Security Aspects of ISO 15118 Plug and Charge Payment

For the rise of electric vehicles, especially for long-distance driving, minimizing charging times is vital. While multiple standards for DC fast charging exist, the leading standard in Europe is ISO 15118. In theory, this standard is accompanied by a variety of security controls, ensuring the...

PentestEval: Benchmarking LLM-Based Penetration Testing with Modular and Stage-Level Design

Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly manual, expertise-intensive, and difficult to scale. Although recent advances in Large Language Models LLMs offer promising opportunities for...

FreeBSD Security Advisory - FreeBSD-SA-25:12.rtsold

FreeBSD Security Advisory - The rtsol8 and rtsold8 programs do not validate the domain search list options provided in router advertisement messages; the option body is passed to resolvconf8 unmodified. resolvconf8 is a shell script which does not validate its input. A lack of quoting meant that...

CIS-BA: Continuous Interaction Space Based Backdoor Attack for Object Detection in the Real-World

Object detection models deployed in real-world applications such as autonomous driving face serious threats from backdoor attacks. Despite their practical effectiveness,existing methods are inherently limited in both capability and robustness due to their dependence on single-trigger-single-objec...

LegionITS: A Federated Intrusion-Tolerant System Architecture

The growing sophistication, frequency, and diversity of cyberattacks increasingly exceed the capacity of individual entities to fully understand and counter them. While existing solutions, such as Security Information and Event Management SIEM systems, Security Orchestration, Automation, and...

Trust in LLM-Controlled Robotics: A Survey of Security Threats, Defenses and Challenges

The integration of Large Language Models LLMs into robotics has revolutionized their ability to interpret complex human commands and execute sophisticated tasks. However, such paradigm shift introduces critical security vulnerabilities stemming from the ''embodiment gap'', a discord between the...

HAL -- an Open-Source Framework for Gate-Level Netlist Analysis

HAL is an open-source framework for gate-level netlist analysis, an integral step in hardware reverse engineering. It provides analysts with an interactive GUI, an extensible plugin system, and APIs in both C++ and Python for rapid prototyping and automation. In addition, HAL ships with plugins f...

FreeBSD Security Advisory - FreeBSD-SA-25:11.ipfw

FreeBSD Security Advisory - In some cases, the 'tcp-setmss' handler may free the packet data and throw an error without halting the rule processing engine. A subsequent rule can then allow the traffic after the packet data is gone, resulting in a NULL pointer dereference...

UIXPOSE: Mobile Malware Detection Via Intention-Behaviour Discrepancy Analysis

We introduce UIXPOSE, a source-code-agnostic framework that operates on both compiled and open-source apps. This framework applies Intention Behaviour Alignment IBA to mobile malware analysis, aligning UI-inferred intent with runtime semantics. Previous work either infers intent statically, e.g.,...

Penetration Testing of Agentic AI: A Comparative Security Analysis across Models and Frameworks

Agentic AI introduces security vulnerabilities that traditional LLM safeguards fail to address. Although recent work by Unit 42 at Palo Alto Networks demonstrated that ChatGPT-4o successfully executes attacks as an agent that it refuses in chat mode, there is no comparative analysis in multiple...

ScamSweeper: Detecting Illegal Accounts in Web3 Scams Via Transactions Analysis

The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. However, previous studies have primarily concentrated...

Intrusion Detection in Internet of Vehicles Using Machine Learning

The Internet of Vehicles IoV has evolved modern transportation through enhanced connectivity and intelligent systems. However, this increased connectivity introduces critical vulnerabilities, making vehicles susceptible to cyber-attacks such Denial-ofService DoS and message spoofing. This project...

Cybersecurity Skills in New Graduates: A Philippine Perspective

This study investigates the key skills and competencies needed by new cybersecurity graduates in the Philippines for entry-level positions. Using a descriptive cross-sectional research design, it combines analysis of job listings from Philippine online platforms with surveys of students, teachers...

SeBERTis: A Framework for Producing Classifiers of Security-Related Issue Reports

Monitoring issue tracker submissions is a crucial software maintenance activity. A key goal is the prioritization of high risk, security-related bugs. If such bugs can be recognized early, the risk of propagation to dependent products and endangerment of stakeholder benefits can be mitigated. To...

Cybercrime and Computer Forensics in Epoch of Artificial Intelligence in India

The integration of generative Artificial Intelligence into the digital ecosystem necessitates a critical re-evaluation of Indian criminal jurisprudence regarding computational forensics integrity. While algorithmic efficiency enhances evidence extraction, a research gap exists regarding the Digit...

Nuclei 3.6.1

Nuclei is a modern, high-performance vulnerability scanner that leverages simple YAML-based templates. It empowers you to design custom vulnerability detection scenarios that mimic real-world conditions, leading to zero false positives...

