Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems

Large Language Models LLMs deployed in enterprise settings e.g., as Microsoft 365 Copilot face novel security challenges. One critical threat is prompt inference attacks: adversaries chain together seemingly benign prompts to gradually extract confidential data. In this paper, we present a...

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy

To mitigate privacy leakage and performance issues in personalized advertising, this paper proposes a framework that integrates federated learning and differential privacy. The system combines distributed feature extraction, dynamic privacy budget allocation, and robust model aggregation to balan...

Key Takeaways from the Take Command Summit 2025: Outpacing the Adversary – Red Teaming in a Complex Threat Landscape

The evolving complexity of modern infrastructures calls for more than traditional pen testing. In this session from the Take Command 2025 Virtual Cybersecurity Summit, red team experts shared how organizations are using continuous testing to outpace attackers — and better prepare their teams to...

CVE-2025-38346 ftrace: Fix UAF when lookup kallsym after ftrace disabled

In the Linux kernel, the following vulnerability has been resolved: ftrace: Fix UAF when lookup kallsym after ftrace disabled The following issue happens with a buggy module: BUG: unable to handle page fault for address: ffffffffc05d0218 PGD 1bd66f067 P4D 1bd66f067 PUD 1bd671067 PMD 101808067 PTE...

IThermTroj: Exploiting Intermittent Thermal Trojans in Multi-Processor System-On-Chips

Thermal Trojan attacks present a pressing concern for the security and reliability of System-on-Chips SoCs, especially in mobile applications. The situation becomes more complicated when such attacks are more evasive and operate sporadically to stay hidden from detection mechanisms. In this paper...

SoK: a Systematic Review of Context- and Behavior-Aware Adaptive Authentication in Mobile Environments

As mobile computing becomes central to digital interaction, researchers have turned their attention to adaptive authentication for its real-time, context- and behavior-aware verification capabilities. However, many implementations remain fragmented, inconsistently apply intelligent techniques, an...

ML-Enhanced AES Anomaly Detection for Real-Time Embedded Security

Advanced Encryption Standard AES is a widely adopted cryptographic algorithm, yet its practical implementations remain susceptible to side-channel and fault injection attacks. In this work, we propose a comprehensive framework that enhances AES-128 encryption security through controlled anomaly...

SmartHome-Bench: a Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models

Video anomaly detection VAD is essential for enhancing safety and security by identifying unusual events across different environments. Existing VAD benchmarks, however, are primarily designed for general-purpose scenarios, neglecting the specific characteristics of smart home applications. To...

GNSS Spoofing Detection Based on Opportunistic Position Information

The limited or no protection for civilian Global Navigation Satellite System GNSS signals makes spoofing attacks relatively easy. With modern mobile devices often featuring network interfaces, state-of-the-art signals of opportunity SOP schemes can provide accurate network positions in replacemen...

Bridging Unsupervised and Semi-Supervised Anomaly Detection: a Theoretically-Grounded and Practical Framework with Synthetic Anomalies

Anomaly detection AD is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to train classifiers to distinguish normal data from synthetic anomalies. We extend this principle to semi-supervised AD,...

Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection

The decentralized finance DeFi community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of t...

Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders

Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders AE show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction...

Evaluation Pipeline for Systematically Searching for Anomaly Detection Systems

Digitalization in the medical world provides major benefits while making it a target for attackers and thus hard to secure. To deal with network intruders we propose an anomaly detection system on hardware to detect malicious clients in real-time. We meet real-time and power restrictions using...

SecureFed: a Two-Phase Framework for Detecting Malicious Clients in Federated Learning

Federated Learning FL protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL...

Enhanced Consistency Bi-Directional GAN(CBiGAN) for Malware Anomaly Detection

Static analysis, a cornerstone technique in cybersecurity, offers a noninvasive method for detecting malware by analyzing dormant software without executing potentially harmful code. However, traditional static analysis often relies on biased or outdated datasets, leading to gaps in detection...

LADSG: Label-Anonymized Distillation and Similar Gradient Substitution for Label Privacy in Vertical Federated Learning

Vertical federated learning VFL has become a key paradigm for collaborative machine learning, enabling multiple parties to train models over distributed feature spaces while preserving data privacy. Despite security protocols that defend against external attacks - such as gradient masking and...

Attack Effect Model Based Malicious Behavior Detection

Traditional security detection methods face three key challenges: inadequate data collection that misses critical security events, resource-intensive monitoring systems, and poor detection algorithms with high false positive rates. We present FEAD Focus-Enhanced Attack Detection, a framework that...

Transformers for Secure Hardware Systems: Applications, Challenges, and Outlook

The rise of hardware-level security threats, such as side-channel attacks, hardware Trojans, and firmware vulnerabilities, demands advanced detection mechanisms that are more intelligent and adaptive. Traditional methods often fall short in addressing the complexity and evasiveness of modern...

Zero-Trust Foundation Models: a New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

This paper focuses on Zero-Trust Foundation Models ZTFMs, a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models FMs for Internet of Things IoT systems. By integrating core tenets, such as continuous verification, least privilege access LPA, data...

CVE-2023-23933

OpenSearch Anomaly Detection identifies atypical data and receives automatic notifications. There is an issue with the application of document and field level restrictions in the Anomaly Detection plugin, where users with the Anomaly Detector role can read aggregated numerical data e.g. averages,...