Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond

The quantity and quality of vulnerability datasets are essential for developing deep learning solutions to vulnerability-related tasks. Due to the limited availability of vulnerabilities, a common approach to building such datasets is analyzing security patches in source code. However, existing...

ABC-FHE : a Resource-Efficient Accelerator Enabling Bootstrappable Parameters for Client-Side Fully Homomorphic Encryption

As the demand for privacy-preserving computation continues to grow, fully homomorphic encryption FHE-which enables continuous computation on encrypted data-has become a critical solution. However, its adoption is hindered by significant computational overhead, requiring 10000-fold more computatio...

On the Ethics of Using LLMs for Offensive Security

Large Language Models LLMs have rapidly evolved over the past few years and are currently evaluated for their efficacy within the domain of offensive cyber-security. While initial forays showcase the potential of LLMs to enhance security research, they also raise critical ethical concerns regardi...

Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test

As API access becomes a primary interface to large language models LLMs, users often interact with black-box systems that offer little transparency into the deployed model. To reduce costs or maliciously alter model behaviors, API providers may discreetly serve quantized or fine-tuned variants,...

GPS Spoofing Attacks on AI-Based Navigation Systems with Obstacle Avoidance in UAV

Recently, approaches using Deep Reinforcement Learning DRL have been proposed to solve UAV navigation systems in complex and unknown environments. However, despite extensive research and attention, systematic studies on various security aspects have not yet been conducted. Therefore, in this pape...

Apple iMessage Zero-Click Key Theft / Remote Code Execution

This is a strategic public disclosure of a zero-click iMessage exploit chain that was discovered live on iOS 18.2 and remained unpatched through iOS 18.4. It enabled Secure Enclave key theft, wormable remote code execution, and undetectable crypto wallet exfiltration. Despite responsible...

SmartAttack: Air-Gap Attack Via Smartwatches

Air-gapped systems are considered highly secure against data leaks due to their physical isolation from external networks. Despite this protection, ultrasonic communication has been demonstrated as an effective method for exfiltrating data from such systems. While smartphones have been extensivel...

SoK: Machine Unlearning for Large Language Models

Large language model LLM unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of techniques have been proposed, including Gradient Ascent, model editing, and...

Navigating Cookie Consent Violations across the Globe

Online services provide users with cookie banners to accept/reject the cookies placed on their web browsers. Despite the increased adoption of cookie banners, little has been done to ensure that cookie consent is compliant with privacy laws around the globe. Prior studies have found that cookies...

Quantifying Mix Network Privacy Erosion with Generative Models

Modern mix networks improve over Tor and provide stronger privacy guarantees by robustly obfuscating metadata. As long as a message is routed through at least one honest mixnode, the privacy of the users involved is safeguarded. However, the complexity of the mixing mechanisms makes it difficult ...

Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings

Federated learning FL enables collaborative model training among multiple clients without the need to expose raw data. Its ability to safeguard privacy, at the heart of FL, has recently been a hot-button debate topic. To elaborate, several studies have introduced a type of attacks known as gradie...

Lightweight Electronic Signatures and Reliable Access Control Included in Sensor Networks to Prevent Cyber Attacks from Modifying Patient Data

Digital terrorism is a major cause of securing patient/healthcare providers data and information. Sensitive topics that may have an impact on a patient's health or even national security include patient health records and information on healthcare providers. Health databases and data sets have be...

Certified Unlearning for Neural Networks

We address the problem of machine unlearning, where the goal is to remove the influence of specific training data from a model upon request, motivated by privacy concerns and regulatory requirements such as the "right to be forgotten." Unfortunately, existing methods rely on restrictive assumptio...

Safeguarding Multimodal Knowledge Copyright in the RAG-As-A-Service Environment

As Retrieval-Augmented Generation RAG evolves into service-oriented platforms Rag-as-a-Service with shared knowledge bases, protecting the copyright of contributed data becomes essential. Existing watermarking methods in RAG focus solely on textual knowledge, leaving image knowledge unprotected. ...

WGLE:Backdoor-Free and Multi-Bit Black-Box Watermarking for Graph Neural Networks

Graph Neural Networks GNNs are increasingly deployed in graph-related applications, making ownership verification critical to protect their intellectual property against model theft. Fingerprinting and black-box watermarking are two main methods. However, the former relies on determining model...

SAGE: Exploring the Boundaries of Unsafe Concept Domain with Semantic-Augment Erasing

Diffusion models DMs have achieved significant progress in text-to-image generation. However, the inevitable inclusion of sensitive information during pre-training poses safety risks, such as unsafe content generation and copyright infringement. Concept erasing finetunes weights to unlearn...