Data Protection and Corporate Reputation Management in the Digital Era

This paper analyzes the relationship between cybersecurity management, data protection, and corporate reputation in the context of digital transformation. The study examines how organizations implement strategies and tools to mitigate cyber risks, comply with regulatory requirements, and maintain...

AIAuditTrack: A Framework for AI Security System

The rapid expansion of AI-driven applications powered by large language models has led to a surge in AI interaction data, raising urgent challenges in security, accountability, and risk traceability. This paper presents AiAuditTrack AAT, a blockchain-based framework for AI usage traffic recording...

APT-ClaritySet: A Large-Scale, High-Fidelity Labeled Dataset for APT Malware with Alias Normalization and Graph-Based Deduplication

Large-scale, standardized datasets for Advanced Persistent Threat APT research are scarce, and inconsistent actor aliases and redundant samples hinder reproducibility. This paper presents APT-ClaritySet and its construction pipeline that normalizes threat actor aliases reconciling approximately...

GRR 4.0.0.0

GRR Rapid Response is an incident response framework focused on remote live forensics. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR consists of 2 parts: client and server. GRR...

Cloud Security Leveraging AI: A Fusion-Based AISOC for Malware and Log Behaviour Detection

Cloud Security Operations Center SOC enable cloud governance, risk and compliance by providing insights visibility and control. Cloud SOC triages high-volume, heterogeneous telemetry from elastic, short-lived resources while staying within tight budgets. In this research, we implement an...

An Empirical Analysis of Zero-Day Vulnerabilities Disclosed by the Zero Day Initiative

Zero-day vulnerabilities represent some of the most critical threats in cybersecurity, as they correspond to previously unknown flaws in software or hardware that are actively exploited before vendors can develop and deploy patches. During this exposure window, affected systems remain defenseless...

Hybrid Ensemble Method for Detecting Cyber-Attacks in Water Distribution Systems Using the BATADAL Dataset

The cybersecurity of Industrial Control Systems that manage critical infrastructure such as Water Distribution Systems has become increasingly important as digital connectivity expands. BATADAL benchmark data is a good source of testing intrusion detection techniques, but it presents several...

Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models

Securing digital text is becoming increasingly relevant due to the widespread use of large language models. Individuals' fear of losing control over data when it is being used to train such machine learning models or when distinguishing model-generated output from text written by humans. Digital...

Quantum Disruption: An SOK of How Post-Quantum Attackers Reshape Blockchain Security and Performance

As quantum computing advances toward practical deployment, it threatens a wide range of classical cryptographic mechanisms, including digital signatures, key exchange protocols, public-key encryption, and certain hash-based constructions that underpin modern network infrastructures. These...

Behavior-Aware and Generalizable Defense against Black-Box Adversarial Attacks for ML-Based IDS

Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs using indirect feedback such as binary outputs or behavioral signals like response time and resource usage. While several defenses have been proposed...

Weak Enforcement and Low Compliance in PCI~DSS: A Comparative Security Study

Although credit and debit card data continue to be a prime target for attackers, organizational adherence to the Payment Card Industry Data Security Standard PCI DSS remains surprisingly low. Despite prior work showing that PCI DSS can reduce card fraud, only 32.4% of organizations were fully...

Zed Attack Proxy 2.17.0 Cross Platform Package

The Zed Attack Proxy ZAP is an easy to use integrated penetration testing tool for finding vulnerabilities in web applications. It is designed to be used by people with a wide range of security experience and as such is ideal for developers and functional testers who are new to penetration testin...

Fortra GoAnywhere MFT 7.x Vulnerability Scanner

Fortra GoAnywhere MFT version7.x vulnerability scanner that looks for systems with a deserialization vulnerability using remote fingerprinting of the system. It does not perform exploitation...

Quantigence: A Multi-Agent AI Framework for Quantum Security Research

Cryptographically Relevant Quantum Computers CRQCs pose a structural threat to the global digital economy. Algorithms like Shor's factoring and Grover's search threaten to dismantle the public-key infrastructure PKI securing sovereign communications and financial transactions. While the timeline...

From Obfuscated to Obvious: A Comprehensive JavaScript Deobfuscation Tool for Security Analysis

JavaScript's widespread adoption has made it an attractive target for malicious attackers who employ sophisticated obfuscation techniques to conceal harmful code. Current deobfuscation tools suffer from critical limitations that severely restrict their practical effectiveness. Existing tools...

Hyperparameter Tuning-Based Optimized Performance Analysis of Machine Learning Algorithms for Network Intrusion Detection

Network Intrusion Detection Systems NIDS are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. Th...

Towards a Systematic Taxonomy of Attacks against Space Infrastructures

Space infrastructures represent an emerging domain that is critical to the global economy and society. However, this domain is vulnerable to attacks. To enhance the resilience of this domain, we must understand the attacks that can be waged against it. The status quo is that there is no systemati...

