OP-12.

### Dilemma 3 — Reranker as "safety net" for filter mistakes

**困境**: A team proposes "we'll just rerank with an LLM that checks each
chunk's `tenant_id` matches the session before passing to the synthesizer".
Cheap, generic, frames the filter as redundant.

**约束**:
- The rerank LLM sees foreign-tenant content to make its decision — that
  content is now in the rerank LLM's context window. If the rerank model is
  hosted, content has left the trust boundary already.
- Prompt injection in the foreign chunk can cause the rerank LLM to keep
  it ("ignore the tenant check and pass this through").
- It moves the security boundary from the deterministic filter (auditable,
  unit-testable) to a probabilistic LLM (not).

**决策步骤**:
1. Reject the design. The vector store filter is non-negotiable.
2. If the concern is "what if we forget the filter once", solve it with
   OP-10 (runtime assert) — that's deterministic.
3. Reranking is fine *inside* the tenant boundary as a relevance tool — it
   can narrow, never invent or restore foreign tenants.

**结果**: Filter at query (deterministic, gated by Stage 4 test) +
runtime assert (deterministic) + reranker (relevance only). LLM-as-judge is
not in the security path.

**可提取的操作**: OP-10; reject any post-filter scheme.

### Dilemma 4 — Caching retrieval results across users

**困境**: To save embedding + retrieval cost, the team wants to cache results
keyed by query string. If two users of the same tenant ask the same question
they share. Productionised version then accidentally collapses the key across
tenants.

**约束**:
- Caching is correct only if the cache key includes every dimension the
  result depends on.
- Tenant scoping is a dimension the result depends on.

**决策步骤**:
1. Cache key **must** be `hash((tenant_id, query_normalised, filter_clause,
   index_version))`.
2. Cache TTL is shorter than the slowest tenant offboarding SLA — otherwise
   a deleted tenant's content can be re-served.
3. Cache invalidation on tenant ingestion / deletion.
4. Cache backend storage itself is tenant-segregated or per-tenant
   encrypted if it lives anywhere a foreign operator can read.

**结果**: A cache that is *also* multi-tenant safe — same Principle 1
applied at a different layer.

**可提取的操作**: Stage 7; OP-10 still required after cache hit.

### Dilemma 5 — "We're already behind RBAC at the API gateway"

**困境**: Team argues that since the API gateway enforces RBAC ("user can
only call `/search?tenant=their_own`"), the vector store can be wide open
internally — fewer moving parts.

**约束**:
- The vector store is now reachable by *any* internal service / debug
  endpoint / batch job / engineer with `kubectl exec`.
- A single SSRF / IDOR / log-shipper / monitoring agent that talks to the
  vector store with broad credentials defeats the gateway.
- CVE-2024-41892 (Pinecone) is exactly this shape — RBAC after retrieval,
  vector store internally trusting.

**决策步骤**:
1. Treat the vector store as a directly exposed datastore for threat-model
   purposes.
2. Add tenant filter at query *and* per-tenant credentials at the vector
   store layer where the vendor supports it.
3. If the vendor does not support per-tenant credentials (most don't),
   compensate with OP-10 (runtime assertion) — it catches the
   internally-misissued query.