DAVSP: Safety Alignment for Large Vision-Language Models Via Deep Aligned Visual Safety Prompt

Large Vision-Language Models LVLMs have achieved impressive progress across various applications but remain vulnerable to malicious queries that exploit the visual modality. Existing alignment approaches typically fail to resist malicious queries while preserving utility on benign ones effectivel...

What Is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?

While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy DP offers formal protection, but achieving a favourable utility-privacy trade-off remains challenging. Recent works explore deep learning-based generative models to produce...

Secure Data Access in Cloud Environments Using Quantum Cryptography

Cloud computing has made storing and accessing data easier but keeping it secure is a big challenge nowadays. Traditional methods of ensuring data may not be strong enough in the future when powerful quantum computers become available. To solve this problem, this study uses quantum cryptography t...

A Red Teaming Roadmap Towards System-Level Safety

Large Language Model LLM safeguards, which implement request refusals, have become a widely adopted mitigation strategy against misuse. At the intersection of adversarial machine learning and AI safety, safeguard red teaming has effectively identified critical vulnerabilities in state-of-the-art...

CAPAA: Classifier-Agnostic Projector-Based Adversarial Attack

Projector-based adversarial attack aims to project carefully designed light patterns i.e., adversarial projections onto scenes to deceive deep image classifiers. It has potential applications in privacy protection and the development of more robust classifiers. However, existing approaches...

Gh0stEdit: Exploiting Layer-Based Access Vulnerability within Docker Container Images

Whitepaper called Gh0stEdit: Exploiting Layer-Based Access Vulnerability Within Docker Container Images...

WordPress Spreadsheet Price Changer 2.4.37 Privilege Escalation

WordPress Spreadsheet Price Changer plugin versions 2.4.37 and below suffer from a privilege escalation vulnerability...

Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms

We analyze two open source deep reinforcement learning agents submitted to the CAGE Challenge 2 cyber defense challenge, where each competitor submitted an agent to defend a simulated network against each of several provided rules-based attack agents. We demonstrate that one can gain...

Secure Distributed Learning for CAVs: Defending against Gradient Leakage with Leveled Homomorphic Encryption

Federated Learning FL enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine learning in domains like Connected and Autonomous Vehicles CAVs. However, recent studies have shown that exchanged model...

Securing Unbounded Differential Privacy against Timing Attacks

Recent works have started to theoretically investigate how we can protect differentially private programs against timing attacks, by making the joint distribution the output and the runtime differentially private JOT-DP. However, the existing approaches to JOT-DP have some limitations, particular...

MalGEN: a Generative Agent Framework for Modeling Malicious Software in Cybersecurity

The dual use nature of Large Language Models LLMs presents a growing challenge in cybersecurity. While LLM enhances automation and reasoning for defenders, they also introduce new risks, particularly their potential to be misused for generating evasive, AI crafted malware. Despite this emerging...

How Good LLM-Generated Password Policies Are?

Generative AI technologies, particularly Large Language Models LLMs, are rapidly being adopted across industry, academia, and government sectors, owing to their remarkable capabilities in natural language processing. However, despite their strengths, the inconsistency and unpredictability of LLM...

Exposing Hidden Backdoors in NFT Smart Contracts: a Static Security Analysis of Rug Pull Patterns

The explosive growth of Non-Fungible Tokens NFTs has revolutionized digital ownership by enabling the creation, exchange, and monetization of unique assets on blockchain networks. However, this surge in popularity has also given rise to a disturbing trend: the emergence of rug pulls - fraudulent...

IF-GUIDE: Influence Function-Guided Detoxification of LLMs

We study how training data contributes to the emergence of toxic behaviors in large-language models. Most prior work on reducing model toxicity adopts $reactive$ approaches, such as fine-tuning pre-trained and potentially toxic models to align them with human values. In contrast, we propose a...

"Vcd2df" -- Leveraging Data Science Insights for Hardware Security Research

In this work, we hope to expand the universe of security practitioners of open-source hardware by creating a bridge from hardware design languages HDLs to data science languages like Python and R through novel libraries that convert VCD value change dump files into data frames, the expected input...

LLM Unlearning Should Be Form-Independent

Large Language Model LLM unlearning aims to erase or suppress undesirable knowledge within the model, offering promise for controlling harmful or private information to prevent misuse. However, recent studies highlight its limited efficacy in real-world scenarios, hindering practical adoption. In...

Private Evolution Converges

Private Evolution PE is a promising training-free method for differentially private DP synthetic data generation. While it achieves strong performance in some domains e.g., images and text, its behavior in others e.g., tabular data is less consistent. To date, the only theoretical analysis of the...

