AZL-65889 CVE-2025-48964 affecting package iputils for versions less than 20240117-2

ping in iputils before 20250602 allows a denial of service application error in adaptive ping mode or incorrect data collection via a crafted ICMP Echo Reply packet, because a zero timestamp can lead to large intermediate values that have an integer overflow when squared during statistics...

Frame-Level Temporal Difference Learning for Partial Deepfake Speech Detection

Detecting partial deepfake speech is essential due to its potential for subtle misinformation. However, existing methods depend on costly frame-level annotations during training, limiting real-world scalability. Also, they focus on detecting transition artifacts between bonafide and deepfake...

Non-Adaptive Adversarial Face Generation

Adversarial attacks on face recognition systems FRSs pose serious security and privacy threats, especially when these systems are used for identity verification. In this paper, we propose a novel method for generating adversarial faces-synthetic facial images that are visually distinct yet...

Thought Purity: Defense Paradigm for Chain-Of-Thought Attack

While reinforcement learning-trained Large Reasoning Models LRMs, e.g., Deepseek-R1 demonstrate advanced reasoning capabilities in the evolving Large Language Models LLMs domain, their susceptibility to security threats remains a critical vulnerability. This weakness is particularly evident in...

LRCTI: a Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification

Verifying the credibility of Cyber Threat Intelligence CTI is essential for reliable cybersecurity defense. However, traditional approaches typically treat this task as a static classification problem, relying on handcrafted features or isolated deep learning models. These methods often lack the...

BandFuzz: an ML-Powered Collaborative Fuzzing Framework

Collaborative fuzzing has recently emerged as a technique that combines multiple individual fuzzers and dynamically chooses the appropriate combinations suited for different programs. Unlike individual fuzzers, which rely on specific assumptions to maintain their effectiveness, collaborative...

DESIGN: Encrypted GNN Inference Via Server-Side Input Graph Pruning

Graph Neural Networks GNNs have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption FHE, typically incurs substantial computational overhead, rendering real-time and...

ARMOR: Aligning Secure and Safe Large Language Models Via Meticulous Reasoning

Large Language Models LLMs have demonstrated remarkable generative capabilities. However, their susceptibility to misuse has raised significant safety concerns. While post-training safety alignment methods have been widely adopted, LLMs remain vulnerable to malicious instructions that can bypass...

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs

The rapid expansion of Internet of Things IoT networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging...

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats

Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical...

AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing

Federated Learning FL faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environments. Recent approaches either sacrifice formal privacy guarantees, incur high overheads, or overlook quantum-enhanced...

TuneShield: Mitigating Toxicity in Conversational AI While Fine-Tuning on Untrusted Data

Recent advances in foundation models, such as LLMs, have revolutionized conversational AI. Chatbots are increasingly being developed by customizing LLMs on specific conversational datasets. However, mitigating toxicity during this customization, especially when dealing with untrusted training dat...

A Novel APVD Steganography Technique Incorporating Pseudorandom Pixel Selection for Robust Image Security

Steganography is the process of embedding secret information discreetly within a carrier, ensuring secure exchange of confidential data. The Adaptive Pixel Value Differencing APVD steganography method, while effective, encounters certain challenges like the "unused blocks" issue. This problem can...

Adaptive Variation-Resilient Random Number Generator for Embedded Encryption

With a growing interest in securing user data within the internet-of-things IoT, embedded encryption has become of paramount importance, requiring light-weight high-quality Random Number Generators RNGs. Emerging stochastic device technologies produce random numbers from stochastic physical...

Adaptive Malware Detection Using Sequential Feature Selection: a Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification

Traditional malware detection methods exhibit computational inefficiency due to exhaustive feature extraction requirements, creating accuracy-efficiency trade-offs that limit real-time deployment. We formulate malware classification as a Markov Decision Process with episodic feature acquisition a...

The Hidden Weaknesses in AI SOC Tools that No One Talks About

If you're evaluating AI-powered SOC platforms, you've likely seen bold claims: faster triage, smarter remediation, and less noise. But under the hood, not all AI is created equal. Many solutions rely on pre-trained AI models that are hardwired for a handful of specific use cases. While that might...

Beyond Traditional Threats: The Rise of AI-Driven API Vulnerabilities

AI has had dramatic impacts on almost every facet of every industry. API security is no exception. Up until recently, defending APIs meant guarding against well-understood threats. But as AI proliferates, automated adversaries, AI-crafted exploits, and business logic abuse have complicated matter...

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS

Adversarial attacks, wherein slight inputs are carefully crafted to mislead intelligent models, have attracted increasing attention. However, a critical gap persists between theoretical advancements and practical application, particularly in structured data like network traffic, where...

SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning

Federated Learning FL has emerged as a leading paradigm for privacy-preserving distributed machine learning, yet the distributed nature of FL introduces unique security challenges, notably the threat of backdoor attacks. Existing backdoor strategies predominantly rely on end-to-end label...

Generative AI for Vulnerability Detection in 6G Wireless Networks: Advances, Case Study, and Future Directions

The rapid advancement of 6G wireless networks, IoT, and edge computing has significantly expanded the cyberattack surface, necessitating more intelligent and adaptive vulnerability detection mechanisms. Traditional security methods, while foundational, struggle with zero-day exploits, adversarial...