adriannoes/awesome-agentic-ai/assessing-vector-and-embedding-weaknesses

Assessing Vector and Embedding Weaknesses

Authorized use only: These tests interact with vector stores and embedding models in RAG systems you own or are authorized to assess. Embedding inversion and cross-tenant probing against systems you do not control may expose third-party data and is prohibited without authorization.

Overview

Retrieval-Augmented Generation (RAG) systems convert documents into embedding vectors stored in a vector database (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) and retrieve the nearest vectors to ground LLM responses. OWASP LLM08:2025 Vector and Embedding Weaknesses covers the security risks unique to this layer:

• Embedding inversion — embeddings are not one-way. A trained inversion model (or a black-box reconstruction attack) can recover substantial portions of the original text from its vector, leaking source documents (maps to MITRE ATLAS AML.T0024.001 Invert ML Model).
• Membership inference — querying whether a specific record contributed to the corpus (AML.T0024.000).
• Cross-tenant / multi-tenant leakage — when one namespace/collection is shared or filter isolation is missing, a tenant retrieves another tenant's chunks.
• Knowledge-base poisoning — an attacker who can write to the corpus inserts crafted chunks that dominate retrieval (high cosine similarity to expected queries) and carry indirect prompt-injection payloads.
• Retrieval manipulation — adversarial documents tuned to be retrieved for many unrelated queries ("retrieval hijacking").

The parent technique is AML.T0024 — Exfiltration via ML Inference API: an attacker uses legitimate inference/query access to exfiltrate data (source text via inversion, membership, or model extraction). This skill provides a repeatable assessment of all five weakness classes.

When to Use

• During a security assessment of any RAG / vector-search application (OWASP LLM08 coverage).
• When a vector store is multi-tenant and you must prove namespace/metadata isolation.
• When the corpus accepts user-supplied or third-party documents (poisoning surface).
• When the embedding endpoint is externally reachable (inversion/membership surface).
• When validating retrieval-filtering controls before go-live.

Prerequisites

• Authorization and scope covering the target embedding endpoint and vector store.
• Python 3.10+.
• Read (and, for poisoning tests, write) access to a test collection — never the production corpus.

# Vector DB clients + embeddings + similarity tooling
python -m pip install numpy scikit-learn sentence-transformers
python -m pip install qdrant-client chromadb pinecone-client weaviate-client
# (optional) text-embedding inversion research baseline
python -m pip install vec2text

Objectives

• Measure embedding-inversion exposure on the target embedding model.
• Run a membership-inference probe against the corpus.
• Test multi-tenant isolation (namespace, metadata filter, RBAC) for cross-tenant leakage.
• Inject benign poisoned chunks into a test collection and measure retrieval dominance.
• Detect indirect prompt-injection content surviving in retrieved chunks.
• Recommend controls: tenant-scoped filters, content validation, embedding-access limits.

MITRE ATT&CK Mapping

Workflow

Step 1: Inventory the RAG pipeline

Document the embedding model + dimensions, the vector store and its tenancy model, the chunking strategy, retrieval top_k and similarity metric (cosine/dot/L2), and any metadata filters applied at query time.

# Example: inspect a Qdrant collection
from qdrant_client import QdrantClient
client = QdrantClient(url="http://localhost:6333")
info = client.get_collection("docs")
print(info.config.params.vectors)   # size + distance metric
print(client.count("docs"))         # corpus size

Step 2: Test embedding-inversion exposure

Embeddings of similar text are close; an attacker with the embedding endpoint can iteratively reconstruct text whose embedding matches a target vector. Measure how much a nearest-neighbour-in-embedding-space recovers, using cosine similarity between candidate reconstructions and the target.

import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
model = SentenceTransformer("all-MiniLM-L6-v2")
secret = "Patient John Doe, MRN 553120, diagnosed with hypertension."
target_vec = model.encode([secret])
# Attacker has only target_vec and the embedding endpoint. Hill-climb candidate text.
candidates = [
    "Patient name and medical record number with a diagnosis.",
    "John Doe medical record hypertension diagnosis",
    "Patient John Doe MRN diagnosed hypertension",
]
cand_vecs = model.encode(candidates)
sims = cosine_similarity(target_vec, cand_vecs)[0]
for c, s in sorted(zip(candidates, sims), key=lambda x: -x[1]):
    print(f"{s:.3f}  {c}")
# High similarity for a near-verbatim guess => inversion risk is real for this model.

For a research-grade reconstruction baseline, vec2text can be used against compatible embedding models to demonstrate full-text recovery.