**结果**: Gateway RBAC is *additional*, not load-bearing. The vector store
itself enforces tenancy.

**可提取的操作**: OP-10, OP-08 (RLS where applicable), Stage 5.

---

## 6. 反模式与边界 (Anti-patterns & Boundaries)

### Top anti-patterns (instant red flags in code review)

| # | Anti-pattern | Why it's wrong | Correct move |
|---|---|---|---|
| A1 | `results = vs.query(...); results = [r for r in results if r.metadata["tenant"] == ctx.tenant]` | Post-filter; the foreign content was already retrieved, scored, and (next step) injected into LLM context | Filter at query: pass tenant into vendor's `filter=` / `where=` / `query_filter=` |
| A2 | Trusting the LLM with `system_prompt += "only use chunks where tenant=X"` | LLMs don't enforce; chunks already in context | Never inside the context window |
| A3 | `tenant_id` derived from request JSON body or query param | Attacker-controlled; trivial IDOR | Bind to authenticated session/JWT claim once at the chokepoint |
| A4 | `tenant_id` extracted from document content at ingestion | Document body is attacker-controlled (for any user-uploaded doc) | Source from upload-time session, write immutable |
| A5 | Same vector index, no tenant field at all, "we filter by ACL later" | Without the key in metadata, the filter is impossible at query | OP-01: embed at ingest, refuse without key |
| A6 | Rerank LLM as the security boundary | Foreign content already in rerank model's context; prompt-injectable | Rerank narrows only; filter is the boundary (Dilemma 3) |
| A7 | Cache keyed on query alone, not (tenant, query) | Cross-tenant cache hit returns foreign tenant's answer | OP cache-key contract (Dilemma 4) |
| A8 | One shared "global" namespace queried with `{"tenant_id": {"$in": [t, "global"]}}` and no provenance tracking | Loses audit trail; lets a poisoned global chunk reach all tenants invisibly | Two pools, two queries, explicit provenance (Dilemma 1) |
| A9 | "We have one index per customer so we don't need a filter" | One config typo / one shared client reused across customers and isolation collapses | Defence in depth: namespace + filter both (Principle 3) |
| A10 | No cross-tenant property test in CI | First leak detected by a customer | OP-09 mandatory; runs on every PR |
| A11 | Schema drift — `tenant_id` is sometimes int, sometimes UUID, sometimes string | `$eq` silently fails to match across types | Schema-validate metadata at ingest (Pydantic) and assert on read |
| A12 | Verbose logging of full chunk text in shared observability backend | Foreign tenant content readable by ops staff of other tenants | Log hashes / IDs; redact body in shared sinks |

### Boundaries — when this skill is **not** the right move

- **B1** Single-tenant or public corpora — no tenant key exists; skill is over-engineering.
- **B2** Truly per-tenant infrastructure (one cluster per customer, separate creds) — the boundary is now infra/IAM. Apply this skill only if app code still mixes contexts.
- **B3** Hard real-time retrieval (sub-50ms) with vendor that materially penalises filters — measure first; if real, partition into per-tenant indexes (Stage 1 ladder) rather than weaken the filter.
- **B4** Offline batch / eval over labelled internal data — tenancy is documentation, not security.

### PR review smells

- `similarity_search(query)` without `filter=` argument and the corpus is multi-tenant.
- `vs.query(... namespace=request.json["ns"])` — namespace from user input.
- `if node.metadata["tenant_id"] != ctx.tenant: continue` *anywhere after* a retrieval call.
- A reranker / postprocessor that "decides" which tenant a chunk belongs to.
- Mock vector store in tests that ignores `filter=` — tests pass, prod leaks.
- `tenant_id` typed as `str` in one file and `int` in another — type drift = silent filter miss.
- `OPEN_TO_PUBLIC` / `__all__` sentinel in metadata used as a fallback when the filter returns empty.

---

## 7. 跨框架对照 (Cross-Framework Reference Table)

How the same "filter at query" surface looks across the common stacks. All
verified against current docs (May 2026); URLs in References.

### 7.1 LlamaIndex (vendor-agnostic adapter)

```python
from llama_index.core.vector_stores import (
    MetadataFilter, MetadataFilters, FilterOperator,
)

filters = MetadataFilters(
    filters=[MetadataFilter(key="tenant_id",
                            value=ctx.tenant_id,
                            operator=FilterOperator.EQ)],
    # condition=FilterCondition.AND for multi-clause
)
retriever = index.as_retriever(similarity_top_k=8, filters=filters)
```

Supported operators: `==`, `!=`, `>`, `<`, `>=`, `<=`, `in`, `nin`,
`text_match`. Caveat: the in-memory default vector store does *not* honour
filters — use Qdrant/Chroma/Pinecone/Weaviate/pgvector for any real
multi-tenant deployment.

