Geometric RAG access control isolates policies but not embeddings
A curious user can recover original embeddings from a few hundred pairs and forge policy-compatible queries.
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A curious user can recover original embeddings from a few hundred pairs and forge policy-compatible queries.
The attack mutates tokens without an external LLM and, the authors say, needs up to 6.77 times fewer queries to extract half a knowledge base.
Query-local graph interface induction allows fixed retriever to work on differently built knowledge graphs.
Offline RAG knowledge packs can be hijacked with up to 5.5% of queries exposed due to text-vector mismatch.

The patches fix four vulnerabilities in Langflow versions 1.0.0 to 1.12.8, including two critical ones rated 9.8 CVSS, and affect Hono framework upgrades.
Authors use RAG on Hanja-Korean corpora to build pseudo-labels, cut temporal bias, and report gains over baselines.
Query, judge, and answer agents are trained with separate rewards, including voting labels and curriculum matching.
A RAG system for stock market queries achieves high ROUGE scores and 1.16 second average response time on 100 queries.
DeFT-RAG reduces LLM calls by 12.9% through QA cache reuse while achieving top accuracy on multi-hop benchmarks.
A resident 9B backbone adds about 0.1 GiB per skill and beats bge-reranker-v2-m3 on SciFact and NFCorpus after 39 minutes of fine-tuning.
The only stated figure is a drop from 201 GB to 6 GB; no method or benchmark details were in the feed.
Eleven decision models hit 50.0–67.0% on CONFLICTS, and 10 matched or beat Gemini 2.5 Flash at 4–39x lower billed cost.
Typed decision models classify knowledge conflicts in RAG faster and cheaper than LLMs while preserving response behavior.
Without task-specific training, it beats dense retrieval plus an 8B reranker by 13.8 R@1 on MultiDoc2Dial.
The training-free method clusters expanded retrieval results and prunes suspicious groups before they reach the model.
On 540 messages from 90 cases, retrieval lifted concern-category accuracy from 54.1% to 59.3%.
The authors split memory's net value into gains over raw-history retrieval and losses they call memory tax.
Stripping PoisonedRAG's question prefix drops reimplemented RAGuard from AUROC 0.953 to 0.758 and raises its miss rate to 0.763.
On an isolated Raspberry Pi 5, a validly signed poisoned pack drove correct answers on 30 questions to zero.
The selected hybrid retriever reached 0.94 on Recall@3, R-Precision, MRR@3, and nDCG@3 for 17 of 18 queries.

An 8-second 3.97MB clip left more than 500MB of logs, caches, renders, backups, and safety records.
Raw-RAG ranked first in six of seven settings, ahead of the best agentic system by 0.13 to 5.53 points.
Three techniques predicted a 47% combined gain but delivered 17% together across 450 configurations.
Authors report 0.994 in-distribution recall at a 0.026 false-positive rate, but near-zero recall on PoisonedRAG.
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