The Vanishing Ground: Link Rot, Source Spoofing, and the Epistemic Fragility of Web-Grounded AI

Abstract

Web-grounded artificial intelligence systems increasingly rely on online information to retrieve evidence, generate answers, and support knowledge-intensive tasks. Yet the web is neither a stable archive nor a uniformly trustworthy evidentiary environment. Sources may disappear, change without notice, become inaccessible, or be deliberately manipulated. These conditions create an epistemic vulnerability that extends beyond model-level errors: even a capable system may produce unreliable conclusions when the evidence available to it is incomplete, altered, or inauthentic.

This paper develops a multidisciplinary narrative review of three interrelated vulnerabilities: link rot and content drift, selective information disappearance, and source spoofing or data manipulation. Drawing on research in digital preservation, information science, cybersecurity, political communication, and social epistemology, it examines how changes in the availability and integrity of online evidence can affect web-grounded AI systems. Existing research documents substantial rates of digital decay and demonstrates the feasibility of certain attacks on web-scale training datasets. However, these findings do not establish the prevalence or coordinated deployment of comparable attacks against contemporary AI search and retrieval systems.

The paper distinguishes source unavailability from source falsity, and technical susceptibility from demonstrated exploitation. It argues that epistemic reliability should be evaluated not only in terms of answer accuracy, but also through the availability, authenticity, temporal stability, and traceability of the evidence supporting an answer. It proposes a research agenda centered on source provenance, temporal verification, archival preservation, retrieval integrity, and systematic testing of how source changes affect generated outputs. The central claim is that the reliability of web-grounded AI depends partly on the survival and integrity of its evidentiary environment, not solely on the capabilities of the model processing it.

Keywords: web-grounded AI; link rot; content drift; source spoofing; data poisoning; retrieval-augmented generation; epistemic reliability; digital preservation

Disclaimer

This paper presents a multidisciplinary narrative review and a conceptual framework for examining the epistemic vulnerabilities of web-grounded artificial intelligence. It does not claim that the prospective scenarios discussed have occurred at scale, that source disappearance necessarily indicates deliberate manipulation, or that any specific organization is responsible for coordinated interference. Throughout the text, demonstrated technical vulnerabilities, reported cases, theoretical risks, and proposed safeguards are strictly distinguished.

Furthermore, the psychological literature is utilized to contextualize potential effects on memory and reasoning, rather than to establish a direct causal relationship between web instability and individual or collective memory distortion. Accordingly, the claims concerning downstream effects on AI-generated knowledge should be understood as foundational research questions that require further empirical validation.

Author’s Note

This paper examines the evidentiary instability of the online environment on which web-grounded artificial intelligence partly depends. It brings together research on link rot, content drift, source impersonation, selective information removal, and data manipulation to examine how changes in accessible evidence may affect the reliability and later verifiability of AI-generated claims.

The argument does not assume a coordinated campaign by any particular corporation, government, or other actor. Source disappearance, content revision, ordinary technical failure, institutional decisions, and deliberate manipulation are distinct processes that may produce superficially similar outcomes. None, by itself, establishes malicious intent or demonstrates that an AI-generated error resulted from external interference. Claims about intent, prevalence, and causation require evidence beyond the observation of a missing, altered, or inconsistent source.

Research on human memory provides a relevant but limited point of comparison. Studies of misinformation and its continued influence show that inaccurate information can affect later recollection and reasoning under particular conditions (Loftus, 1992; Walter & Tukachinsky, 2020). These findings do not establish that the disappearance of digital sources necessarily distorts memory, that inaccessible records cause people to accept false accounts, or that such effects occur uniformly. The relationship between source instability, individual recollection, and the reconstruction of shared accounts remains an empirical question.

The paper’s narrower purpose is to make the evidentiary dependencies of web-grounded AI more explicit. A plausible or even currently accurate answer is not necessarily one whose supporting evidence can be independently recovered and checked later. Evaluating reliability therefore requires attention not only to answer quality, but also to source availability, content integrity, provenance, and the possibility of reconstructing the evidentiary path. The proposed safeguards and evaluation framework are research directions rather than established solutions. The aim is to make claims about evidence loss testable without presuming either systematic manipulation or inevitable failure.