A Survey on Privacy-Preserving Computing in the Automotive Domain

As vehicles become increasingly connected and autonomous, they accumulate and manage various personal data, thereby presenting a key challenge in preserving privacy during data sharing and processing. This survey reviews applications of Secure Multi-Party Computation MPC and Homomorphic Encryptio...

BeDKD: Backdoor Defense Based on Dynamic Knowledge Distillation and Directional Mapping Modulator

Although existing backdoor defenses have gained success in mitigating backdoor attacks, they still face substantial challenges. In particular, most of them rely on large amounts of clean data to weaken the backdoor mapping but generally struggle with residual trigger effects, resulting in...

Semantic Encryption: Secure and Effective Interaction with Cloud-Based Large Language Models Via Semantic Transformation

The increasing adoption of Cloud-based Large Language Models CLLMs has raised significant concerns regarding data privacy during user interactions. While existing approaches primarily focus on encrypting sensitive information, they often overlook the logical structure of user inputs. This oversig...

Complete Evasion, Zero Modification: PDF Attacks on AI Text Detection

AI-generated text detectors have become essential tools for maintaining content authenticity, yet their robustness against evasion attacks remains questionable. We present PDFuzz, a novel attack that exploits the discrepancy between visual text layout and extraction order in PDF documents. Our...

Hard-Earned Lessons in Access Control at Scale: Enforcing Identity and Policy across Trust Boundaries with Reverse Proxies and MTLS

In today's enterprise environment, traditional access methods such as Virtual Private Networks VPNs and application-specific Single Sign-On SSO often fall short when it comes to securely scaling access for a distributed and dynamic workforce. This paper presents our experience implementing a...

Proactive Disentangled Modeling of Trigger-Object Pairings for Backdoor Defense

Deep neural networks DNNs and generative AI GenAI are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify or misinterpret target labels. Beyond traditional single-trigger scenarios, attackers may inject multiple triggers across...

GPU in the Blind Spot: Overlooked Security Risks in Transportation

Graphics processing units GPUs are becoming an essential part of the intelligent transportation system ITS for enabling video-based and artificial intelligence AI based applications. GPUs provide high-throughput and energy-efficient computing for tasks like sensor fusion and roadside video...

IMU: Influence-Guided Machine Unlearning

Recent studies have shown that deep learning models are vulnerable to attacks and tend to memorize training data points, raising significant concerns about privacy leakage. This motivates the development of machine unlearning MU, i.e., a paradigm that enables models to selectively forget specific...

A Provably Secure Network Protocol for Private Communication with Analysis and Tracing Resistance

Anonymous communication networks have emerged as crucial tools for obfuscating communication pathways and concealing user identities. However, their practical deployments face significant challenges, including susceptibility to artificial intelligence AI-powered metadata analysis, difficulties in...

Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: a Survey and Tutorial

Space-air-ground integrated networks SAGINs face unprecedented security challenges due to their inherent characteristics, such as multidimensional heterogeneity and dynamic topologies. These characteristics fundamentally undermine conventional security methods and traditional artificial...

DUP: Detection-Guided Unlearning for Backdoor Purification in Language Models

As backdoor attacks become more stealthy and robust, they reveal critical weaknesses in current defense strategies: detection methods often rely on coarse-grained feature statistics, and purification methods typically require full retraining or additional clean models. To address these challenges...

Analyzing the Mirai IoT Botnet and Its Recent Variants: Satori, Mukashi, Moobot, and Sonic

Mirai is undoubtedly one of the most significant Internet of Things IoT botnet attacks in history. In terms of its detrimental effects, seamless spread, and low detection rate, it surpassed its predecessors. Its developers released the source code, which triggered the development of several...

VWAttacker: a Systematic Security Testing Framework for Voice over WiFi User Equipments

We present VWAttacker, the first systematic testing framework for analyzing the security of Voice over WiFi VoWiFi User Equipment UE implementations. VWAttacker includes a complete VoWiFi network testbed that communicates with Commercial-Off-The-Shelf COTS UEs based on a simple interface to test...

Think Broad, Act Narrow: CWE Identification with Multi-Agent Large Language Models

Machine learning and Large language models LLMs for vulnerability detection has received significant attention in recent years. Unfortunately, state-of-the-art techniques show that LLMs are unsuccessful in even distinguishing the vulnerable function from its benign counterpart, due to three main...

Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks

The increase of IoT devices, driven by advancements in hardware technologies, has led to widespread deployment in large-scale networks that process massive amounts of data daily. However, the reliance on Edge Computing to manage these devices has introduced significant security vulnerabilities, a...