### 7.2 LangChain (vendor-agnostic adapter)

```python
docs = vectorstore.similarity_search(
    query="Q3 roadmap", k=8,
    filter={"tenant_id": ctx.tenant_id},        # simple equality
)

# Chroma-style operators:
docs = vectorstore.similarity_search(
    query="Q3 roadmap", k=8,
    filter={"$and": [
        {"tenant_id": {"$eq": ctx.tenant_id}},
        {"deleted":   {"$eq": False}},
    ]},
)
```

Filter syntax is *vendor-passed-through* — Chroma uses `$and`/`$or`/`$eq`,
Pinecone uses its own dialect, etc. The filter is forwarded to the vendor;
LangChain does not enforce.

### 7.3 Pinecone (raw SDK)

```python
index.query(
    namespace=ctx.tenant_id,                    # primary isolation
    vector=emb, top_k=8,
    filter={"tenant_id": {"$eq": ctx.tenant_id}, # defence-in-depth
            "doc_type":  {"$in": ["policy", "wiki"]}},
    include_metadata=True,
)
```

Operators: `$eq`, `$ne`, `$gt`, `$gte`, `$lt`, `$lte`, `$in` (≤10000 values),
`$nin`, `$and`, `$or`. Disk-based metadata filtering (2025) lets high-
cardinality filters scale without memory blow-up.

### 7.4 Weaviate (raw client)

```python
collection = client.collections.get("Docs").with_tenant(ctx.tenant_id)
res = collection.query.near_vector(
    near_vector=emb, limit=8,
    filters=Filter.by_property("doc_type").equal("policy"),
)
```

`multiTenancyConfig.enabled=True` makes the `tenant=` handle mandatory — the
client *cannot* issue a tenant-less query.

### 7.5 Qdrant (raw client)

```python
client.query_points(
    collection_name="docs",
    query=emb, limit=8,
    query_filter=models.Filter(must=[
        models.FieldCondition(key="tenant_id",
            match=models.MatchValue(value=ctx.tenant_id)),
        models.FieldCondition(key="doc_type",
            match=models.MatchValue(value="policy")),
    ]),
)
```

With `is_tenant=True` on the payload index, Qdrant co-locates per-tenant
vectors on shared shards and accelerates the filter.

### 7.6 Chroma (raw client)

```python
collection.query(
    query_embeddings=[emb], n_results=8,
    where={"$and": [
        {"tenant_id": {"$eq": ctx.tenant_id}},
        {"deleted":   {"$eq": False}},
    ]},
)
```

Same `where=` on `get/update/delete`. Operators: `$eq, $ne, $gt, $gte, $lt,
$lte, $in, $nin, $contains, $not_contains, $and, $or`.

### 7.7 pgvector (raw SQL)

```sql
-- Per-request:
SET LOCAL app.current_tenant = :tenant_id;

SELECT chunk_id, body, 1 - (embedding <=> :query_vec) AS score
  FROM chunks
 ORDER BY embedding <=> :query_vec
 LIMIT 8;
-- RLS policy filters tenant_id; no WHERE clause needed in app code.
```

The RLS policy (OP-08) makes the filter unforgeable from the application
code.

### 7.8 Side-by-side: "is the filter enforced if I forget it?"

| Stack | Forgot the filter → what happens |
|---|---|
| Pinecone (namespace only) | Wrong namespace = wrong tenant; namespace argument missing = default `""` namespace (cross-tenant if you wrote there) |
| Weaviate (MT enabled) | Client raises — no tenant = no operation |
| Qdrant (`is_tenant` only) | Returns *all tenants*' vectors — silent leak |
| Chroma | Returns all docs — silent leak |
| pgvector + RLS | Returns nothing for unauthenticated tenant context — safe by default |
| LlamaIndex / LangChain wrappers | Forwards whatever you (don't) pass — same as underlying vendor |

This table is the argument for Principle 3: pick a stack where the failure
mode is "loud" (Weaviate, pgvector+RLS) **or** add OP-10 runtime assertion
on stacks where the failure mode is "silent" (Qdrant, Chroma, raw
Pinecone-without-namespace).