Step 3: Membership inference

Determine whether a specific document is in the corpus by measuring the top-1 retrieval similarity for an exact-quote query: in-corpus items return a markedly higher max similarity than out-of-corpus controls.

def membership_score(client, collection, embed, text):
    vec = embed([text])[0].tolist()
    hits = client.search(collection_name=collection, query_vector=vec, limit=1)
    return hits[0].score if hits else 0.0
in_corpus = membership_score(client, "docs", model.encode, "<exact quote from a known chunk>")
control  = membership_score(client, "docs", model.encode, "An unrelated random sentence.")
print(f"in-corpus={in_corpus:.3f}  control={control:.3f}  delta={in_corpus-control:.3f}")
# A large positive delta indicates the item is in the corpus (membership leak).

Step 4: Test multi-tenant isolation

Confirm that tenant B cannot retrieve tenant A's chunks. Issue tenant-B-authenticated queries that should be filtered, and verify no tenant-A tenant_id appears in results.

# Query as tenant B; expect ONLY tenant_id == "B" results.
from qdrant_client.models import Filter, FieldCondition, MatchValue
vec = model.encode(["confidential salary information"])[0].tolist()
hits = client.search(
    collection_name="docs",
    query_vector=vec,
    limit=10,
    query_filter=Filter(must=[FieldCondition(key="tenant_id", match=MatchValue(value="B"))]),
)
leaked = [h for h in hits if h.payload.get("tenant_id") != "B"]
print("CROSS-TENANT LEAK" if leaked else "isolation OK", "->", len(leaked), "foreign rows")
# Critical test: repeat WITHOUT the filter to confirm the server, not the client,
# enforces isolation. If unfiltered queries return tenant A data, isolation is client-side only.
hits_nofilter = client.search(collection_name="docs", query_vector=vec, limit=10)
print("server-side isolation FAILS" if any(h.payload.get("tenant_id") != "B" for h in hits_nofilter) else "OK")

Step 5: Knowledge-base poisoning (test collection only)

Insert a benign poisoned chunk crafted to be retrieved for many unrelated queries, then measure how often it appears in top_k.

from qdrant_client.models import PointStruct
# Benign marker payload (no real injection) to measure retrieval dominance.
poison = "POISON-CANARY. " + " ".join(
    ["password reset billing refund account login support error help"] * 8
)
client.upsert("docs_test", points=[
    PointStruct(id=999999, vector=model.encode([poison])[0].tolist(),
                payload={"tenant_id": "B", "source": "poison-test"})
])
queries = ["how do I get a refund", "reset my password", "what is the weather"]
for q in queries:
    hits = client.search("docs_test", model.encode([q])[0].tolist(), limit=5)
    dominated = any(h.payload.get("source") == "poison-test" for h in hits)
    print(f"{'POISON in top5' if dominated else 'clean'}: {q}")

Step 6: Detect indirect prompt injection in retrieved chunks

Scan retrieved chunk text for injection markers before it is concatenated into the prompt.

import re
INJECTION_PATTERNS = [
    r"ignore (all|previous|the above) instructions",
    r"system prompt", r"you are now", r"disregard", r"</?(system|instructions)>",
]
def chunk_is_injection(text):
    low = text.lower()
    return [p for p in INJECTION_PATTERNS if re.search(p, low)]
for hit in client.search("docs", model.encode(["help"])[0].tolist(), limit=10):
    flags = chunk_is_injection(hit.payload.get("text", ""))
    if flags:
        print("INDIRECT INJECTION in chunk", hit.id, flags)

Step 7: Report and remediate

• Inversion/membership: rate-limit and authenticate the embedding endpoint; avoid returning raw similarity scores; restrict who can query embeddings.
• Cross-tenant: enforce tenant filters server-side (separate collections/namespaces per tenant where feasible); never rely on client-supplied filters.
• Poisoning: validate and provenance-tag every ingested chunk; scan inputs for injection; cap any single source's share of retrieval.
• Indirect injection: sanitize retrieved chunks and apply output guardrails (see defending-llms-with-guardrails).

Tools and Resources

Validation Criteria

• [ ] RAG pipeline inventoried (embedding model, store, tenancy, metric, top_k, filters).
• [ ] Embedding-inversion exposure measured and rated.
• [ ] Membership-inference delta computed for in-corpus vs control items.
• [ ] Multi-tenant isolation tested both with and without client filters (server-side enforcement confirmed).
• [ ] Poisoning dominance measured in a test collection only.
• [ ] Retrieved chunks scanned for indirect-injection content.
• [ ] Findings reported with remediation for each weakness class.
• [ ] No production corpus modified during the assessment.