SAMD: A Tool for Identifying False Data Injection Scenarios in AI/ML-Enabled Medical Devices

The growing integration of artificial intelligence AI and machine learning ML in medical systems requires effective measures to address emerging security risks. One such risk is that of adversaries introducing false data through vulnerable system components during inference, causing misdiagnosis...

Do You Dare to Try Test-Driven Forensics? Increasing Trust in Desktop Forensics with ADARE

Digital forensic relies on validated tools and established procedures, yet the underlying operating systems, applications, and analysis tools evolve rapidly. This evolution can cause artifact behavior and tool outputs to drift, silently degrading repeatability and confidence in long-lived forensi...

portofolio_DWForSec

DwF — Cybersecurity Portfolio A professional cybersecurity po...

Mythos and the Unverified Cage: Z3-Based Pre-Deployment Verification for Frontier-Model Sandbox Infrastructure

The April 2026 Claude Mythos sandbox escape exposed a critical weakness in frontier AI containment: the infrastructure surrounding advanced models remains susceptible to formally characterizable arithmetic vulnerabilities. Anthropic has not publicly characterized the escape vector; some secondary...

Wa3r-OffSec-Kit-

Waer's Cybersecurity Knowledge Base 50+ documents · 2...

The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey

AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. However, this unprecedented flexibility introduces complex security challenges fundamentally different from those in...

Exploring the Integration of Differential Privacy in Cybersecurity Analytics: Balancing Data Utility and Privacy in Threat Intelligence

To resolve the acute problem of privacy protection and guarantee that data can be used in the context of threat intelligence, this paper considers the implementation of Differential Privacy DP in cybersecurity analytics. DP, which is a sound mathematical framework, ensures privacy by adding a...

Assessing the Software Security Comprehension of Large Language Models

Large language models LLMs are increasingly used in software development, but their level of software security expertise remains unclear. This work systematically evaluates the security comprehension of five leading LLMs: GPT-4o-Mini, GPT-5-Mini, Gemini-2.5-Flash, Llama-3.1, and Qwen-2.5, using...

Chasing Shadows: Pitfalls in LLM Security Research

Large language models LLMs are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of reproducibility, rigor, and evaluation. Prior work has identified common pitfalls in traditional machine learning researc...

Systems Security Foundations for Agentic Computing

This paper articulates short- and long-term research problems in AI agent security and privacy, using the lens of computer systems security. This approach examines end-to-end security properties of entire systems, rather than AI models in isolation. While we recognize that hardening a single mode...

Secure Control of Connected and Autonomous Electrified Vehicles under Adversarial Cyber-Attacks

Connected and Autonomous Electrified Vehicles CAEV is the solution to the future smart mobility having benefits of efficient traffic flow and cleaner environmental impact. Although CAEV has advantages they are still susceptible to adversarial cyber attacks due to their autonomous electric operati...

ULTIMATE-CYBERSECURITY-MASTER-GUIDE

🛡️ ULTIMATE CYBERSECURITY MASTER GUIDE COLLECTION 📊 Comple...

NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models

In deployment and application, large language models LLMs typically undergo safety alignment to prevent illegal and unethical outputs. However, the continuous advancement of jailbreak attack techniques, designed to bypass safety mechanisms with adversarial prompts, has placed increasing pressure ...

Implementing Zero Trust Architecture to Enhance Security and Resilience in the Pharmaceutical Supply Chain

The pharmaceutical supply chain faces escalating cybersecurity challenges threatening patient safety and operational continuity. This paper examines the transformative potential of zero trust architecture for enhancing security and resilience within this critical ecosystem. We explore the...

Quantifying the ROI of Cyber Threat Intelligence: a Data-Driven Approach

The valuation of Cyber Threat Intelligence CTI remains a persistent challenge due to the problem of negative evidence: successful threat prevention results in non-events that generate minimal observable financial impact, making CTI expenditures difficult to justify within traditional cost-benefit...

Adversarial Attacks to Image Classification Systems Using Evolutionary Algorithms

Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models based on artificial intelligence. This article explores an approach to generate adversarial attacks against image...

Design Patterns for Securing LLM Agents against Prompt Injections

As AI agents powered by Large Language Models LLMs become increasingly versatile and capable of addressing a broad spectrum of tasks, ensuring their security has become a critical challenge. Among the most pressing threats are prompt injection attacks, which exploit the agent's resilience on...

ATAG: AI-Agent Application Threat Assessment with Attack Graphs

Evaluating the security of multi-agent systems MASs powered by large language models LLMs is challenging, primarily because of the systems' complex internal dynamics and the evolving nature of LLM vulnerabilities. Traditional attack graph AG methods often lack the specific capabilities to model...

Server-Side Template Injection Vulnerabilities and Exploitation Techniques

Research article called Server-Side Template Injection SSTI Vulnerabilities and Exploitation Techniques. The paper provides a structured methodology for detecting and exploiting SSTI vulnerabilities across multiple template engines, along with real-world case studies and mitigation strategies...

On the Security Risks of ML-Based Malware Detection Systems: a Survey

Malware presents a persistent threat to user privacy and data integrity. To combat this, machine learning-based ML-based malware detection MD systems have been developed. However, these systems have increasingly been attacked in recent years, undermining their effectiveness in practice. While the...