Offensive Tool Determination Strategy R.I.D.D.L.E. + (C)

Intentional threats are a major risk factor related to vulnerabilities in critical infrastructure assets, and an accurate risk assessment is necessary to analyze threats, assess vulnerabilities, and evaluate potential impacts on assets and systems. This research proposes a methodology that can be...

Adaptive Dual-Layer Web Application Firewall (ADL-WAF) Leveraging Machine Learning for Enhanced Anomaly and Threat Detection

Web Application Firewalls are crucial for protecting web applications against a wide range of cyber threats. Traditional Web Application Firewalls often struggle to effectively distinguish between malicious and legitimate traffic, leading to limited efficacy in threat detection. To overcome these...

An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metrics

The performance of Machine Learning ML and Deep Learning DL-based Intrusion Detection and Prevention Systems IDS/IPS is critically dependent on the relevance and quality of the datasets used for training and evaluation. However, current AI model evaluation practices for developing IDS/IPS focus...

T2I-Based Physical-World Appearance Attack against Traffic Sign Recognition Systems in Autonomous Driving

Traffic Sign Recognition TSR systems play a critical role in Autonomous Driving AD systems, enabling real-time detection of road signs, such as STOP and speed limit signs. While these systems are increasingly integrated into commercial vehicles, recent research has exposed their vulnerability to...

ProxyPrints: From Database Breach to Spoof, a Plug-And-Play Defense for Biometric Systems

Fingerprint recognition systems are widely deployed for authentication and forensic applications, but the security of stored fingerprint data remains a critical vulnerability. While many systems avoid storing raw fingerprint images in favor of minutiae-based templates, recent research shows that...

Whose Narrative Is It Anyway? A KV Cache Manipulation Attack

The Key ValueKV cache is an important component for efficient inference in autoregressive Large Language Models LLMs, but its role as a representation of the model's internal state makes it a potential target for integrity attacks. This paper introduces "History Swapping," a novel block-level...

Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments

Large language models LLMs increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adversarially. This study examines whether larger models can systematically jailbreak smaller ones - eliciting harmful or...

Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detection

Fuzzing is a widely used technique for detecting vulnerabilities in smart contracts, which generates transaction sequences to explore the execution paths of smart contracts. However, existing fuzzers are falling short in detecting sophisticated vulnerabilities that require specific attack...

RulePilot: An LLM-Powered Agent for Security Rule Generation

The real-time demand for system security leads to the detection rules becoming an integral part of the intrusion detection life-cycle. Rule-based detection often identifies malicious logs based on the predefined grammar logic, requiring experts with deep domain knowledge for rule generation...

Exploring AI in Steganography and Steganalysis: Trends, Clusters, and Sustainable Development Potential

Steganography and steganalysis are strongly related subjects of information security. Over the past decade, many powerful and efficient artificial intelligence AI - driven techniques have been designed and presented during research into steganography as well as steganalysis. This study presents a...

GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs

Text-attributed graphs TAGs, which combine structural and textual node information, are ubiquitous across many domains. Recent work integrates Large Language Models LLMs with Graph Neural Networks GNNs to jointly model semantics and structure, resulting in more general and expressive models that...

BackWeak: Backdooring Knowledge Distillation Simply with Weak Triggers and Fine-Tuning

Knowledge Distillation KD is essential for compressing large models, yet relying on pre-trained "teacher" models downloaded from third-party repositories introduces serious security risks -- most notably backdoor attacks. Existing KD backdoor methods are typically complex and computationally...

Software Supply Chain Security of Web3

Web3 applications, built on blockchain technology, manage billions of dollars in digital assets through decentralized applications dApps and smart contracts. These systems rely on complex, software supply chains that introduce significant security vulnerabilities. This paper examines the software...

Human-Centered Threat Modeling in Practice: Lessons, Challenges, and Paths Forward

Human-centered threat modeling HCTM is an emerging area within security and privacy research that focuses on how people define and navigate threats in various social, cultural, and technological contexts. While researchers increasingly approach threat modeling from a human-centered perspective,...

Explainable Transformer-Based Email Phishing Classification with Adversarial Robustness

Phishing and related cyber threats are becoming more varied and technologically advanced. Among these, email-based phishing remains the most dominant and persistent threat. These attacks exploit human vulnerabilities to disseminate malware or gain unauthorized access to sensitive information. Dee...

SoK: Security Evaluation of Wi-Fi CSI Biometrics: Attacks, Metrics, and Systemic Weaknesses

Wi-Fi Channel State Information CSI has been repeatedly proposed as a biometric modality, often with reports of high accuracy and operational feasibility. However, the field lacks a consolidated understanding of its security properties, adversarial resilience, and methodological consistency. This...

Prompt Engineering Vs. Fine-Tuning for LLM-Based Vulnerability Detection in Solana and Algorand Smart Contracts

Smart contracts have emerged as key components within decentralized environments, enabling the automation of transactions through self-executing programs. While these innovations offer significant advantages, they also present potential drawbacks if the smart contract code is not carefully design...

