Retrieval Pivot Attacks in Hybrid RAG: Measuring and Mitigating Amplified Leakage from Vector Seeds to Graph Expansion
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910044656762880 |
|---|---|
| author | Thornton, Scott |
| author_facet | Thornton, Scott |
| contents | Hybrid Retrieval-Augmented Generation (RAG) pipelines combine vector similarity search with knowledge graph expansion for multi-hop reasoning. We show that this composition introduces a distinct security failure mode: a vector-retrieved "seed" chunk can pivot via entity links into sensitive graph neighborhoods, causing cross-tenant data leakage that does not occur in vector-only retrieval. We formalize this risk as Retrieval Pivot Risk (RPR) and introduce companion metrics Leakage@k, Amplification Factor, and Pivot Depth (PD) to quantify leakage magnitude and traversal structure.
We present seven Retrieval Pivot Attacks that exploit the vector-to-graph boundary and show that adversarial injection is not required: naturally shared entities create cross-tenant pivot paths organically. Across a synthetic multi-tenant enterprise corpus and the Enron email corpus, the undefended hybrid pipeline exhibits high pivot risk (RPR up to 0.95) with multiple unauthorized items returned per query. Leakage consistently appears at PD=2, which we attribute to the bipartite chunk-entity topology and formalize as a proposition.
We then show that enforcing authorization at a single location, the graph expansion boundary, eliminates measured leakage (RPR near 0) across both corpora, all attack variants, and label forgery rates up to 10 percent, with minimal overhead. Our results indicate the root cause is boundary enforcement, not inherently complex defenses: two individually secure retrieval components can compose into an insecure system unless authorization is re-checked at the transition point. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08668 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Retrieval Pivot Attacks in Hybrid RAG: Measuring and Mitigating Amplified Leakage from Vector Seeds to Graph Expansion Thornton, Scott Cryptography and Security Information Retrieval Machine Learning Hybrid Retrieval-Augmented Generation (RAG) pipelines combine vector similarity search with knowledge graph expansion for multi-hop reasoning. We show that this composition introduces a distinct security failure mode: a vector-retrieved "seed" chunk can pivot via entity links into sensitive graph neighborhoods, causing cross-tenant data leakage that does not occur in vector-only retrieval. We formalize this risk as Retrieval Pivot Risk (RPR) and introduce companion metrics Leakage@k, Amplification Factor, and Pivot Depth (PD) to quantify leakage magnitude and traversal structure. We present seven Retrieval Pivot Attacks that exploit the vector-to-graph boundary and show that adversarial injection is not required: naturally shared entities create cross-tenant pivot paths organically. Across a synthetic multi-tenant enterprise corpus and the Enron email corpus, the undefended hybrid pipeline exhibits high pivot risk (RPR up to 0.95) with multiple unauthorized items returned per query. Leakage consistently appears at PD=2, which we attribute to the bipartite chunk-entity topology and formalize as a proposition. We then show that enforcing authorization at a single location, the graph expansion boundary, eliminates measured leakage (RPR near 0) across both corpora, all attack variants, and label forgery rates up to 10 percent, with minimal overhead. Our results indicate the root cause is boundary enforcement, not inherently complex defenses: two individually secure retrieval components can compose into an insecure system unless authorization is re-checked at the transition point. |
| title | Retrieval Pivot Attacks in Hybrid RAG: Measuring and Mitigating Amplified Leakage from Vector Seeds to Graph Expansion |
| topic | Cryptography and Security Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2602.08668 |