Abstract
As artificial intelligence systems increasingly rely on intent-based security mechanisms to regulate user behavior, the nature of adversarial interaction is undergoing a subtle but significant shift. Rather than attempting to bypass safeguards through explicit policy violations or recognizable malicious patterns, attackers are increasingly manipulating meaning itself. This paper introduces Intent-Obfuscation Attacks, a class of semantic evasion techniques that exploit the gap between surface-level language compliance and the underlying objectives inferred by AI systems. Unlike traditional prompt injection or keyword-based attacks, intent-obfuscation operates through linguistic ambiguity, contextual indirection, and gradual semantic drift. Individual interactions appear benign and policy-compliant, yet when interpreted collectively, they guide the system toward restricted or unsafe outcomes. These attacks exploit the probabilistic nature of intent classification, where meaning is inferred rather than explicitly stated, and where security decisions depend on contextual interpretation rather than fixed rules. This work analyzes how intent detection is implemented in contemporary AI security architectures and why these mechanisms are inherently fragile when confronted with deliberate manipulation of meaning. Through realistic, real-world inspired scenarios drawn from enterprise AI assistants, automated cybersecurity tools, and decision-support systems, the paper demonstrates how intent-obfuscation can evade detection without triggering conventional alerts or moderation systems. The paper further examines why existing defenses struggle to identify such attacks, highlighting limitations in prompt-level filtering, output moderation, and static risk assessment. Finally, it discusses broader implications for AI security governance and outlines future research directions focused on longitudinal semantic analysis, intent robustness, and human-in-the-loop oversight. By shifting attention from what users say to how meaning evolves over time, this work contributes to a deeper understanding of emerging semantic attack surfaces in AI systems.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatanagar, Pranav Mr, "INTENT-OBFUSCATION ATTACKS: EVADING AI SECURITY BY MANIPULATING MEANING", Technical Disclosure Commons, (February 09, 2026)
https://www.tdcommons.org/dpubs_series/9294