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...

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...

Detecting and Visualizing Lateral Movement Attacks with Trellix XDR

Detecting and Visualizing Lateral Movement Attacks with Trellix Helix Connect By Maulik Maheta and Adithya Chandra · July 17, 2025 Executive summary This blog marks the third installment in our series on detecting and visualizing lateral movement attacks with Trellix Helix Connect. A lateral...

An Adversarial Quantum Key Distribution Project

Quantum key distribution QKD is a popular introduction to quantum technologies used in education and public outreach, as very little background in quantum theory is needed and the practical applications are easily understood. There is considerably less exposure to the many real-world consideratio...

HASSLE: a Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated Learning

Vertical Federated Learning VFL enables an orchestrating active party to perform a machine learning task by cooperating with passive parties that provide additional task-related features for the same training data entities. While prior research has leveraged the privacy vulnerability of VFL to...

PRM-Free Security Alignment of Large Models Via Red Teaming and Adversarial Training

Large Language Models LLMs have demonstrated remarkable capabilities across diverse applications, yet they pose significant security risks that threaten their safe deployment in critical domains. Current security alignment methodologies predominantly rely on Process Reward Models PRMs to evaluate...

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...

AdvGrasp: Adversarial Attacks on Robotic Grasping from a Physical Perspective

Adversarial attacks on robotic grasping provide valuable insights into evaluating and improving the robustness of these systems. Unlike studies that focus solely on neural network predictions while overlooking the physical principles of grasping, this paper introduces AdvGrasp, a framework for...

Autonomous AI-Based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigation

Critical infrastructure systems, including energy grids, healthcare facilities, transportation networks, and water distribution systems, are pivotal to societal stability and economic resilience. However, the increasing interconnectivity of these systems exposes them to various cyber threats,...

RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation

Retrieval-Augmented Generation RAG enhances large language models LLMs by retrieving external data to mitigate hallucinations and outdated knowledge issues. Benefiting from the strong ability in facilitating diverse data sources and supporting faithful reasoning, knowledge graphs KGs have been...

CAVGAN: Unifying Jailbreak and Defense of LLMs Via Generative Adversarial Attacks on Their Internal Representations

Security alignment enables the Large Language Model LLM to gain the protection against malicious queries, but various jailbreak attack methods reveal the vulnerability of this security mechanism. Previous studies have isolated LLM jailbreak attacks and defenses. We analyze the security protection...

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective

The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns. Dataset auditing techniques, which aim to...

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-Of-Distribution Trap Is All You Need

Data-free knowledge distillation DFKD transfers knowledge from a teacher to a student without access the real in-distribution ID data. Its common solution is to use a generator to synthesize fake data and use them as a substitute for real ID data. However, existing works typically assume teachers...

False Alarms, Real Damage: Adversarial Attacks Using LLM-Based Models on Text-Based Cyber Threat Intelligence Systems

Cyber Threat Intelligence CTI has emerged as a vital complementary approach that operates in the early phases of the cyber threat lifecycle. CTI involves collecting, processing, and analyzing threat data to provide a more accurate and rapid understanding of cyber threats. Due to the large volume ...

LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing

The proliferation of Low-Rank Adaptation LoRA models has democratized personalized text-to-image generation, enabling users to share lightweight models e.g., personal portraits on platforms like Civitai and Liblib. However, this "share-and-play" ecosystem introduces critical risks: benign LoRAs c...

DEBIAN-CVE-2025-6297

It was discovered that dpkg-deb does not properly sanitize directory permissions when extracting a control member into a temporary directory, which is documented as being a safe operation even on untrusted data. This may result in leaving temporary files behind on cleanup. Given automated and...

AZL-64641 CVE-2025-6297 affecting package dpkg 1.20.10-1

It was discovered that dpkg-deb does not properly sanitize directory permissions when extracting a control member into a temporary directory, which is documented as being a safe operation even on untrusted data. This may result in leaving temporary files behind on cleanup. Given automated and...

Boosting Generative Adversarial Transferability with Self-Supervised Vision Transformer Features

The ability of deep neural networks DNNs come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on hard labels, we craft adversarial perturbation that generalize more effectively, boosting black-box transferability...

On the Feasibility of Poisoning Text-To-Image AI Models Via Adversarial Mislabeling

Today's text-to-image generative models are trained on millions of images sourced from the Internet, each paired with a detailed caption produced by Vision-Language Models VLMs. This part of the training pipeline is critical for supplying the models with large volumes of high-quality image-captio...

PhishKey: a Novel Centroid-Based Approach for Enhanced Phishing Detection Using Adaptive HTML Component Extraction

Phishing attacks pose a significant cybersecurity threat, evolving rapidly to bypass detection mechanisms and exploit human vulnerabilities. This paper introduces PhishKey to address the challenges of adaptability, robustness, and efficiency. PhishKey is a novel phishing detection method using...