This paper evaluates the assumption that decoy placement is possible for all MITRE ATT&CK v18.1 techniques and introduces a rubric for infrastructure deception.

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…

The paper proposes a vision for system-level defenses against indirect prompt injection attacks targeting AI agents, emphasizing structured control and human oversight.

A new LLM-based honeypot design, AdvancedShelLM, is presented using a multi-agent, multi-LLM architecture to improve believability and reduce identifiability.

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…

The paper identifies a critical vulnerability, the Camouflage Detection Gap (CDG), where standard LLM injection detectors fail dramatically when malicious payloads mimic the target domain's language a…

The paper introduces the Canonical Security Telemetry Substrate (CSTS), a standardized, AI-ready foundation designed to harmonize fragmented and heterogeneous cybersecurity data into a unified model f…

The paper argues that Agentic AI fundamentally breaks the historical security tradeoff between deception fidelity and scale, necessitating a shift from authenticating actors to evaluating actions.

AgentVisor is a novel defense framework that uses semantic virtualization, inspired by OS principles, to significantly reduce LLM agent vulnerability to prompt injection while maintaining high utility…

PocketAgents introduces a manifest-driven framework for autonomous defense agents, enabling measurable and attributable LLM-driven security responses by strictly controlling agent actions and telemetr…

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…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

The paper introduces a novel stateful online monitoring system that detects distributed multi-agent cyberattacks by aggregating weak suspiciousness signals across many user accounts, overcoming the bl…

The paper introduces a novel stateful online monitoring system that detects distributed multi-agent cyberattacks by aggregating weak suspiciousness signals across many user accounts, overcoming the bl…

This survey analyzes the unique security threats posed by complex, multi-agent AI systems and proposes Confidential Computing (CC) using Trusted Execution Environments (TEEs) as a hardware-rooted defe…

This paper analyzes darknet traffic to characterize advanced, AI-assisted bot reconnaissance, finding that modern evasion techniques allow most bot traffic to bypass standard IDS thresholds.

This paper systematically maps the expanded attack surface of agentic AI systems, identifying new threat vectors like RAG poisoning and cross-agent manipulation, and proposes a comprehensive security…

The paper proposes the Layered Attack Surface Model (LASM), a structural taxonomy that maps security threats and defenses across the complex, multi-layered architecture of AI agents, revealing signifi…