MacOS Sandbox Escape via Double Free in coreaudiod/CoreAudio Framework

MacOS suffers from a sandbox escape vulnerability due to a double-free condition in coreaudiod/CoreAudio Framework...

Counterfactual Influence As a Distributional Quantity

Machine learning models are known to memorize samples from their training data, raising concerns around privacy and generalization. Counterfactual self-influence is a popular metric to study memorization, quantifying how the model's prediction for a sample changes depending on the sample's...

On the Impact of Sybil-Based Attacks on Mobile Crowdsensing for Transportation

Mobile Crowd-Sensing MCS enables users with personal mobile devices PMDs to gain information on their surroundings. Users collect and contribute data on different phenomena using their PMD sensors, and the MCS system processes this data to extract valuable information for end users. Navigation...

AppleAVD AV1_Syntax::Parse_Header Out-Of-Bounds Read

There is an issue in AppleAVD kernel extension with decoding AV1 video files that could potentially be used to read out-of bound data or potentially cause a kernel crash when rendering a malformed video file. The issue was observed on macOS Sonoma 14.5...

Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-To-Peer Learning

The growing adoption of Artificial Intelligence AI in Internet of Things IoT ecosystems has intensified the need for personalized learning methods that can operate efficiently and privately across heterogeneous, resource-constrained devices. However, enabling effective personalized learning in...

msm_npu Race Condition / Memory Corruption

msmnpu has a race condition between npuhostunloadnetwork and npuhostexecnetworkv2 that leads to memory corruption...

Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning

Recent work has shown that gradient updates in federated learning FL can unintentionally reveal sensitive information about a client's local data. This risk becomes significantly greater when a malicious server manipulates the global model to provoke information-rich updates from clients. In this...

Rational Miner Behaviour, Protocol Stability, and Time Preference: an Austrian and Game-Theoretic Analysis of Bitcoin'S Incentive Environment

This paper integrates Austrian capital theory with repeated game theory to examine strategic miner behaviour under different institutional conditions in blockchain systems. It shows that when protocol rules are mutable, effective time preference rises, undermining rational long-term planning and...

Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA

Memorization in large language models LLMs makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explored its impact in fine-tuning, particularly for LoRA fine-tuning, a widely adopted parameter-efficient method. In this...

Perry: a High-Level Framework for Accelerating Cyber Deception Experimentation

Cyber deception aims to distract, delay, and detect network attackers with fake assets such as honeypots, decoy credentials, or decoy files. However, today, it is difficult for operators to experiment, explore, and evaluate deception approaches. Existing tools and platforms have non-portable and...

Poster: Enhancing GNN Robustness for Network Intrusion Detection Via Agent-Based Analysis

Graph Neural Networks GNNs show great promise for Network Intrusion Detection Systems NIDS, particularly in IoT environments, but suffer performance degradation due to distribution drift and lack robustness against realistic adversarial attacks. Current robustness evaluations often rely on...

Empowering Digital Agriculture: a Privacy-Preserving Framework for Data Sharing and Collaborative Research

Data-driven agriculture, which integrates technology and data into agricultural practices, has the potential to improve crop yield, disease resilience, and long-term soil health. However, privacy concerns, such as adverse pricing, discrimination, and resource manipulation, deter farmers from...

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...

AppleAVD AV1_Syntax::f Out-Of-Bounds Read

There is an issue in AppleAVD kernel extension with decoding AV1 video files that could potentially be used to read out-of bound data or potentially cause a kernel crash when rendering a malformed video file. The issue was observed on macOS Sonoma 14.5...

Samsung S24 MP3 Decoder Out-Of-Bounds Read

There is an out-of-bounds read in the MP3 decoder in the Samsung S24. The function smp123djointstereov1 indexes into several tables for decoding, and does not check that the index is valid, allowing the tables to be read out of bounds. It may be possible to use this bug to bypass ASLR, as loading...

Communication-Efficient Publication of Sparse Vectors under Differential Privacy

Whitepaper called Communication-Efficient Publication Of Sparse Vectors Under Differential Privacy...

Measuring Modern Phishing Tactics: a Quantitative Study of Body Obfuscation Prevalence, Co-Occurrence, and Filter Impact

Phishing attacks frequently use email body obfuscation to bypass detection filters, but quantitative insights into how techniques are combined and their impact on filter scores remain limited. This paper addresses this gap by empirically investigating the prevalence, co-occurrence patterns, and...

XNU VM_BEHAVIOR_ZERO_WIRED_PAGES Read-Only Write

XNU VMBEHAVIORZEROWIREDPAGES suffers from a flaw that allows writing to read-only pages...

