Cornell researchers demonstrate that single user-generated comments with lexically similar text to queries reliably manipulate LLM outputs and citations—a trivial attack vector exploitable via content placement on Reddit, Wikipedia, and similar platforms.
Summary
If you build with RAG systems or integrate deep research agents, you're importing poisoned training data from UGC sites at scale. This breaks the reliability contract developers rely on when citing scraped sources, forcing you to either add adversarial filtering layers or distrust external citations entirely.
Why it matters
If you build with RAG systems or integrate deep research agents, you're importing poisoned training data from UGC sites at scale. This breaks the reliability contract developers rely on when citing scraped sources, forcing you to either add adversarial filtering layers or distrust external citations entirely.
Implementation verdict
Replaces the assumption that UGC-sourced citations are trustworthy. Requires validation of cited content against author/domain reputation, deduplication of similar claims across sources, and lexical anomaly detection for suspiciously query-aligned text. Not production-ready until your retrieval pipeline includes poison detection—consider it now if you already cite Reddit/Wikipedia in agent outputs.
Sources
Dev Signal
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