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...

appsec-sentinel

AppSec-Sentinel AI-powered security scanner with cross-file...

Digital Threat Modeling Under Authoritarianism

Today's world requires us to make complex and nuanced decisions about our digital security. Evaluating when to use a secure messaging app like Signal or WhatsApp, which passwords to store on your smartphone, or what to share on social media requires us to assess risks and make judgments...

AegisShield: Democratizing Cyber Threat Modeling with Generative AI

The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with limited resources. This paper develops and evaluates AegisShield, a generative AI enhanced threat modeling tool that implements STRIDE and MITRE ATT&CK to...

Rethinking Denial-Of-Service: a Conditional Taxonomy Unifying Availability and Sustainability Threats

This paper proposes a unified, condition-based framework for classifying both legacy and cloud-era denial-of-service DoS attacks. The framework comprises three interrelated models: a formal conditional tree taxonomy, a hierarchical lattice structure based on order theory, and a conceptual Venn...

Comprehensive MCP Security Checklist: Protecting Your AI-Powered Infrastructure

With innovation comes risk. As organizations race to build AI-first infrastructure, security is struggling to keep pace. Multi-Agentic Systems – those built on Large Language Models LLMs and Multi-Component Protocols MCP - bring immense potential, but also novel vulnerabilities that traditional...

Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System

When combining Large Language Models LLMs with autonomous agents, used in network monitoring and decision-making systems, this will create serious security issues. In this research, the MAESTRO framework consisting of the seven layers threat modeling architecture in the system was used to expose,...

Characterizing Security and Privacy Teaching Standards for Schools in the United States

Increasingly, students begin learning aspects of security and privacy during their primary and secondary education grades K-12 in the United States. Individual U.S. states and some national organizations publish teaching standards -- guidance that outlines expectations for what students should...

Hedge Funds on a Swamp: Analyzing Patterns, Vulnerabilities, and Defense Measures in Blockchain Bridges [Experiment, Analysis and Benchmark]

Blockchain bridges have become essential infrastructure for enabling interoperability across different blockchain networks, with more than $24B monthly bridge transaction volume. However, their growing adoption has been accompanied by a disproportionate rise in security breaches, making them the...

Agent Safety Alignment Via Reinforcement Learning

The emergence of autonomous Large Language Model LLM agents capable of tool usage has introduced new safety risks that go beyond traditional conversational misuse. These agents, empowered to execute external functions, are vulnerable to both user-initiated threats e.g., adversarial prompts and...

SV-LLM: an Agentic Approach for SoC Security Verification Using Large Language Models

Ensuring the security of complex system-on-chips SoCs designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models LLMs, with their...

Agent Capability Negotiation and Binding Protocol (ACNBP)

As multi-agent systems evolve to encompass increasingly diverse and specialized agents, the challenge of enabling effective collaboration between heterogeneous agents has become paramount, with traditional agent communication protocols often assuming homogeneous environments or predefined...

Modeling Interdependent Privacy Threats

The rise of online social networks, user-gene-rated content, and third-party apps made data sharing an inevitable trend, driven by both user behavior and the commercial value of personal information. As service providers amass vast amounts of data, safeguarding individual privacy has become...

ACSE-Eval: Can LLMs Threat Model Real-World Cloud Infrastructure?

While Large Language Models have shown promise in cybersecurity applications, their effectiveness in identifying security threats within cloud deployments remains unexplored. This paper introduces AWS Cloud Security Engineering Eval, a novel dataset for evaluating LLMs cloud security threat...

Ghosted by a cybercriminal

Welcome to this week's edition of the Threat Source newsletter. Talos recently published research into how threat actors are increasingly teaming up across the attack chain. Each group handles a slice of the operation, passing the breach along like a relay baton. It's a concerning trend -- one th...

Redefining IABs: Impacts of compartmentalization on threat tracking and modeling

Cisco Talos has observed a growing trend of attack kill chains being split into two stages -- initial compromise and subsequent exploitation -- executed by separate threat actors. This compartmentalization increases the complexity and difficulty of performing threat modeling and actor profiling...

ThreatLens: LLM-Guided Threat Modeling and Test Plan Generation for Hardware Security Verification

Current hardware security verification processes predominantly rely on manual threat modeling and test plan generation, which are labor-intensive, error-prone, and struggle to scale with increasing design complexity and evolving attack methodologies. To address these challenges, we propose...