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Public story · 2026-03-04 · source-backed
Adversaries inject documents into RAG knowledge bases that trigger safety refusals on benign queries. Weaponizes alignment homogeneity itself — high cross-model transfer rates. The alignment-as-vulnerability paradox. (arXiv 2603.03919)
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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...
Dahal and Xiong target injected documents that are individually benign but create false associations once aggregated, which is structurally invisible to any per-document filter (arXiv 2607.20437). TopoGuard builds a semantic similarity graph over the retrieved set and flags ma...
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...
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 reliability protocol (arXiv:2606.07783) tests RAG on factoid questions the model already answers correctly without retrieval, then injects clean, misleading, and mixed evidence to measure when retrieved content overrides correct internal knowledge. It introduces a parame...
The study extracted 130 clean atomic state transitions from 707 real issues in SWE-bench Lite and Verified. Plain RAG scored 0.57-0.59 answer accuracy; an LLM reranker didn't help and added latency, about 18 seconds against 2.1. A (subject, relation, object) supersession memor...
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