Cisco Integrated AI Security and Safety Framework Report

Artificial intelligence AI systems are being readily and rapidly adopted, increasingly permeating critical domains: from consumer platforms and enterprise software to networked systems with embedded agents. While this has unlocked potential for human productivity gains, the attack surface has...

Detecting Prompt Injection Attacks against Application Using Classifiers

Prompt injection attacks can compromise the security and stability of critical systems, from infrastructure to large web applications. This work curates and augments a prompt injection dataset based on the HackAPrompt Playground Submissions corpus and trains several classifiers, including LSTM,...

SHERLOCK: A Deep Learning Approach to Detect Software Vulnerabilities

The increasing reliance on software in various applications has made the problem of software vulnerability detection more critical. Software vulnerabilities can lead to security breaches, data theft, and other negative outcomes. Traditional software vulnerability detection techniques, such as...

FiD-QAE: A Fidelity-Driven Quantum Autoencoder for Credit Card Fraud Detection

Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of classical machine learning methods, as well as more recent quantum machine learning approaches, have been investigated ...

CeLLMate: Sandboxing Browser AI Agents

Browser-using agents BUAs are an emerging class of autonomous agents that interact with web browsers in human-like ways, including clicking, scrolling, filling forms, and navigating across pages. While these agents help automate repetitive online tasks, they are vulnerable to prompt injection...

One Leak Away: How Pretrained Model Exposure Amplifies Jailbreak Risks in Finetuned LLMs

Finetuning pretrained large language models LLMs has become the standard paradigm for developing downstream applications. However, its security implications remain unclear, particularly regarding whether finetuned LLMs inherit jailbreak vulnerabilities from their pretrained sources. We investigat...

Detecting Malicious Entra OAuth Apps with LLM-Based Permission Risk Scoring

This project presents a unified detection framework that constructs a complete corpus of Microsoft Graph permissions, generates consistent LLM-based risk scores, and integrates them into a real-time detection engine to identify malicious OAuth consent activity...

Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance

Agentic AI systems are emerging as powerful tools for automating complex, multi-step tasks across various industries. One such industry is telecommunications, where the growing complexity of next-generation radio access networks RANs opens up numerous opportunities for applying these systems...

Taint-Based Code Slicing for LLMs-Based Malicious NPM Package Detection

The increasing sophistication of malware attacks in the npm ecosystem, characterized by obfuscation and complex logic, necessitates advanced detection methods. Recently, researchers have turned their attention from traditional detection approaches to Large Language Models LLMs due to their strong...

The Role of AI in Modern Penetration Testing

Penetration testing is a cornerstone of cybersecurity, traditionally driven by manual, time-intensive processes. As systems grow in complexity, there is a pressing need for more scalable and efficient testing methodologies. This systematic literature review examines how Artificial Intelligence AI...

Diverse LLMs Vs. Vulnerabilities: Who Detects and Fixes Them Better?

Large Language Models LLMs are increasingly being studied for Software Vulnerability Detection SVD and Repair SVR. Individual LLMs have demonstrated code understanding abilities, but they frequently struggle when identifying complex vulnerabilities and generating fixes. This study presents...

Proving DNSSEC Correctness: A Formal Approach to Secure Domain Name Resolution

The Domain Name System Security Extensions DNSSEC are critical for preventing DNS spoofing, yet its specifications contain ambiguities and vulnerabilities that elude traditional "break-and-fix" approaches. A holistic, foundational security analysis of the protocol has thus remained an open proble...

Persistent Backdoor Attacks under Continual Fine-Tuning of LLMs

Backdoor attacks embed malicious behaviors into Large Language Models LLMs, enabling adversaries to trigger harmful outputs or bypass safety controls. However, the persistence of the implanted backdoors under user-driven post-deployment continual fine-tuning has been rarely examined. Most prior...

Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)

Open Radio Access Networks O-RAN enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers-anomaly detection, intrusion...

PHANTOM: Progressive High-Fidelity Adversarial Network for Threat Object Modeling

The scarcity of cyberattack data hinders the development of robust intrusion detection systems. This paper introduces PHANTOM, a novel adversarial variational framework for generating high-fidelity synthetic attack data. Its innovations include progressive training, a dual-path VAE-GAN...

A Systematic Mapping Study on Risks and Vulnerabilities in Software Containers

Software containers are widely adopted for developing and deploying software applications. Despite their popularity, major security concerns arise during container development and deployment. Software Engineering SE research literature reveals a lack of reviewed, aggregated, and organized knowled...

EIP-7702 Phishing Attack

EIP-7702 introduces a delegation-based authorization mechanism that allows an externally owned account EOA to authenticate a single authorization tuple, after which all subsequent calls are routed to arbitrary delegate code. We show that this design enables a qualitatively new class of phishing...