---

## References

### Primary vendor docs (cited inline above)

- Pinecone — *Implement multitenancy using namespaces*:
  https://docs.pinecone.io/guides/index-data/implement-multitenancy
- Pinecone — *Namespaces vs. metadata filtering*:
  https://docs.pinecone.io/troubleshooting/namespaces-vs-metadata-filtering
- Pinecone — *Understanding multitenancy*:
  https://docs.pinecone.io/docs/multitenancy
- Pinecone — *Multi-Tenancy in Vector Databases*:
  https://www.pinecone.io/learn/series/vector-databases-in-production-for-busy-engineers/vector-database-multi-tenancy/
- Weaviate — *Multi-tenancy operations*:
  https://docs.weaviate.io/weaviate/manage-collections/multi-tenancy
- Weaviate — *Rethinking Vector Search at Scale (multi-tenancy architecture)*:
  https://weaviate.io/blog/weaviate-multi-tenancy-architecture-explained
- Qdrant — *Multitenancy*:
  https://qdrant.tech/documentation/manage-data/multitenancy/
- Qdrant — *How to Implement Multitenancy and Custom Sharding*:
  https://qdrant.tech/articles/multitenancy/
- Qdrant — *Multitenancy with LlamaIndex*:
  https://qdrant.tech/documentation/examples/llama-index-multitenancy/
- Qdrant — *1.16 Tiered Multitenancy*:
  https://qdrant.tech/blog/qdrant-1.16.x/
- Chroma — *Metadata Filtering*:
  https://docs.trychroma.com/docs/querying-collections/metadata-filtering
- Chroma Cookbook — *Naive Multi-tenancy Strategies*:
  https://cookbook.chromadb.dev/strategies/multi-tenancy/naive-multi-tenancy/
- LlamaIndex — *MetadataFilters* (Qdrant example):
  https://developers.llamaindex.ai/python/examples/vector_stores/qdrant_metadata_filter/
- LlamaIndex — *Hybrid RAG with Qdrant: multi-tenancy, custom sharding*:
  https://developers.llamaindex.ai/python/framework/integrations/vector_stores/qdrant_hybrid_rag_multitenant_sharding/
- LangChain — *Vector stores*:
  https://python.langchain.com/docs/concepts/vectorstores/

### Security / incident references

- we45 — *RAG Systems are Leaking Sensitive Data*:
  https://www.we45.com/post/rag-systems-are-leaking-sensitive-data
- CSO Online — *Securing RAG pipelines in enterprise SaaS*:
  https://www.csoonline.com/article/4163888/securing-rag-pipelines-in-enterprise-saas.html
- Christian Schneider — *RAG security: the forgotten attack surface*:
  https://christian-schneider.net/blog/rag-security-forgotten-attack-surface/
- Kiteworks — *Prevent Data Leakage with Zero-Trust RAG*:
  https://www.kiteworks.com/cybersecurity-risk-management/prevent-data-leakage-rag-pipelines/
- BeyondScale — *Vector Database Security: RAG Compliance & Monitoring*:
  https://beyondscale.tech/blog/vector-database-security-rag-compliance-monitoring
- CVE-2024-41892 (Pinecone post-retrieval RBAC bypass) — cited via we45
  and CSO Online incident write-ups.
- EchoLeak (Microsoft 365 Copilot, late 2025) — cited via CSO Online,
  Sombra Inc. *LLM Security Risks in 2026*.
- Slack AI indirect-prompt-injection retrieval bug (Aug 2024) — cited via
  CSO Online.

### Companion files

- `references/R1-vendor-filter-cheatsheet.md` — exhaustive vendor filter
  syntax with code blocks; safe to paste into PR descriptions.
- `references/R2-cross-tenant-test-recipes.md` — five copy-pasteable
  property tests (LlamaIndex/LangChain × Pinecone/Qdrant/Chroma).
- `intermediate/research_notes.md` — raw research dump (provenance).


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