Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies

Adversaries hackers attempting to infiltrate networks frequently face uncertainty in their operational environments. This research explores the ability to model and detect when they exhibit ambiguity aversion, a cognitive bias reflecting a preference for known versus unknown probabilities. We...

Agentic Artificial Intelligence for Ethical Cybersecurity in Uganda: A Reinforcement Learning Framework for Threat Detection in Resource-Constrained Environments

Uganda's rapid digital transformation, supported by national strategies such as Vision 2040 and the Digital Transformation Roadmap, has expanded reliance on networked services while simultaneously increasing exposure to sophisticated cyber threats. In resource-constrained settings, commonly...

AgenticCyber: A GenAI-Powered Multi-Agent System for Multimodal Threat Detection and Adaptive Response in Cybersecurity

The increasing complexity of cyber threats in distributed environments demands advanced frameworks for real-time detection and response across multimodal data streams. This paper introduces AgenticCyber, a generative AI powered multi-agent system that orchestrates specialized agents to monitor...

The Road of Adaptive AI for Precision in Cybersecurity

Cybersecurity's evolving complexity presents unique challenges and opportunities for AI research and practice. This paper shares key lessons and insights from designing, building, and operating production-grade GenAI pipelines in cybersecurity, with a focus on the continual adaptation required to...

ReFuzz: Reusing Tests for Processor Fuzzing with Contextual Bandits

Processor designs rely on iterative modifications and reuse well-established designs. However, this reuse of prior designs also leads to similar vulnerabilities across multiple processors. As processors grow increasingly complex with iterative modifications, efficiently detecting vulnerabilities...

CVE-2025-54515

The Secure Flag passed to Versal™ Adaptive SoC’s Trusted Firmware for Cortex®-A processors TF-A for Arm’s Power State Coordination Interface PSCI commands were incorrectly set to secure instead of using the processor’s actual security state. This would allow the PSCI requests to appear they were...

HarmonicAttack: An Adaptive Cross-Domain Audio Watermark Removal

The availability of high-quality, AI-generated audio raises security challenges such as misinformation campaigns and voice-cloning fraud. A key defense against the misuse of AI-generated audio is by watermarking it, so that it can be easily distinguished from genuine audio. As those seeking to...

EUVD-2025-198581

The Secure Flag passed to Versal™ Adaptive SoC’s Arm® Trusted Firmware for Cortex®-A processors TF-A for Arm’s Power State Coordination Interface PSCI commands were incorrectly set to secure instead of using the processor’s actual security state. This would allow the PSCI requests to appear they...

CVE-2025-54515

The Secure Flag passed to Versal™ Adaptive SoC’s Trusted Firmware for Cortex®-A processors TF-A for Arm’s Power State Coordination Interface PSCI commands were incorrectly set to secure instead of using the processor’s actual security state. This would allow the PSCI requests to appear they were...

CVE-2025-54515

The Secure Flag passed to Versal™ Adaptive SoC’s Trusted Firmware for Cortex®-A processors TF-A for Arm’s Power State Coordination Interface PSCI commands were incorrectly set to secure instead of using the processor’s actual security state. This would allow the PSCI requests to appear they were...

CVE-2025-54515

The CVE describes a mis-set Secure Flag in the Versal Adaptive SoC’s ARM TF-A PSCI handling, where PSCI commands were marked secure instead of reflecting the processor’s actual security state. Affected: Versal Adaptive SoC with Cortex-A TF-A, enabling PSCI requests to appear from the secure state...

AMD Versal Adaptive SoC 安全漏洞

AMD Versal Adaptive SoC is a chip from UltraMicro Semiconductor AMD. A security vulnerability exists in the AMD Versal Adaptive SoC that stems from an improperly set security flag on the PSCI command, which could result in requests from a non-secure state being mistakenly recognized as coming fro...

Systematically Deconstructing APVD Steganography and Its Payload with a Unified Deep Learning Paradigm

In the era of digital communication, steganography allows covert embedding of data within media files. Adaptive Pixel Value Differencing APVD is a steganographic method valued for its high embedding capacity and invisibility, posing challenges for traditional steganalysis. This paper proposes a...

Trustworthy GenAI over 6G: Integrated Applications and Security Frameworks

The integration of generative artificial intelligence GenAI into 6G networks promises substantial performance gains while simultaneously exposing novel security vulnerabilities rooted in multimodal data processing and autonomous reasoning. This article presents a unified perspective on cross-doma...

Agents built into your workflow: Get Security Copilot with Microsoft 365 E5

The cybersecurity landscape is at a historic inflection point. As cyberattackers wield AI to automate cyberattacks at extraordinary speed and scale, the challenge before us is not just to keep pace—but to leap ahead. There are over four million unfilled cybersecurity jobs, so depending solely on...

ForensicFlow: A Tri-Modal Adaptive Network for Robust Deepfake Detection

Deepfakes generated by advanced GANs and autoencoders severely threaten information integrity and societal stability. Single-stream CNNs fail to capture multi-scale forgery artifacts across spatial, texture, and frequency domains, limiting robustness and generalization. We introduce the...

Collaborative research by Microsoft and NVIDIA on real-time immunity

AI-Powered Threats Demand AI-Powered Defense While AI supports growth and innovation, it is also reshaping how organizations address faster, more adaptive security risks. AI-driven security threats, including “vibe-hacking”, are evolving faster than traditional defenses can adapt. Attackers can n...

MalRAG: A Retrieval-Augmented LLM Framework for Open-Set Malicious Traffic Identification

Fine-grained identification of IDS-flagged suspicious traffic is crucial in cybersecurity. In practice, cyber threats evolve continuously, making the discovery of novel malicious traffic a critical necessity as well as the identification of known classes. Recent studies have advanced this goal wi...

Efficient Adversarial Malware Defense Via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reduction

The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and computational efficiency. While recent advances in adversarial defenses have demonstrated strong robustness improvements, they often introduce computationa...

Adaptive Dual-Layer Web Application Firewall (ADL-WAF) Leveraging Machine Learning for Enhanced Anomaly and Threat Detection

Web Application Firewalls are crucial for protecting web applications against a wide range of cyber threats. Traditional Web Application Firewalls often struggle to effectively distinguish between malicious and legitimate traffic, leading to limited efficacy in threat detection. To overcome these...