Abstract
As artificial intelligence becomes central to modern cybersecurity operations, defensive systems increasingly rely on internal knowledge representations, long-term memory modules, and retrieval-based reasoning mechanisms. These components enable adaptive learning and contextual awareness, but they also introduce a largely unexamined security vulnerability: the integrity of what the system knows. Existing research on AI security has primarily focused on attacks that manipulate outputs, bypass detection mechanisms, or compromise execution pathways. Far less attention has been given to attacks that corrupt internal knowledge structures themselves. This paper introduces Epistemic Corruption Attacks, a novel class of adversarial strategies that target the belief systems, embeddings, memory stores, and knowledge repositories of AI-based security platforms. Rather than triggering immediate failures, these attacks gradually distort internal representations, leading to persistent misinterpretation of threats and normalization of unsafe patterns. We develop a unified theoretical framework for understanding epistemic corruption, propose a taxonomy of attack vectors, and analyze realistic deployment scenarios in cybersecurity environments. Our findings demonstrate that compromised knowledge integrity can undermine system reliability even in the absence of ongoing exploitation. This work establishes epistemic security as a foundational requirement for trustworthy AI-driven defense systems.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatanagar, Pranav Mr, "Epistemic Corruption Attacks: Poisoning What AI Security Systems “Know” Rather Than What They “Do”", Technical Disclosure Commons, (February 09, 2026)
https://www.tdcommons.org/dpubs_series/9296