NanoZone: Scalable, Efficient, and Secure Memory Protection for Arm CCA

Arm Confidential Computing Architecture CCA currently isolates at the granularity of an entire Confidential Virtual Machine CVM, leaving intra-VM bugs such as Heartbleed unmitigated. The state-of-the-art narrows this to the process level, yet still cannot stop attacks that pivot within the same...

Efficient RL-Based Cache Vulnerability Exploration by Penalizing Useless Agent Actions

Cache-timing attacks exploit microarchitectural characteristics to leak sensitive data, posing a severe threat to modern systems. Despite its severity, analyzing the vulnerability of a given cache structure against cache-timing attacks is challenging. To this end, a method based on Reinforcement...

Mind the Web: the Security of Web Use Agents

Web-use agents are rapidly being deployed to automate complex web tasks, operating with extensive browser capabilities including multi-tab navigation, DOM manipulation, JavaScript execution and authenticated session access. However, these powerful capabilities create a critical and previously...

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

The remarkable success of Large Language Models LLMs has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communities, owing to their unprecedented performance across various applications. As LLMs continue to gain prominence in both...

From Static to Adaptive Defense: Federated Multi-Agent Deep Reinforcement Learning-Driven Moving Target Defense against DoS Attacks in UAV Swarm Networks

The proliferation of unmanned aerial vehicle UAV swarms has enabled a wide range of mission-critical applications, but also exposes UAV networks to severe Denial-of-Service DoS threats due to their open wireless environment, dynamic topology, and resource constraints. Traditional static or...

Enhancing Watermarking Quality for LLMs Via Contextual Generation States Awareness

Recent advancements in watermarking techniques have enabled the embedding of secret messages into AI-generated text AIGT, serving as an important mechanism for AIGT detection. Existing methods typically interfere with the generation processes of large language models LLMs to embed signals within...

Exploiting Inaccurate Branch History in Side-Channel Attacks

Modern out-of-order CPUs heavily rely on speculative execution for performance optimization, with branch prediction serving as a cornerstone to minimize stalls and maximize efficiency. Whenever shared branch prediction resources lack proper isolation and sanitization methods, they may originate...

Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models

Differential Privacy DP is a widely adopted technique, valued for its effectiveness in protecting the privacy of task-specific datasets, making it a critical tool for large language models. However, its effectiveness in Multimodal Large Language Models MLLMs remains uncertain. Applying Differenti...

MARVEL: Multi-Agent RTL Vulnerability Extraction Using Large Language Models

Hardware security verification is a challenging and time-consuming task. For this purpose, design engineers may utilize tools such as formal verification, linters, and functional simulation tests, coupled with analysis and a deep understanding of the hardware design being inspected. Large Languag...

Beyond Jailbreaks: Revealing Stealthier and Broader LLM Security Risks Stemming from Alignment Failures

Large language models LLMs are increasingly deployed in real-world applications, raising concerns about their security. While jailbreak attacks highlight failures under overtly harmful queries, they overlook a critical risk: incorrectly answering harmless-looking inputs can be dangerous and cause...

ModelForge: Using GenAI to Improve the Development of Security Protocols

Formal methods can be used for verifying security protocols, but their adoption can be hindered by the complexity of translating natural language protocol specifications into formal representations. In this paper, we introduce ModelForge, a novel tool that automates the translation of protocol...

HauntAttack: When Attack Follows Reasoning As a Shadow

Emerging Large Reasoning Models LRMs consistently excel in mathematical and reasoning tasks, showcasing exceptional capabilities. However, the enhancement of reasoning abilities and the exposure of their internal reasoning processes introduce new safety vulnerabilities. One intriguing concern is:...

JavelinGuard: Low-Cost Transformer Architectures for LLM Security

We present JavelinGuard, a suite of low-cost, high-performance model architectures designed for detecting malicious intent in Large Language Model LLM interactions, optimized specifically for production deployment. Recent advances in transformer architectures, including compact BERTDevlin et al...