VULPO: Context-Aware Vulnerability Detection Via On-Policy LLM Optimization

The widespread reliance on open-source software dramatically increases the risk of vulnerability exploitation, underscoring the need for effective and scalable vulnerability detection VD. Existing VD techniques, whether traditional machine learning-based or LLM-based approaches like prompt...

Grid-STIX: A STIX 2.1-Compliant Cyber-Physical Security Ontology for Power Grid

Modern electrical power grids represent complex cyber-physical systems requiring specialized cybersecurity frameworks beyond traditional IT security models. Existing threat intelligence standards such as STIX 2.1 and MITRE ATT&CK lack coverage for grid-specific assets, operational technology...

Data Poisoning Vulnerabilities across Healthcare AI Architectures: A Security Threat Analysis

Healthcare AI systems face major vulnerabilities to data poisoning that current defenses and regulations cannot adequately address. We analyzed eight attack scenarios in four categories: architectural attacks on convolutional neural networks, large language models, and reinforcement learning...

Retrofit: Continual Learning with Bounded Forgetting for Security Applications

Modern security analytics are increasingly powered by deep learning models, but their performance often degrades as threat landscapes evolve and data representations shift. While continual learning CL offers a promising paradigm to maintain model effectiveness, many approaches rely on full...

Finding Software Supply Chain Attack Paths with Logical Attack Graphs

Cyberattacks are becoming increasingly frequent and sophisticated, often exploiting the software supply chain SSC as an attack vector. Attack graphs provide a detailed representation of the sequence of events and vulnerabilities that could lead to a successful security breach in a system. MulVal ...

Incentive Attacks in BTC: Short-Term Revenue Changes and Long-Term Efficiencies

Bitcoin's BTC Difficulty Adjustment Algorithm DAA has been a source of vulnerability for incentive attacks such as selfish mining, block withholding and coin hopping strategies. In this paper, first, we rigorously study the short-term revenue change per hashpower of the adversarial and honest...

Automated Side-Channel Analysis of Cryptographic Protocol Implementations

We extract the first formal model of WhatsApp from its implementation by combining binary-level analysis via CryptoBap with reverse engineering via Ghidra to handle this large closed-source application. Using this model, we prove forward secrecy, identify a known clone-attack against...

Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learning

The routing protocol for low-power and lossy networks RPL has become the de facto routing standard for resource-constrained IoT systems, but its lightweight design exposes critical vulnerabilities to a wide range of routing-layer attacks such as hello flood, decreased rank, and version number...

PATCHEVAL: A New Benchmark for Evaluating LLMs on Patching Real-World Vulnerabilities

Software vulnerabilities are increasing at an alarming rate. However, manual patching is both time-consuming and resource-intensive, while existing automated vulnerability repair AVR techniques remain limited in effectiveness. Recent advances in large language models LLMs have opened a new paradi...

NegBLEURT Forest: Leveraging Inconsistencies for Detecting Jailbreak Attacks

Jailbreak attacks designed to bypass safety mechanisms pose a serious threat by prompting LLMs to generate harmful or inappropriate content, despite alignment with ethical guidelines. Crafting universal filtering rules remains difficult due to their inherent dependence on specific contexts. To...

The Jasmin Compiler Preserves Cryptographic Security

Jasmin is a programming and verification framework for developing efficient, formally verified, cryptographic implementations. A main component of the framework is the Jasmin compiler, which empowers programmers to write efficient implementations of state-of-the-art cryptographic primitives,...

OpenSCAP Libraries 1.3.13

The openscap project is a set of open source libraries that support the SCAP Security Content Automation Protocol set of standards from NIST. It supports CPE, CCE, CVE, CVSS, OVAL, and XCCDF...

GraphFaaS: Serverless GNN Inference for Burst-Resilient, Real-Time Intrusion Detection

Provenance-based intrusion detection is an increasingly popular application of graphical machine learning in cybersecurity, where system activities are modeled as provenance graphs to capture causality and correlations among potentially malicious actions. Graph Neural Networks GNNs have...

AFLGopher: Accelerating Directed Fuzzing Via Feasibility-Aware Guidance

Directed fuzzing is a useful testing technique that aims to efficiently reach target code sites in a program. The core of directed fuzzing is the guiding mechanism that directs the fuzzing to the specified target. A general guiding mechanism adopted in existing directed fuzzers is to calculate th...

Wazuh 4.14.1

Wazuh is a free and open source security platform that unifies XDR and SIEM capabilities. It protects workloads across on-premises, virtualized, containerized, and cloud-based environments. This is the source code release...

Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection

Fairness is a core element in the trustworthy deployment of deepfake detection models, especially in the field of digital identity security. Biases in detection models toward different demographic groups, such as gender and race, may lead to systemic misjudgments, exacerbating the digital divide...

