Validating Threat Modeling Results with the Help of Vulnerable Test Applications

Validating threat modeling results remains difficult because completeness is hard to judge without an external oracle. Existing studies often rely on expert-produced reference models and other human baselines, but these can contain omissions or disagreements. This paper evaluates a complementary,...

FALCON-C: Flow-Based Analysis and Labeling for Connected Vehicular Network Cybersecurity

Along with the recent rise in popularity of Electric Vehicles EVs, Electric Vehicle Supply Equipment EVSE has emerged as a new target for cyber attacks. Therefore, ensuring the security and integrity of network communication between EVSE components and vehicular clients is a significant challenge...

OpenAI Launches Daybreak for AI-Powered Vulnerability Detection and Patch Validation

OpenAI has launched Daybreak , a new cybersecurity initiative that brings together frontier artificial intelligence AI model capabilities and Codex Security to help organizations identify and patch vulnerabilities before attackers find a way in using the same issues. "Daybreak combines the...

Exploit for Embedded Malicious Code in Tukaani Xz

Security Review: CVE-2024-3094 XZ Utils Backdoor Автор:...

SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems

Threat modeling for cyber-physical systems CPS remains a largely manual exercise. This project presents SMSI System Model Security Inference, a hybrid neuro-symbolic pipeline that starts from a SysML architecture model and produces a prioritized list of NIST 800-53 security controls. The prototyp...

SecScan

SecScan Local-LLM-powered security scanner for GitHub repos...

cruxss-bb-agent

CRUXSS Bug Bounty Agent A semi-autonomous bug bounty hunting...

Detecting and analyzing prompt abuse in AI tools

This second post in our AI Application Security series is all about moving from planning to practice. AI Application Series 1: Security considerations when adopting AI tools established how AI adoption expands the attack surface and our threat-modelling guidance on the Microsoft security blog...

Exploit for Out-of-bounds Write in Netapp Bootstrap_Os

Typeform DevSecOps Pipeline POC !Pythonhttps://img.shields...

Security Considerations for Multi-Agent Systems

Multi-agent artificial intelligence systems or MAS are systems of autonomous agents that exercise delegated tool authority, share persistent memory, and coordinate via inter-agent communication. MAS introduces qualitatively distinct security vulnerabilities from those documented for singular AI...

Threat modeling AI applications

Proactively identifying, assessing, and addressing risk in AI systems We cannot anticipate every misuse or emergent behavior in AI systems. We can , however, identify what can go wrong, assess how bad it could be, and design systems that help reduce the likelihood or impact of those failure modes...

Threat modeling AI applications

Proactively identifying, assessing, and addressing risk in AI systems We cannot anticipate every misuse or emergent behavior in AI systems. We can , however, identify what can go wrong, assess how bad it could be, and design systems that help reduce the likelihood or impact of those failure modes...

How to Organize Safely in the Age of Surveillance

From threat modeling to encrypted collaboration apps, we’ve collected experts’ tips and tools for safely and effectively building a group—even while being targeted and tracked by the powerful...

Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP

The rapid development of the AI agent communication protocols, including the Model Context Protocol MCP, Agent2Agent A2A, Agora, and Agent Network Protocol ANP, is reshaping how AI agents communicate with tools, services, and each other. While these protocols support scalable multi-agent...

A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities across Clinical Specialties

Medical Large Language Models LLMs are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU cluster...

ASTRIDE: A Security Threat Modeling Platform for Agentic-AI Applications

AI agent-based systems are becoming increasingly integral to modern software architectures, enabling autonomous decision-making, dynamic task execution, and multimodal interactions through large language models LLMs. However, these systems introduce novel and evolving security challenges, includi...

Future-Back Threat Modeling: A Foresight-Driven Security Framework

Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks are often disconnected from operational or architectural artifacts. This creates a fundamental weakness: the most serious cyber threats often do not arise...

Human-Centered Threat Modeling in Practice: Lessons, Challenges, and Paths Forward

Human-centered threat modeling HCTM is an emerging area within security and privacy research that focuses on how people define and navigate threats in various social, cultural, and technological contexts. While researchers increasingly approach threat modeling from a human-centered perspective,...

AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI

This paper introduces the Agentic AI Governance Assurance & Trust Engine AAGATE, a Kubernetes-native control plane designed to address the unique security and governance challenges posed by autonomous, language-model-driven agents in production. Recognizing the limitations of traditional...

AgentCyTE: Leveraging Agentic AI to Generate Cybersecurity Training and Experimentation Scenarios

Designing realistic and adaptive networked threat scenarios remains a core challenge in cybersecurity research and training, still requiring substantial manual effort. While large language models LLMs show promise for automated synthesis, unconstrained generation often yields configurations that...