Analyzing Concentration, Temporal Routines and Targeting in Public Ransomware Leak Site Data

Ransomware has grown to become one of the most damaging types of cybercrime, affecting private and public organizations in any sector. While early types of ransomware targeted many victims via automated attacks, ransomware groups have started to specifically target organizations and companies in...

CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering

Large language models LLMs are increasingly applied to cybersecurity question answering QA for critical tasks such as incident response and vulnerability analysis. However, real-world operational contexts, including system logs and network configurations, inherently contain sensitive identifiers,...

AI-Driven Adaptive Adversaries and the Erosion of Cryptographic Trust in Public Key Systems

This paper examines the erosion of Public Key Cryptography PKC security under adaptive adversarial optimisation driven by artificial intelligence. The problem addressed is the growing mismatch between algorithm-centric cryptographic security models and operational attack realities, where...

Routing Cybersecurity Awareness Training by FFM Personality Trait: A Quasi-Experimental Evaluation

Cybersecurity awareness training has historically adopted a one-size-fits-all approach, despite established individual differences in how users process and retain security information. Personality has been proposed as one axis along which training content might be tailored; yet no prior study has...

From Frontier to Shadow AI: A Simmering Threat to Assurance and Security in Critical Infrastructure

Frontier AI systems, including large language models and emerging agentic AI tools, offer significant operational benefits but present unique challenges to critical infrastructure CI environments due to their non-deterministic and emergent properties. While formal adoption is inherently cautious...

Demystifying the Mythos or Disrupting Bugonomics? from Zero-Day Asymmetry to Defender Remediation Throughput

Recent demonstrations of large language models producing candidate and confirmed vulnerabilities in production software have renewed the narrative that AI will reshape offensive and defensive security. Headlines emphasize capability; they rarely interrogate costs and incentives. This paper examin...

Reasoning As an Attack Surface: Adaptive Evolutionary CoT Jailbreaks for LLMs

Large Reasoning Models LRMs have demonstrated remarkable capabilities in reasoning and generation tasks and are increasingly deployed in real-world applications. However, their explicit chain-of-thought CoT mechanism introduces new security risks, making them particularly vulnerable to jailbreak...

CyBOKClaw: Human-In-The-Loop CyBOK Mapping for Cybersecurity Curriculum

This paper presents CyBOKClaw, an interpretable human-in-the-loop retrieval framework for mapping cybersecurity keywords or phrases KWoPs to the Cyber Security Body of Knowledge CyBOK. Rather than treating the task as strict exact classification, the framework is designed as a top-k candidate...

CALIBURN: A Regime-Sensitivity Study of Operationally Calibrated Streaming Intrusion Detection

Streaming network intrusion detection systems must process flows continuously while keeping memory bounded, but most current methods leave alerting threshold selection as a post-hoc tuning problem poorly suited to production. Operators need alerting behaviour specifiable before deployment using...

AI Security Research Should Better Incentivize Defense Research

This work examines an imbalance in artificial intelligence AI security research: the field tends to produce more work on attacking AI systems than on defending them. Drawing on related academic papers, we find biased attack-to-defense ratios across subfields, including federated learning, speech...

Attested Tool-Server Admission: A Security Extension to the Model Context Protocol

The Model Context Protocol MCP standardizes how a large-language-model LLM agent and an external tool server exchange messages, but not trust: a host reads a server's self-declared tool list and dispatches calls, with no notion of which servers it may use, at what sensitivity, or which of a...

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,...

Cybersecurity of Electric Vehicle Charging Infrastructure: Recent Advances, Open Challenges, and Future Directions

Electric Vehicles EVs have emerged as significant disruptors in the transportation sector over the past decade. Their growing popularity and adoption are accompanied by capital expenditures to deploy charging infrastructure. EV charging infrastructure sits at the intersection of the power grid, t...

Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence

Many existing Artificial Intelligence AI solutions on mobile devices rely on an extensive collection of sensitive data, raising privacy concerns and often requiring storage for both context and model improvement. Apple's Private Cloud Compute PCC aims to address this by emphasizing mobile device...

Modernizing User Privacy Preference Measurement through GPPI: A GDPR-Aligned Privacy Preference Item Bank

Privacy measurement instruments e.g., CFIP, IUIPC, PAQ predate GDPR by over a decade and measure privacy concerns, distinct from preferences for regulatory protections e.g., data portability, erasure, automated decision-making rights. This leaves practitioners without tools to assess whether user...

