This paper proposes using a zero-determinant (ZD) strategy to construct an effective Moving Target Defense (MTD) that maintains performance comparable to the optimal Stackelberg equilibrium while dras…

This paper proposes using cyber deception with honey drones (HDs) to defend UAV mission systems against Denial-of-Service (DoS) attacks, achieving superior performance using a novel Hypergame-Theoreti…

The paper proposes a system that operationalizes cybersecurity governance frameworks by integrating them with attack-path modeling and Deep Reinforcement Learning to generate practical, resource-const…

The Pen-Strategist framework addresses the limitations of current automated penetration testing by integrating a novel domain-specific reasoning model for robust strategy formation and an accurate cla…

The paper introduces a queueing-theoretic framework to model dynamic cyber-attack surfaces, developing an adaptive reinforcement learning defense policy that significantly reduces active vulnerabiliti…

Defense training for LLM agents, intended to improve safety, systematically degrades their core competence, leading to unreliability in multi-step tasks.

DeepStage is a deep reinforcement learning framework that achieves autonomous, stage-aware defense against multi-stage APT campaigns by fusing graph-based telemetry and predicting attacker stages.

This paper systematically measures and explains how sequential model defenses can conflict, finding that 38.9% of ordered defense sequences cause measurable risk exacerbation due to anti-aligned param…

The paper models the cybersecurity arms race using a contest-theoretic framework, showing that full cross-correlation of threat intelligence can neutralize the attacker's structural advantage from inc…

This paper reviews advanced AI-based solutions, specifically combining LLMs and RL, to create dynamic and cost-effective network and device-level cyber deception strategies for contested environments.

The paper introduces C-MADF, a causally constrained multi-agent framework that significantly reduces false positives in autonomous cyber defense by restricting response actions to structurally consist…

DeepXplain introduces an explainable deep reinforcement learning framework that enhances the trustworthiness and effectiveness of autonomous cyber defense against multi-stage APT campaigns by integrat…

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

The paper evaluates Language Model Agents (LMAs) for red-teaming by benchmarking their ability to perform lateral movement, finding that expert-defined action plans are most effective, though all moda…

AutoRISE proposes optimizing the entire attack strategy—by searching over executable programs—rather than just optimizing prompts, achieving significant improvements in red-teaming large language mode…

PlanGuard is a training-free defense framework that uses an isolated Planner and hierarchical verification to defend LLM agents against Indirect Prompt Injection by verifying the consistency of planne…

The paper demonstrates that current defenses against malicious fine-tuning of foundation models are insufficient because they only address fixed attacks, and introduces a unified adaptive attack that…

The paper proposes an autonomous red teaming framework combining LLMs and RL to generate sophisticated, multi-stage cyber attack campaigns, demonstrating its necessity for evaluating robust AI-enabled…

The paper introduces a challenging benchmark for LLM agents to perform unsupervised threat hunting on raw Windows event logs, finding that current frontier models perform poorly and are not ready for…

This paper synthesizes expert knowledge from a workshop to provide a comprehensive framework and best-practice guidelines for developing high-quality reinforcement learning environments for autonomous…