Living Long Doing Pentests

Whitepaper called Living Long Doing Pentests. It discusses basic LLDP protocol fuzzing and usage from a pentester's point of view...

CodeGuard: a Generalized and Stealthy Backdoor Watermarking for Generative Code Models

Generative code models GCMs significantly enhance development efficiency through automated code generation and code summarization. However, building and training these models require computational resources and time, necessitating effective digital copyright protection to prevent unauthorized lea...

SIMulator: SIM Tracing on a (Pico-)Budget

SIM tracing -- the ability to inspect, modify, and relay communication between a SIM card and modem -- has become a significant technique in cellular network research. It enables essential security- and development-related applications such as fuzzing communication interfaces, extracting session...

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...

E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification Via MLLMs

The rapid spread of misinformation in mobile and wireless networks presents critical security challenges. This study introduces a training-free, retrieval-based multimodal fact verification system that leverages pretrained vision-language models and large language models for credibility assessmen...

JsDeObsBench: Measuring and Benchmarking LLMs for JavaScript Deobfuscation

Deobfuscating JavaScript JS code poses a significant challenge in web security, particularly as obfuscation techniques are frequently used to conceal malicious activities within scripts. While Large Language Models LLMs have recently shown promise in automating the deobfuscation process,...

ZKPROV: a Zero-Knowledge Approach to Dataset Provenance for Large Language Models

As the deployment of large language models LLMs grows in sensitive domains, ensuring the integrity of their computational provenance becomes a critical challenge, particularly in regulated sectors such as healthcare, where strict requirements are applied in dataset usage. We introduce ZKPROV, a...

Diffusion-Based Task-Oriented Semantic Communications with Model Inversion Attack

Semantic communication has emerged as a promising neural network-based system design for 6G networks. Task-oriented semantic communication is a novel paradigm whose core goal is to efficiently complete specific tasks by transmitting semantic information, optimizing communication efficiency and ta...

Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing

We propose Guardian-FC, a novel two-layer framework for privacy preserving federated computing that unifies safety enforcement across diverse privacy preserving mechanisms, including cryptographic back-ends like fully homomorphic encryption FHE and multiparty computation MPC, as well as statistic...

Quest KACE Systems Management Appliance 14.1 Authentication Bypass

Seralys Security Advisory - Quest KACE SMA contains an authentication bypass vulnerability that allows attackers to impersonate legitimate users without valid credentials. The vulnerability exists in the SSO authentication handling mechanism and can lead to complete administrative takeover. Versi...

The Impact of the Russia-Ukraine Conflict on the Cloud Computing Risk Landscape

The Russian invasion of Ukraine has fundamentally altered the information technology IT risk landscape, particularly in cloud computing environments. This paper examines how this geopolitical conflict has accelerated data sovereignty concerns, transformed cybersecurity paradigms, and reshaped clo...

Verifiable Unlearning on Edge

Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infringements, biases, or regulatory requirements may require the verifiable removal of certain data samples across all edg...

Quest KACE Systems Management Appliance 14.1 Unauthenticated Backup Upload

Seralys Security Advisory - Quest KACE SMA allows unauthenticated users to upload backup files to the system. While signature validation is implemented, weaknesses in the validation process can be exploited to upload malicious backup content that could compromise system integrity. Version 14.1 is...

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation

Advances in generative models have transformed the field of synthetic image generation for privacy-preserving data synthesis PPDS. However, the field lacks a comprehensive survey and comparison of synthetic image generation methods across diverse settings. In particular, when we generate syntheti...

Machine Learning with Privacy for Protected Attributes

Differential privacy DP has become the standard for private data analysis. Certain machine learning applications only require privacy protection for specific protected attributes. Using naive variants of differential privacy in such use cases can result in unnecessary degradation of utility. In...

Assessing Risk of Stealing Proprietary Models for Medical Imaging Tasks

The success of deep learning in medical imaging applications has led several companies to deploy proprietary models in diagnostic workflows, offering monetized services. Even though model weights are hidden to protect the intellectual property of the service provider, these models are exposed to...

Out-of-Bounds Write Vulnerability in BACnet MS/TP Kernel Module

A critical buffer overflow vulnerability in the mstp.ko kernel module, used in ABB’s Cylon ASPECT/FLXeon BACnet MS/TP controllers for building management systems BMS, allows out-of-bounds writes in the SendFrame function due to inadequate bounds checking of BACnet MS/TP frames. This flaw,...