TokenBreak: Bypassing Text Classification Models through Token Manipulation

Natural Language Processing NLP models are used for text-related tasks such as classification and generation. To complete these tasks, input data is first tokenized from human-readable text into a format the model can understand, enabling it to make inferences and understand context. Text...

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models

Large Language Models LLMs memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information PII, which should not be stored and, consequently, not leaked. In this paper, we introduce Private Memorization Editing PME, an approach for preventing private...

Understanding the Error Sensitivity of Privacy-Aware Computing

Homomorphic Encryption HE enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit fro...

LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges

The widespread adoption of Large Language Models LLMs has heightened concerns about their security, particularly their vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. While prior research has been conducted on general security capabilities of LLMs,...

Data-Driven Understanding of Security Issue Reporting in GitHub Repositories of Open Source Npm Packages

The npm Node Package Manager ecosystem is the most important package manager for JavaScript development with millions of users. Consequently, a plethora of earlier work investigated how vulnerability reporting, patch propagation, and in general detection as well as resolution of security issues i...

Evaluating Explainable AI for Deep Learning-Based Network Intrusion Detection System Alert Classification

A Network Intrusion Detection System NIDS monitors networks for cyber attacks and other unwanted activities. However, NIDS solutions often generate an overwhelming number of alerts daily, making it challenging for analysts to prioritize high-priority threats. While deep learning models promise to...

Walrus: an Efficient Decentralized Storage Network

Decentralized storage systems face a fundamental trade-off between replication overhead, recovery efficiency, and security guarantees. Current approaches either rely on full replication, incurring substantial storage costs, or employ trivial erasure coding schemes that struggle with efficient...

Are Trees Really Green? A Detection Approach of IoT Malware Attacks

Nowadays, the Internet of Things IoT is widely employed, and its usage is growing exponentially because it facilitates remote monitoring, predictive maintenance, and data-driven decision making, especially in the healthcare and industrial sectors. However, IoT devices remain vulnerable due to the...

A Systematic Literature Review on Continuous Integration and Deployment (CI/CD) for Secure Cloud Computing

As cloud environments become widespread, cybersecurity has emerged as a top priority across areas such as networks, communication, data privacy, response times, and availability. Various sectors, including industries, healthcare, and government, have recently faced cyberattacks targeting their...

Explainable AI for Enhancing IDS against Advanced Persistent Kill Chain

Advanced Persistent Threats APTs represent a sophisticated and persistent cy-bersecurity challenge, characterized by stealthy, multi-phase, and targeted attacks aimed at compromising information systems over an extended period. Develop-ing an effective Intrusion Detection System IDS capable of...

Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest

With the rapid expansion of Internet of Things IoT networks, detecting malicious traffic in real-time has become a critical cybersecurity challenge. This research addresses the detection challenges by presenting a comprehensive empirical analysis of machine learning techniques for malware detecti...

Distortion Search, a Web Search Privacy Heuristic

Search engines have vast technical capabilities to retain Internet search logs for each user and thus present major privacy vulnerabilities to both individuals and organizations in revealing user intent. Additionally, many of the web search privacy enhancing tools available today require that the...

Doxing Via the Lens: Revealing Location-Related Privacy Leakage on Multi-Modal Large Reasoning Models

Recent advances in multi-modal large reasoning models MLRMs have shown significant ability to interpret complex visual content. While these models enable impressive reasoning capabilities, they also introduce novel and underexplored privacy risks. In this paper, we identify a novel category of...

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense

Traditional deep neural networks suffer from several limitations, including catastrophic forgetting. When models are adapted to new datasets, they tend to quickly forget previously learned knowledge. Another significant issue is the lack of robustness to even small perturbations in the input data...

Minoritised Ethnic People'S Security and Privacy Concerns and Responses Towards Essential Online Services

Minoritised ethnic people are marginalised in society, and therefore at a higher risk of adverse online harms, including those arising from the loss of security and privacy of personal data. Despite this, there has been very little research focused on minoritised ethnic people's security and...

Profiling Electric Vehicles Via Early Charging Voltage Patterns

Electric Vehicles EVs are rapidly gaining adoption as a sustainable alternative to fuel-powered vehicles, making secure charging infrastructure essential. Despite traditional authentication protocols, recent results showed that attackers may steal energy through tailored relay attacks. One...

SoK: Data Reconstruction Attacks against Machine Learning Models: Definition, Metrics, and Benchmark

Data reconstruction attacks, which aim to recover the training dataset of a target model with limited access, have gained increasing attention in recent years. However, there is currently no consensus on a formal definition of data reconstruction attacks or appropriate evaluation metrics for...