MTAttack: Multi-Target Backdoor Attacks against Large Vision-Language Models

Recent advances in Large Visual Language Models LVLMs have demonstrated impressive performance across various vision-language tasks by leveraging large-scale image-text pretraining and instruction tuning. However, the security vulnerabilities of LVLMs have become increasingly concerning,...

How Worrying Are Privacy Attacks against Machine Learning?

In several jurisdictions, the regulatory framework on the release and sharing of personal data is being extended to machine learning ML. The implicit assumption is that disclosing a trained ML model entails a privacy risk for any personal data used in training comparable to directly releasing tho...

Can AI Models Be Jailbroken to Phish Elderly Victims? an End-To-End Evaluation

We present an end-to-end demonstration of how attackers can exploit AI safety failures to harm vulnerable populations: from jailbreaking LLMs to generate phishing content, to deploying those messages against real targets, to successfully compromising elderly victims. We systematically evaluated...

Enhanced Anonymous Credentials for E-Voting Systems

A simple and practical method for achieving everlasting privacy in e-voting systems, without relying on advanced cryptographic techniques, is to use anonymous voter credentials. The simplicity of this approach may, however, create some challenges, when combined with other security features, such ...

An In-Depth Systematic Analysis of the Security, Usability, and Automation Capabilities of Password Update Processes on Top-Ranked Websites

Password updates are a critical account security measure and an essential part of the password lifecycle. Service providers and common security recommendations advise users to update their passwords in response to incidents or as a critical cyber hygiene measure. However, password update processe...

Phantom Menace: Exploring and Enhancing the Robustness of VLA Models against Physical Sensor Attacks

Vision-Language-Action VLA models revolutionize robotic systems by enabling end-to-end perception-to-action pipelines that integrate multiple sensory modalities, such as visual signals processed by cameras and auditory signals captured by microphones. This multi-modality integration allows VLA...

Robustness of LLM-Enabled Vehicle Trajectory Prediction under Data Security Threats

The integration of large language models LLMs into automated driving systems has opened new possibilities for reasoning and decision-making by transforming complex driving contexts into language-understandable representations. Recent studies demonstrate that fine-tuned LLMs can accurately predict...

Framing the Hacker: Media Representations and Public Discourse in Germany

This paper examines how the figure of the hacker is portrayed in German mainstream media and explores the impact of media framing on public discourse. Through a longitudinal content analysis of 301 articles from four of the most widely circulated German newspapers Die Zeit, S�ddeutsche Zeitung,...

One Signature, Multiple Payments: Demystifying and Detecting Signature Replay Vulnerabilities in Smart Contracts

Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability...

How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-Based Phishing Detection Models

Large language models LLMs have emerged as a promising phishing detection mechanism, addressing the limitations of traditional deep learning-based detectors, including poor generalization to previously unseen websites and a lack of interpretability. However, LLMs' effectiveness for phishing...

Slice-Aware Spoofing Detection in 5G Networks Using Lightweight Machine Learning

The increasing virtualization of fifth generation 5G networks expands the attack surface of the user plane, making spoofing a persistent threat to slice integrity and service reliability. This study presents a slice-aware lightweight machine-learning framework for detecting spoofing attacks withi...

Enhancing Password Security through a High-Accuracy Scoring Framework Using Random Forests

Password security plays a crucial role in cybersecurity, yet traditional password strength meters, which rely on static rules like character-type requirements, often fail. Such methods are easily bypassed by common password patterns e.g., 'P@ssw0rd1!', giving users a false sense of security. To...

SecTracer: A Framework for Uncovering the Root Causes of Network Intrusions Via Security Provenance

Modern enterprise networks comprise diverse and heterogeneous systems that support a wide range of services, making it challenging for administrators to track and analyze sophisticated attacks such as advanced persistent threats APTs, which often exploit multiple vectors. To address this challeng...

Taught by the Flawed: How Dataset Insecurity Breeds Vulnerable AI Code

AI programming assistants have demonstrated a tendency to generate code containing basic security vulnerabilities. While developers are ultimately responsible for validating and reviewing such outputs, improving the inherent quality of these generated code snippets remains essential. A key...

Toward an Intrusion Detection System for a Virtualization Framework in Edge Computing

Edge computing pushes computation closer to data sources, but it also expands the attack surface on resource-constrained devices. This work explores the deployment of the Lightweight Deep Anomaly Detection for Network Traffic LDPI integrated as an isolated service within a virtualization framewor...

Pack-A-Mal: A Malware Analysis Framework for Open-Source Packages

The increasingly sophisticated environment in which attackers operate makes software security an even greater challenge in open-source projects, where malicious packages are prevalent. Static analysis tools, such as Malcontent, are highly useful but are often incapable of dealing with obfuscated...

TOR Virtual Network Tunneling Tool 0.4.8.20

Tor is a network of virtual tunnels that allows people and groups to improve their privacy and security on the Internet. It also enables software developers to create new communication tools with built-in privacy features. It provides the foundation for a range of applications that allow...