Anti-Phishing Training Does Not Work: a Large-Scale Empirical Assessment of Multi-Modal Training Grounded in the NIST Phish Scale

Social engineering attacks using email, commonly known as phishing, are a critical cybersecurity threat. Phishing attacks often lead to operational incidents and data breaches. As a result, many organizations allocate a substantial portion of their cybersecurity budgets to phishing awareness...

Yealink RPS Information Disclosure / Man-In-The-Middle

Yealink RPS contains several vulnerabilities that can lead to leaking of PII and/or man-in-the-middle attacks. Some vulnerabilities remain unpatched even after disclosure to the manufacturer...

WebGuard++: Interpretable Malicious URL Detection Via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT

URL+HTML feature fusion shows promise for robust malicious URL detection, since attacker artifacts persist in DOM structures. However, prior work suffers from four critical shortcomings: 1 incomplete URL modeling, failing to jointly capture lexical patterns and semantic context; 2 HTML graph...

PrivacyXray: Detecting Privacy Breaches in LLMs through Semantic Consistency and Probability Certainty

Large Language Models LLMs are widely used in sensitive domains, including healthcare, finance, and legal services, raising concerns about potential private information leaks during inference. Privacy extraction attacks, such as jailbreaking, expose vulnerabilities in LLMs by crafting inputs that...

Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks

Website Fingerprinting WF attacks aim to infer which websites a user is visiting by analyzing traffic patterns, thereby compromising user anonymity. Although this technique has been demonstrated to be effective in controlled experimental environments, it remains largely limited to small-scale...

An ETSI GS QKD Compliant TLS Implementation

A modification of the TLS protocol is presented, using our implementation of the Quantum Key Distribution QKD standard ETSI GS QKD 014 v1.1.1. We rely on the Rustls library for this. The TLS protocol is modified while maintaining backward compatibility on the client and server side. We thus wish ...

Quantum-Resistant Domain Name System: a Comprehensive System-Level Study

The Domain Name System DNS plays a foundational role in Internet infrastructure, yet its core protocols remain vulnerable to compromise by quantum adversaries. As cryptographically relevant quantum computers become a realistic threat, ensuring DNS confidentiality, authenticity, and integrity in t...

Retrieval-Confused Generation Is a Good Defender for Privacy Violation Attack of Large Language Models

Recent advances in large language models LLMs have made a profound impact on our society and also raised new security concerns. Particularly, due to the remarkable inference ability of LLMs, the privacy violation attack PVA, revealed by Staab et al., introduces serious personal privacy issues...

Quest KACE Systems Management Appliance 14.1 2FA Bypass

Seralys Security Advisory - Quest KACE SMA contains a logic flaw in its two-factor authentication implementation that allows authenticated users to bypass TOTP-based 2FA requirements. The vulnerability exists in the 2FA validation process and can be exploited to gain elevated access. Version 14.1...

A Hybrid Intrusion Detection System with a New Approach to Protect the Cybersecurity of Cloud Computing

Cybersecurity is one of the foremost challenges facing the world of cloud computing. Recently, the widespread adoption of smart devices in cloud computing environments that provide Internet-based services has become prevalent. Therefore, it is essential to consider the security threats in these...

FuncVul: an Effective Function Level Vulnerability Detection Model Using LLM and Code Chunk

Software supply chain vulnerabilities arise when attackers exploit weaknesses by injecting vulnerable code into widely used packages or libraries within software repositories. While most existing approaches focus on identifying vulnerable packages or libraries, they often overlook the specific...

A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures

In recent years, Large-Language-Model-driven AI agents have exhibited unprecedented intelligence, flexibility, and adaptability, and are rapidly changing human production and lifestyle. Nowadays, agents are undergoing a new round of evolution. They no longer act as an isolated island like LLMs...

KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs

Despite extensive research on Machine Learning-based Network Intrusion Detection Systems ML-NIDS, their capability to detect diverse attack variants remains uncertain. Prior studies have largely relied on homogeneous datasets, which artificially inflate performance scores and offer a false sense ...

Recalling the Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy

Machine Unlearning MU technology facilitates the removal of the influence of specific data instances from trained models on request. Despite rapid advancements in MU technology, its vulnerabilities are still under explored, posing potential risks of privacy breaches through leaks of ostensibly...

PhishingHook: Catching Phishing Ethereum Smart Contracts Leveraging EVM Opcodes

The Ethereum Virtual Machine EVM is a decentralized computing engine. It enables the Ethereum blockchain to execute smart contracts and decentralized applications dApps. The increasing adoption of Ethereum sparked the rise of phishing activities. Phishing attacks often target users through...