Manifest-Assisted Retrieval in Artificial Intelligence Search: Evidence Gaps and a Security Evaluation
Read the original on arXiv AI →The paper reports a non‑systematic audit of 39 external sources up to August 16, 2026, and targeted updates through October 14, examining the incremental security and utility impact of consuming a file named /llms.txt while keeping ordinary retrieval constant. A 15‑tool study found no automatic requests in 45 trials, and a log study showed that 97% of domains with detected files received no recorded requests in May 2026. A pilot with 24 tasks across eight sites and 120 real model responses identified attacker‑target adoption in 21 of 24 raw adversarial responses, while the URLs‑only condition had no known adoptions; the pilot measures output changes conditioned on supplied evidence without live retrieval or external actions.
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