Formal Verification of Probing Security Via Conditional Independence

Side-channel attacks are a major threat to the security of cryptosystems. Masking is a widely used countermeasure against such attacks, but proving the security of masked algorithms is error-prone without formal verification. In this work, we propose a novel approach to formal verification of...

CodeQL 2.25.5

Discover vulnerabilities across a codebase with CodeQL, an industry-leading semantic code analysis engine. CodeQL lets you query code as though it were data. Write a query to find all variants of a vulnerability, eradicating it forever. Then share your query to help others do the same...

An Empirical Evaluation of LLM-Generated Code Security across Prompting Methods

The growing use of Large Language Models LLMs for automated code generation has enhanced software development efficiency, but often at the cost of security. Generated code frequently overlooks critical concerns, leaving it vulnerable to issues such as weak encryption and improper input validation...

Security, Privacy, and Ethical Risks in OpenClaw

This paper systematically investigates the security, privacy, and ethical risks, as well as the traceability challenges of OpenClaw, a locally executable AI agent system for natural language interaction and real-world task completion. While OpenClaw shows strong potential for personal assistance,...

angr 9.2.217

angr is an open-source binary analysis platform for Python. It combines both static and dynamic symbolic "concolic" analysis, providing tools to solve a variety of tasks...

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...

Joern 4.0.546

Joern is the bug hunter's workbench. With this tool, you can uncover attack surface, sloppy coding practices, and variants of known vulnerabilities using an interactive code analysis shell. Joern supports C, C++, LLVM bitcode, x86 binaries via Ghidra, JVM bytecode via Soot, and Javascript...

Adversarial Vulnerability under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection

We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The dataset is organized into yearly slices and evaluated under three...

Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks

We evaluate whether frontier LLMs are ready for cybersecurity through a dual-mode benchmark: white-box function-level vulnerability detection VulnLLM-R, across C/Java/Python and black-box web application security testing five production-style applications with 118 ground-truth vulnerabilities...

Beyond Zero: Enterprise Security for the AI Era

The rise of autonomous AI agents and the accelerating velocity of corporate data access are stretching the application-centric model of zero trust security to its breaking point. This paper introduces Beyond Zero, a new security paradigm designed for the AI era. The Beyond Zero architecture...

Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation Using Machine Learning Algorithms

The advent of cardless artificial intelligence AI banking heralds a paradigm shift in the financial landscape, offering users unprecedented security and convenience. This paper outlines a comprehensive framework designed to enhance cybersecurity, introduce auto-generated virtual cards, and mitiga...

UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection

The detection of previously unseen network attacks remains a major challenge for intrusion detection systems. Although supervised learning methods often perform well on known attack classes, they are limited when new attack types are not represented in the training data. Unsupervised methods are...

Market-Analysis-Driven Methodology for Assessing Charging Station Cybersecurity

Modern charging communication standards for electric vehicles include optional security controls such as TLS-based authentication and encryption. However, with tens of thousands of fast charging points deployed in any given country, individually testing each one for security control support is...

Parser-Free Querying of Security Logs

Security analysts routinely query system logs to detect threats and investigate incidents, but each log source uses its own semi-structured format: logs are cheap to produce, but expensive to use. The standard approach, building per-source parsers to normalize logs into structured schemas, is...

UFONet 2.0

UFONet abuses OSI Layer 7-HTTP to create/manage 'zombies' and to conduct different attacks using GET/POST, multi-threading, proxies, origin spoofing methods, cache evasion techniques, etc...

Security of LLM-Generated Code: A Comparative Analysis

The majority of software developers use or are planning to use Artificial Intelligence AI tools in their development processes. Their top reasons include improving productivity and faster learning. In fact, Large Language Model LLM-generated code is currently in production, including in major tec...

From Preventive to Reactive: How AI Coding Assistants Transform Developers' Security Awareness

AI coding assistants are now central to professional software development, yet their impact on how developers think about and practice security remains poorly understood. While prior work has documented vulnerability rates in AI-generated code, a more fundamental question persists: how do these...

Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems

Injection detectors deployed to protect LLM agents are calibrated on static, template-based payloads that announce themselves as override directives. We identify a systematic blind spot: when payloads are generated to mimic the domain vocabulary and authority structures of the target document, wh...

Prompt Overflow: What the Guardrail Inspects Is Not What the Model Infers

Guardrail models a.k.a. safety checkers are widely deployed to screen user inputs before they reach large language models LLMs, serving as a primary defense against prompt injection attacks. Due to strict context constraints, these models handle overlength prompts through truncation or...

