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,...

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...

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...

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...

编号撤回

BentoML is an open source modeling service library from BentoML Open Source. For building high-performance and scalable AI applications using Python. This CVE number has been withdrawn...

编号撤回

BentoML is an open source modeling service library from BentoML Open Source. For building high-performance and scalable AI applications using Python. This CVE number has been withdrawn...

The vulnerability of Cobalt Ashlar-Vellum’s software-based parametric automated design and 3D modeling capabilities lies in its ability to exploit memory after release, allowing an attacker to execute arbitrary code.

The vulnerability of Cobalt Ashlar-Vellum’s parametric automated design and 3D modeling software lies in its ability to exploit memory after it is freed. Exploiting this vulnerability allows an attacker to execute arbitrary code within the context of the current process...

The vulnerability of the Cobalt Ashlar-Vellum software for parametric automated design and 3D modeling lies in its ability to read data beyond the acceptable range of memory. This allows a malicious actor to execute arbitrary code.

The vulnerability of the Cobalt Ashlar-Vellum software for parametric automated design and 3D modeling lies in the ability to read data beyond the acceptable range in memory. Exploiting this vulnerability could allow an attacker to execute arbitrary code within the context of the current process...

The vulnerability of Cobalt Ashlar-Vellum’s software for parametric automated design and 3D modeling lies in its integer overflow vulnerabilities, allowing an attacker to execute arbitrary code.

The vulnerability of Cobalt Ashlar-Vellum software for parametric automated design and 3D modeling is related to a numerical overflow condition. Exploiting this vulnerability could allow an attacker to execute arbitrary code within the context of the current process...

The vulnerability of Cobalt Ashlar-Vellum software for parametric automated design and 3D modeling lies in its integer overflow vulnerabilities, allowing an attacker to execute arbitrary code.

The vulnerability of the Cobalt Ashlar-Vellum software for parametric automated design and 3D modeling is related to a numerical overflow condition. Exploiting this vulnerability could allow an attacker to execute arbitrary code within the context of the current process...