VulCPE: Context-Aware Cybersecurity Vulnerability Retrieval and Management

The dynamic landscape of cybersecurity demands precise and scalable solutions for vulnerability management in heterogeneous systems, where configuration-specific vulnerabilities are often misidentified due to inconsistent data in databases like the National Vulnerability Database NVD. Inaccurate...

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...

Securing WiFi Fingerprint-Based Indoor Localization Systems from Malicious Access Points

WiFi fingerprint-based indoor localization schemes deliver highly accurate location data by matching the received signal strength indicator RSSI with an offline database using machine learning ML or deep learning DL models. However, over time, RSSI values degrade due to the malicious behavior of...

Optimizing Mouse Dynamics for User Authentication by Machine Learning: Addressing Data Sufficiency, Accuracy-Practicality Trade-Off, and Model Performance Challenges

User authentication is essential to ensure secure access to computer systems, yet traditional methods face limitations in usability, cost, and security. Mouse dynamics authentication, based on the analysis of users' natural interaction behaviors with mouse devices, offers a cost-effective,...

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...

Privacy-Preserving Transformers: SwiftKey'S Differential Privacy Implementation

In this paper we train a transformer using differential privacy DP for language modeling in SwiftKey. We run multiple experiments to balance the trade-off between the model size, run-time speed and accuracy. We show that we get small and consistent gains in the next-word-prediction and accuracy...

LLMs' Suitability for Network Security: a Case Study of STRIDE Threat Modeling

Artificial Intelligence AI is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are almost nonexistent studies that analyze the suitability of Large Language...

Risk Assessment and Threat Modeling for Safe Autonomous Driving Technology

This research paper delves into the field of autonomous vehicle technology, examining the vulnerabilities inherent in each component of these transformative vehicles. Autonomous vehicles AVs are revolutionizing transportation by seamlessly integrating advanced functionalities such as sensing,...

Securing the Future of IVR: AI-Driven Innovation with Agile Security, Data Regulation, and Ethical AI Integration

The rapid digitalization of communication systems has elevated Interactive Voice Response IVR technologies to become critical interfaces for customer engagement. With Artificial Intelligence AI now driving these platforms, ensuring secure, compliant, and ethically designed development practices i...

Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning IRL. Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attributio...

Enhancing the Cloud Security through Topic Modelling

Protecting cloud applications is crucial in an age where security constantly threatens the digital world. The inevitable cyber-attacks throughout the CI/CD pipeline make cloud security innovations necessary. This research is motivated by applying Natural Language Processing NLP methodologies, suc...

14 secure coding tips: Learn from the experts at Microsoft Build

Hey friends! If you are a developer, you know that writing clean and efficient code is just the starting point. Now, with AI playing a bigger role, secure coding isn't just a 'nice-to-have'—it's a must. Whether you're building web apps, working on cloud services, or adding AI to your projects,...

14 secure coding tips: Learn from the experts at Microsoft Build

Hey friends! If you are a developer, you know that writing clean and efficient code is just the starting point. Now, with AI playing a bigger role, secure coding isn't just a 'nice-to-have'—it's a must. Whether you're building web apps, working on cloud services, or adding AI to your projects,...

CISA: Roadmap to Innovation in the Dams Sector

The Roadmap to Innovation in the Dams Sector outlines Research and Development Focus Areas for the next 3-5 years to enhance the security and resilience of the sector and ensure that dams and related infrastructure can withstand current and emerging risks. The R+D Focus Areas identified in this...

ThreMoLIA: Threat Modeling of Large Language Model-Integrated Applications

Large Language Models LLMs are currently being integrated into industrial software applications to help users perform more complex tasks in less time. However, these LLM-Integrated Applications LIA expand the attack surface and introduce new kinds of threats. Threat modeling is commonly used to...

Cluster-Aware Attacks on Graph Watermarks

Data from domains such as social networks, healthcare, finance, and cybersecurity can be represented as graph-structured information. Given the sensitive nature of this data and their frequent distribution among collaborators, ensuring secure and attributable sharing is essential. Graph...

DoomArena: a Framework for Testing AI Agents against Evolving Security Threats

We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1 It is a plug-in framework and integrates easily into realistic agentic frameworks like BrowserGym for web agents and $τ$-bench for tool calling agents; 2 It is configurable and allows...

InjectLab: a Tactical Framework for Adversarial Threat Modeling against Large Language Models

Large Language Models LLMs are changing the way people interact with technology. Tools like ChatGPT and Claude AI are now common in business, research, and everyday life. But with that growth comes new risks, especially prompt-based attacks that exploit how these models process language. InjectLa...

Vulnerability of software for modeling, designing, and drawing in AutoCAD, related to errors during initialization of variables, allowing attackers to trigger a service failure and gain unauthorized access to protected information.

The vulnerability of software for modeling, designing, and drawing in AutoCAD is related to errors during initialization of variables. Exploiting this vulnerability can allow attackers to cause service failures and gain unauthorized access to protected information using a specially created...

Vulnerability of software for modeling, designing, and drawing in AutoCAD, related to buffer overflow in dynamic memory, allowing attackers to cause system failures.

The vulnerability of software for modeling, designing, and drawing in AutoCAD is related to buffer overflow in dynamic memory. Exploiting this vulnerability can allow an attacker to cause a service failure using a specially created MODEL file...