The Cognitive Firewall is a hybrid edge-cloud defense architecture that significantly reduces the attack success rate of Indirect Prompt Injection against browser-based AI agents by combining local vi…

This study provides the first systematic measurement of prompt injection attacks in a real-world LLM-based resume screening application, finding that approximately 1% of resumes contain hidden injecti…

This study provides the first large-scale measurement of prompt injection attacks in real-world LLM-based resume screening, finding that approximately 1% of resumes contain hidden injections.

The paper proposes a structured prompt engineering framework to enhance the integrity and reliability of Chain-of-Thought (CoT) reasoning in LLMs, demonstrating significant improvements in security-se…

The paper introduces GenTI, a novel LLM-driven benchmark and dataset, to automatically generate high-quality, deployable IDPS rules for detecting unseen and zero-day cyber attacks.

BodhiPromptShield is a policy-aware framework that mediates prompt privacy by detecting sensitive data and replacing it with secure placeholders across multiple stages (retrieval, memory, tools) to pr…

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 Prompt Control-Flow Integrity (PCFI), a priority-aware runtime defense that models LLM prompts as structured segments to intercept prompt injection attacks with high accuracy and…

ZERO-APT introduces a novel closed-loop adversarial framework for automated penetration testing that simulates attacks against an intelligent, real-time defending system, achieving a high attack succe…

This paper investigates how Generative AI enables scalable, hyper-realistic fraud in Chinese e-commerce by fabricating product defect evidence, proposing new defense mechanisms like verifiable materia…

HunterAgent is a neuro-symbolic framework that reconstructs causal attack chains from fragmented, anti-forensics-corrupted logs, achieving high accuracy while drastically reducing hallucination.

The paper introduces a novel framework using steganographic canary files to detect and block unauthorized processing of sensitive documents by LLMs, even when the data passes through traditional secur…

The paper introduces an end-to-end framework that not only detects network intrusions using deep learning but also generates actionable, citation-grounded mitigation reports using a Retrieval-Augmente…

The paper introduces Landseer, a modular framework designed to systematically evaluate and compose multiple machine learning defenses to address complex, real-world security requirements.

AttackEval systematically evaluates the effectiveness of 250 prompt injection prompts across ten attack categories, finding that composite and obfuscation attacks are highly effective against current…

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…

The paper introduces IOCRegex-gen, an automated LLM-based system that converts Indicators of Compromise (IOCs) into syntactically and semantically correct regular expressions, achieving high accuracy…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by GenAI, moving beyond traditional react…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by Generative AI, moving beyond tradition…

The paper proposes a confidence-aware, multi-layered Cloud-IDS pipeline that integrates adaptive Q-Learning, Chroma memory, and LLM semantic analysis to enhance detection accuracy and reduce reliance…