Adversarial Answer Engine Optimization - by Akash Narayan

ABSTRACT
Search engine optimisation grew a shadow discipline almost as soon as it appeared: link farms, keyword stuffing,
cloaking and the other tactics that search engines spent two decades policing. Its successor, answer engine optimisation,
is inheriting that shadow, but the target has changed. Where a manipulated search result competed for attention against
nine other links on a page, a manipulated answer arrives as a single synthesised statement, cited and confident, with
nothing beside it for the reader to weigh it against. This paper treats adversarial AEO as a data-poisoning problem: the
manipulation of the corpus an aggregation agent retrieves from, in order to control what it says. It sets out a taxonomy
spanning commercial optimisation, the exploitation of sparse-data queries, indirect prompt injection and coordinated
disinformation, and reviews evidence that a handful of planted documents can steer a retrieval system with success rates
above ninety per cent. It argues that aggregation agents are structurally more exposed than ranked search, because they
concentrate authority into one answer and lend the trust of the aggregator to whatever source it cites. It proposes a
layered defence, from provenance and source vetting at ingestion to corroboration at retrieval and inspectable citation at
output, and connects these to earlier work in this series on auditability, confirmatory friction and misplaced trust. It
closes with the defender's asymmetry and the unresolved question of who decides which sources are trustworthy.

Keywords: adversarial AEO; answer engine optimisation; data poisoning; retrieval-augmented generation; aggregation
agents; indirect prompt injection; LLM grooming; content provenance; disinformation; trust calibration

Disclosure by Author: Portions of this manuscript were prepared with the assistance of generative AI tools for research synthesis, drafting, and editing. The models used were Indian Sovereign AI models provided by Ayen.