A First Measurement Study on Authentication Security in Real-World Remote MCP Servers

The Model Context Protocol MCP is emerging as a common interface connecting large language models LLMs with external services. Remote deployments are becoming increasingly important as agents connect to user-linked online services, such as social, productivity, and financial services. In such...

BYOT-CPS: A Hybrid Cyber-Physical Systems Testbed for IoT Security Assessment and Platform Evaluation

Internet of Things IoT security research continues to face a methodological gap between scalable virtual experimentation and realistic device behaviour. While pure simulation and emulation platforms provide control, repeatability, and scale, they do not fully reproduce firmware-specific behaviour...

Human Vulnerability Assessment in Cybersecurity: A Systematic Literature Review of Methods, Models, and Instruments

In cybersecurity, vulnerability assessment has typically focused on identifying and measuring vulnerabilities within digital assets and technical infrastructures. However, there is growing recognition that this approach alone is inadequate without a structured examination of the human factor, whi...

Measuring Security without Fooling Ourselves: Why Benchmarking Agents Is Hard

The benchmarks used to evaluate AI agents in security-critical roles suffer from crucial weaknesses. Building on recent empirical evidence, we characterize three core challenges that undermine security evaluations: benchmark vulnerabilities, temporal staleness, and runtime uncertainty. We then...

Botnet Detection on CTU-13 Using Lightweight Machine Learning Models

Botnets are among the most persistent cyber threats, enabling large-scale attacks such as spam, credential theft, and distributed denial-of-service DDoS. While deep learning approaches have recently been applied to botnet detection, they are computationally intensive and often lack...

Practical Countermeasure against Attacks Exploiting Detection Efficiency Mismatch in Quantum Key Distribution

We demonstrate a practical countermeasure against a well-known class of attacks on quantum key distribution QKD systems that exploit detection efficiency mismatch, where the receiver's detectors do not exhibit identical responses to incoming photons across all degrees of freedom. This class of...

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets

This paper investigates a unexplored yet impactful vulnerability in AI explainability used in intrusion detection IDS: multicollinearity-induced instability. Despite extensive reliance on post-hoc explainability tools such as SHAP or LIME, the impact of correlated features on explanation robustne...

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications

Large Language Models LLMs have become the predominant paradigm in NLP, advancing both research and industry. As model sizes and pretraining data grow, concerns about Pretraining Data Exposure PDE increase due to the scale and opacity of training datasets. PDE refers to determining whether specif...

TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis

Electromagnetic EM side-channel analysis traditionally assumes a stationary, close-proximity probe - a threat model that underestimates aerial adversaries. TriSweep is a simulation framework that designs and evaluates a four-drone swarm architecture for autonomous standoff EM-SCA of embedded...

Encrypted Neural Networks without Overflows

Fully homomorphic encryption FHE enables private inference by evaluating neural networks on encrypted data. In this way, we can delegate the computation to a third party server without ever revealing the user's data. Currently, the CKKS scheme is the backbone of most efficient FHE implementations...

FreeBSD Security Advisory - FreeBSD-SA-26:19.file

FreeBSD Security Advisory - A file descriptor can be closed while a thread is blocked in a poll2 or select2 call waiting for that descriptor. Because the blocked thread does not hold a reference to the underlying object, this closure may result in the object being freed while the thread remains...

Backchaining Loss of Control Mitigations from Mission-Specific Benchmarks in National Security

Affordances and permissions are promising and timely safety levers for mitigating Loss of Control LoC threats in high-stakes deployment contexts, such as national security. Deployers in defense and intelligence could rely on several approaches to identify which affordances and permissions should ...

IronCurtain 0.11.0

IronCurtain is an early-stage research project exploring how to make AI agents safe enough to be genuinely useful. It is a runtime for autonomous AI agents, where security policy is derived from a human-readable constitution. APIs, configuration formats, and architecture may change...

Quality and Security Signals in AI-Generated Python Refactoring Pull Requests

As AI agents increasingly contribute to code development and maintenance, there is still limited empirical evidence on the quality and risk characteristics of their changes in real-world projects, particularly for refactoring-oriented contributions. It remains unclear how agent-authored refactori...

Wireshark Analyzer 4.6.6

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers. Thi...

Profiling User Vulnerability to Phishing through Psychological and Behavioral Factors

Phishing remains one of the most pervasive cybersecurity threats, shifting the focus from technological vulnerabilities to human cognitive and psychological factors. In coherence with the trend of studies on phishing to increasingly focus on human aspects and vulnerable users profiling, this stud...