Packet Storm New Exploits for July, 2025

This archive contains all of the 132 exploits added to Packet Storm in July, 2025...

Implementing Zero Trust Architecture to Enhance Security and Resilience in the Pharmaceutical Supply Chain

The pharmaceutical supply chain faces escalating cybersecurity challenges threatening patient safety and operational continuity. This paper examines the transformative potential of zero trust architecture for enhancing security and resilience within this critical ecosystem. We explore the...

Rtpengine mr13.4.1.1 Injection / Redirection

Rtpengine starting at version mr13.4.1.1 allows for redirection to an attacker-controlled host and insertion of arbitrary RTP packet into active calls...

Apple Security Advisory 07-29-2025-4

Apple Security Advisory 07-29-2025-4 - macOS Sonoma 14.7.7 addresses bypass, code execution, integer overflow, out of bounds access, out of bounds read, and use-after-free vulnerabilities...

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection

Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and...

Apple Security Advisory 07-29-2025-5

Apple Security Advisory 07-29-2025-5 - macOS Ventura 13.7.7 addresses bypass, code execution, integer overflow, out of bounds access, out of bounds read, and use-after-free vulnerabilities...

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection

With the rapid expansion of web-based applications and cloud services, malicious JavaScript code continues to pose significant threats to user privacy, system integrity, and enterprise security. But, detecting such threats remains challenging due to sophisticated code obfuscation techniques and...

Apple Security Advisory 07-29-2025-6

Apple Security Advisory 07-29-2025-6 - watchOS 11.6 addresses out of bounds access, out of bounds read, and use-after-free vulnerabilities...

Apple Security Advisory 07-29-2025-3

Apple Security Advisory 07-29-2025-3 - macOS Sequoia 15.6 addresses bypass, cross site scripting, integer overflow, out of bounds access, out of bounds read, out of bounds write, and use-after-free vulnerabilities...

Apple Security Advisory 07-29-2025-2

Apple Security Advisory 07-29-2025-2 - iPadOS 17.7.9 addresses out of bounds access, out of bounds read, and use-after-free vulnerabilities...

Concrete Security Bounds for Simulation-Based Proofs of Multi-Party Computation Protocols

The concrete security paradigm aims to give precise bounds on the probability that an adversary can subvert a cryptographic mechanism. This is in contrast to asymptotic security, where the probability of subversion may be eventually small, but large enough in practice to be insecure. Fully...

SpectraSentinel: LightWeight Dual-Stream Real-Time Drone Detection, Tracking and Payload Identification

The proliferation of drones in civilian airspace has raised urgent security concerns, necessitating robust real-time surveillance systems. In response to the 2025 VIP Cup challenge tasks - drone detection, tracking, and payload identification - we propose a dual-stream drone monitoring framework...

GitKraken Desktop 11.2.1 Code Injection

GitKraken Desktop versions 10.8.0 through 11.2.1 are vulnerable to code injection via misconfigured Electron Fuses...

Malleability-Resistant Encrypted Control System with Disturbance Compensation and Real-Time Attack Detection

This study proposes an encrypted PID control system with a disturbance observer DOB using a keyed-homomorphic encryption KHE scheme, aiming to achieve control performance while providing resistance to malleability-based attacks. The controller integrates a DOB with a PID structure to compensate f...

Apple Security Advisory 07-29-2025-8

Apple Security Advisory 07-29-2025-8 - visionOS 2.6 addresses out of bounds access, out of bounds read, and use-after-free vulnerabilities...

Cryptanalysis of LC-MUME: a Lightweight Certificateless Multi-User Matchmaking Encryption for Mobile Devices

Yang et al. proposed a lightweight certificateless multiuser matchmaking encryption LC-MUME scheme for mobile devices, published in IEEE Transactions on Information Forensics and Security TIFS DOI: 10.1109/TIFS.2023.3321961. Their construction aims to reduce computational and communication overhe...

SAEL: Leveraging Large Language Models with Adaptive Mixture-Of-Experts for Smart Contract Vulnerability Detection

With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitations: 1 Static analysis methods struggle with complex scenarios. 2 Methods based on specialized pre-trained models...

Apple Security Advisory 07-29-2025-1

Apple Security Advisory 07-29-2025-1 - iOS 18.6 and iPadOS 18.6 addresses out of bounds access, out of bounds read, spoofing, and use-after-free vulnerabilities...

Resource-Efficient Automatic Software Vulnerability Assessment Via Knowledge Distillation and Particle Swarm Optimization

The increasing complexity of software systems has led to a surge in cybersecurity vulnerabilities, necessitating efficient and scalable solutions for vulnerability assessment. However, the deployment of large pre-trained models in real-world scenarios is hindered by their substantial computationa...

