Abstract
Abstract
Why AI Systems Can Be Tricked by Information That Simply Sounds Important
Trust is one of the most powerful forces shaping human decision-making. People frequently evaluate information not only based on evidence, but also based on perceived authority. Titles such as “expert,” “official guideline,” or “research finding” often influence how strongly a claim is believed. Artificial intelligence systems trained on human language patterns inherit many of these same interpretive tendencies. This paper introduces Authority Injection Attacks, a class of adversarial influence techniques in which attackers manipulate AI reasoning by inserting information that appears authoritative or expert-level. Instead of directly forcing a model to produce incorrect answers through adversarial prompts, the attacker introduces contextual signals that mimic credible sources such as technical reports, institutional statements, expert commentary, or official documentation. Once these signals enter the reasoning environment of the AI system whether through user prompts, retrieved documents, or contextual memory they may exert disproportionate influence on the system’s interpretation of information. Because language models are trained on patterns where authoritative language often correlates with reliable knowledge, the model may assign higher confidence to statements that merely appear authoritative, even when those statements are incorrect or intentionally misleading. Authority Injection Attacks become particularly powerful in environments where AI assistants support decision-making, including enterprise copilots, cybersecurity analysis tools, financial advisory systems, and knowledge retrieval platforms. In these contexts, users often rely on AI-generated explanations to interpret complex technical information. If the reasoning process of the AI system is influenced by injected authority signals, the resulting recommendations may appear highly credible while being based on manipulated context. The danger of authority injection does not lie solely in malicious adversaries. Organizational documentation errors, outdated policy references, and incorrectly cited research findings may also function as authority signals that influence AI reasoning. Over time, repeated exposure to these signals may cause the system to reinforce incorrect assumptions simply because those assumptions appear to originate from credible sources. This research examines how authority signals influence AI reasoning processes, identifies the technical mechanisms through which authoritative framing can distort model interpretation, and explores the potential operational risks associated with authority injection in AI-assisted decision environments. Understanding how AI systems respond to perceived authority is essential for building architectures capable of distinguishing between genuine expertise and carefully crafted illusions of expertise.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "Authority Injection Attacks in AI Assistants (When Confidence, Titles, and “Expert Sources” Quietly Hijack AI Reasoning)", Technical Disclosure Commons, (March 19, 2026)
https://www.tdcommons.org/dpubs_series/9571