LLM08: Vector and Embedding Weaknesses · 2025

Weaknesses in vectors and retrieval can leak information, mix tenants, invert embeddings or manipulate a RAG system.

Summary

Weaknesses in vectors and retrieval can leak information, mix tenants, invert embeddings or manipulate a RAG system.

How the attack works

Similarity search does not filter by tenant; it retrieves a document from another organization and includes it in the answer.

Example

Vulnerable · Pseudocode

hits = vectors.search(embed(query))
reply = llm(query, context=hits)
# No tenant or document authorization

Fix: Apply tenant scope and document authorization within the query; authenticate ingestion sources.

Prevention

Partition data and apply granular access controls.
Authenticate sources and record ingestion provenance.
Detect anomalous queries and vectors.

Lineage

2025 · LLM08: Vector and Embedding Weaknesses Moves down to rank 9 in 2026
2026 · LLM09: Vector and Embedding Weaknesses Moves down from rank 8

2025:LLM08 → 2026:LLM09: Vector and Embedding Weaknesses moves down from rank 8 to 9.

Sources

Open official source ↗

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