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Artificial Adaptive Control Language (AACL): State-Bound Ephemeral Grammars for Secure Web Actions

Authors: M Venkata Sasidhar Kaushik;

Artificial Adaptive Control Language (AACL): State-Bound Ephemeral Grammars for Secure Web Actions

Abstract

Web applications commonly rely on widely deployed defenses such as tokens, headers, andserver-side validation logic to mitigate request forgery, tampering, and replay. While effectiveagainst many threats, these mechanisms primarily establish authenticity and coarse integrity,and do not formally enforce the precise structure and single-use semantic intent of individualstateful actions.This paper introduces AACL, a language-theoretic control framework in which the serverdynamically issues an ephemeral, intent-bound grammar for each sensitive action instance. Eachrequest is treated as a short-lived language instance and validated through formal recognition(e.g., DFA-driven parsing) before execution. Grammars are state-bound and invalidated after useor expiry, enforcing structural conformance and single-use semantics under explicit threat-modelassumptions.We formalize the AACL model, describe its integration into a web application architecture,and present a prototype demonstrating how ephemeral grammars reduce replay and structuralmutation attacks. AACL complements existing authentication and authorization mechanisms byintroducing a state-aware, language-level enforcement layer for web interactions.

Keywords

Replay Attacks, Web Security, Cybersecurity, Language-Theoretic Security, Ephemeral Grammars, Formal Methods

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average