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Research2026-09-24 · source-backed
It avoids the correlated near-duplicates that similarity-aware and conflict-aware defenses filter on. Across two QA datasets, three LLMs and nine RAG configurations it matches or beats prior attacks in most settings, with its biggest gains against clustering- and conflict-aware defenses (arXiv).
Each link below shares sources, entities, or timing with this story.
InceptionRAG fragments the payload into a chain of dormant passages, each benign under isolated inspection, that lead the model to self-deduce the target misinformation through multi-hop reasoning when retrieved together. Across three datasets and three LLMs it exceeds 80% ASR...
RAGAS-style evaluation checks correctness against a frozen snapshot, which means routine document updates and corrections can silently break production without moving a dashboard. This ASE 2026 paper defines 11 mutation operators perturbing at both the pre-chunk index level an...
A new paper shows LLMs can be pushed toward misleading conclusions when fabricated "evidence" gets injected into context. No exploit, no jailbreak, just planted false context shifting the stated answer. Source: arXiv This is the threat model RAG builders keep underrating. Your...
arXiv 2608.00765 compresses retrieved docs into query-conditioned visual representations, sidestepping the trade-off where hard compression is query-aware but weak and soft compression is strong but needs costly offline encoding. Beats both baselines across varying retrieval d...
Existing RAG poisoning is filterable because the adversarial chunk contains the query. CamoDocs chunks synthesized benign and adversarial drafts, swaps selected tokens in benign chunks for dispersion tokens that spread the poisoned embeddings, then coherence-filters for readab...
1. Flip your multi-model pipeline to review-then-generate. Instead of using a reasoning model to plan before code generation, let the specialist generate freely and use reasoning tokens for review. Paper shows 90.2% pass@1 vs 87.2% for the planning pattern. Source 2. Audit you...
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