Blog

TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption: what happens when an adversary uses AI to flood the web with convincing-but-false content about an entity? I’m building an ethical, fabricated-entity testbed to measure exactly how much fake content it takes to make production AI-search systems assert falsehoods — a “vulnerability scaling law” for RAG.

This is an ongoing project. This post describes the research questions, the experimental design, and what I’m building right now.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.