Apple Security Advisory 07-29-2025-7

Apple Security Advisory 07-29-2025-7 - tvOS 18.6 addresses out of bounds access, out of bounds read, and use-after-free vulnerabilities...

Microsegmentation in Zero Trust Part One: Introduction and Planning

The Journey to Zero Trust series covers cybersecurity capabilities and architecture supporting organization adoption of modern zero trust ZT principles. ZT’s core concept of never trust and always verify evolved from prior cybersecurity models. This current ZT series supports an organization’s ZT...

DoS Attacks and Defense Technologies in Blockchain Systems: a Hierarchical Analysis

Blockchain technology is widely used in various fields due to its ability to provide decentralization and trustless security. This is a fundamental understanding held by many advocates, but it is misunderstood, leading participants to fail to recognize the limitations of the security that...

SHoM: a Mental-Synthesis Trust Management Model for Mitigating Botnet-Driven DDoS Attacks in the Internet of Things

The advantages of IoT in strengthening commercial, industrial, and social ecosystems have led to its widespread expansion. Nevertheless, because endpoint devices have limited computation, storage, and communication capabilities, the IoT infrastructure is vulnerable to several cyber threats. As a...

Strategic Deflection: Defending LLMs from Logit Manipulation

With the growing adoption of Large Language Models LLMs in critical areas, ensuring their security against jailbreaking attacks is paramount. While traditional defenses primarily rely on refusing malicious prompts, recent logit-level attacks have demonstrated the ability to bypass these safeguard...

Prompt Optimization and Evaluation for LLM Automated Red Teaming

Applications that use Large Language Models LLMs are becoming widespread, making the identification of system vulnerabilities increasingly important. Automated Red Teaming accelerates this effort by using an LLM to generate and execute attacks against target systems. Attack generators are evaluat...

ZIUM: Zero-Shot Intent-Aware Adversarial Attack on Unlearned Models

Machine unlearning MU removes specific data points or concepts from deep learning models to enhance privacy and prevent sensitive content generation. Adversarial prompts can exploit unlearned models to generate content containing removed concepts, posing a significant security risk. However,...

Invisible Injections: Exploiting Vision-Language Models through Steganographic Prompt Embedding

Vision-language models VLMs have revolutionized multimodal AI applications but introduce novel security vulnerabilities that remain largely unexplored. We present the first comprehensive study of steganographic prompt injection attacks against VLMs, where malicious instructions are invisibly...

Lynis Auditing Tool 3.1.5

Lynis is an auditing tool for Unix specialists. It scans the system and available software to detect security issues. Beside security related information it will also scan for general system information, installed packages and configuration mistakes. This software aims in assisting automated...

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection

Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning ML algorithms have been applied to Android...

PRISM: Programmatic Reasoning with Image Sequence Manipulation for LVLM Jailbreaking

The increasing sophistication of large vision-language models LVLMs has been accompanied by advances in safety alignment mechanisms designed to prevent harmful content generation. However, these defenses remain vulnerable to sophisticated adversarial attacks. Existing jailbreak methods typically...

Whisper Smarter, Not Harder: Adversarial Attack on Partial Suppression

Currently, Automatic Speech Recognition ASR models are deployed in an extensive range of applications. However, recent studies have demonstrated the possibility of adversarial attack on these models which could potentially suppress or disrupt model output. We investigate and verify the robustness...

Benchmarking Fraud Detectors on Private Graph Data

We introduce the novel problem of benchmarking fraud detectors on private graph-structured data. Currently, many types of fraud are managed in part by automated detection algorithms that operate over graphs. We consider the scenario where a data holder wishes to outsource development of fraud...

Secure Coding for Web Applications: Frameworks, Challenges, and the Role of LLMs

Secure coding is a critical yet often overlooked practice in software development. Despite extensive awareness efforts, real-world adoption remains inconsistent due to organizational, educational, and technical barriers. This paper provides a comprehensive review of secure coding practices across...

POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage

Microelectronic systems are widely used in many sensitive applications e.g., manufacturing, energy, defense. These systems increasingly handle sensitive data e.g., encryption key and are vulnerable to diverse threats, such as, power side-channel attacks, which infer sensitive data through dynamic...

Optimal Planning for Enhancing the Resilience of Modern Distribution Systems against Cyberattacks

The increasing integration of IoT-connected devices in smart grids has introduced new vulnerabilities at the distribution level. Of particular concern is the potential for cyberattacks that exploit high-wattage IoT devices, such as EV chargers, to manipulate local demand and destabilize the grid...