The paper proposes a unified closed-loop threat taxonomy to systematically analyze and defend foundation models by explicitly framing the bidirectional security interactions between data and models.

This paper provides the first comprehensive review of threats and defenses specifically targeting on-device AI inference, revealing a significant imbalance where certain attack types, like adversarial…

The paper introduces AutoMIA, a novel framework that uses LLM agents to automate the discovery and implementation of Membership Inference Attacks (MIAs), achieving state-of-the-art performance by syst…

This paper investigates a novel physical backdoor attack against Deep Automatic Modulation Classifiers (AMC) in wireless communications, demonstrating that an adversary using Explainable AI (XAI) can…

The paper introduces Constraint-Guided Semantic Testing (ConSeT), a novel framework that systematically finds critical, pre-authentication vulnerabilities in 5G User Equipment (UE) by exploiting seman…

The paper introduces Trace, a forensic framework that fingerprints the model family of autonomous AI attack agents using terminal behavior, enabling subsequent prompt injection to extract system promp…

This paper demonstrates that a specific routing-layer defense mechanism in OLSR-based MANETs can be inferred from passively observable routing and control-plane behavior, even when the defense operate…

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these honeypots provide substantially longer and harder-to-detect…

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these systems provide substantially longer and harder-to-detect i…

This paper proposes an explainable threat attribution system for IoT networks that uses SHAP and flow behavior modeling to accurately classify and explain over 30 distinct attack variants into 8 meani…

This paper proposes a lightweight, multi-layer Machine Learning-based security framework for Industrial IoT (IIoT) to enhance trust convergence and detect advanced threats.

The paper introduces KBF, a low-cost black-box auditing protocol that fingerprints LLM APIs by analyzing stable numerical recall near the knowledge boundary, successfully detecting numerous model subs…

The paper introduces KBF, a novel black-box auditing protocol that fingerprints LLM APIs by analyzing stable numerical recall near the knowledge boundary, effectively detecting model substitutions and…

The paper proposes DA-GC, a certified causal attribution framework that accurately identifies cross-slice attack origins in 6G networks under strict real-time latency constraints by systematically mod…

This paper systematically analyzes the forensic artifacts left by popular local LLM runners (Ollama, LM Studio, llama.cpp) on Windows and Linux, providing a foundational corpus of evidence for digital…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper proposes VerFU, a client-verifiable federated unlearning framework for low-altitude wireless networks that allows devices to ensure the server accurately removes their historical data contri…

This paper demonstrates that current AI model extraction defenses, which assume attacks come from single sources, are easily bypassed by coordinated, distributed threat actors.

The paper introduces PINSIGHT, a novel methodology that rigorously assesses Wi-Fi PIN code inference attacks by separating environmental effects from typing effects, concluding that current state-of-t…