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...

A Simulation-Based Evaluation Framework for Inter-VM RowHammer Mitigation Techniques

Inter-VM RowHammer is an attack that induces a bitflip beyond the boundaries of virtual machines VMs to compromise a VM from another, and some software-based techniques have been proposed to mitigate this attack. Evaluating these mitigation techniques requires to confirm that they actually mitiga...

Shuffling Cards When You Are of Very Little Brain: Low Memory Generation of Permutations

How can we generate a permutation of the numbers $1$ through $n$ so that it is hard to guess the next element given the history so far? The twist is that the generator of the permutation the "Dealer" has limited memory, while the "Guesser" has unlimited memory. With unbounded memory actually $n$...

An Ultra-Sub-Wavelength Microwave Polarization Switch Implemented with Directed Surface Acoustic Waves in a Magnonic Crystal

The ability to switch the polarization of a transmitted electromagnetic wave from vertical to horizontal, or vice versa, is of great technological interest because of its many applications in long distance communication. Binary bits can be encoded in two orthogonal polarizations and transmitted...

Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning

The rapid global adoption of electric vehicles EVs has established electric vehicle supply equipment EVSE as a critical component of smart grid infrastructure. While essential for ensuring reliable energy delivery and accessibility, EVSE systems face significant cybersecurity challenges, includin...

From Threat to Tool: Leveraging Refusal-Aware Injection Attacks for Safety Alignment

Safely aligning large language models LLMs often demands extensive human-labeled preference data, a process that's both costly and time-consuming. While synthetic data offers a promising alternative, current methods frequently rely on complex iterative prompting or auxiliary models. To address...

Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions

Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows,...

SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding

Federated recommender system FedRec has emerged as a solution to protect user data through collaborative training techniques. A typical FedRec involves transmitting the full model and entire weight updates between edge devices and the server, causing significant burdens to devices with limited...

Can In-Context Reinforcement Learning Recover from Reward Poisoning Attacks?

We study the corruption-robustness of in-context reinforcement learning ICRL, focusing on the Decision-Pretrained Transformer DPT, Lee et al., 2023. To address the challenge of reward poisoning attacks targeting the DPT, we propose a novel adversarial training framework, called Adversarially...

Identity Deepfake Threats to Biometric Authentication Systems: Public and Expert Perspectives

Generative AI Gen-AI deepfakes pose a rapidly evolving threat to biometric authentication, yet a significant gap exists between expert understanding of these risks and public perception. This disconnection creates critical vulnerabilities in systems trusted by millions. To bridge this gap, we...

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...

Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models Via Generative Continual Learning

Foundation models have revolutionized fields such as natural language processing and computer vision by enabling general-purpose learning across diverse tasks and datasets. However, building analogous models for human mobility remains challenging due to the privacy-sensitive nature of mobility da...

Differentially Private Sparse Linear Regression with Heavy-Tailed Responses

As a fundamental problem in machine learning and differential privacy DP, DP linear regression has been extensively studied. However, most existing methods focus primarily on either regular data distributions or low-dimensional cases with irregular data. To address these limitations, this paper...

Rewriting the Budget: a General Framework for Black-Box Attacks under Cost Asymmetry

Traditional decision-based black-box adversarial attacks on image classifiers aim to generate adversarial examples by slightly modifying input images while keeping the number of queries low, where each query involves sending an input to the model and observing its output. Most existing methods...

ARGOS: Anomaly Recognition and Guarding through O-RAN Sensing

Rogue Base Station RBS attacks, particularly those exploiting downgrade vulnerabilities, remain a persistent threat as 5G Standalone SA deployments are still limited and User Equipment UE manufacturers continue to support legacy network connectivity. This work introduces ARGOS, a comprehensive...

An Efficient Digital Watermarking Technique for Small Scale Devices

In the age of IoT and mobile platforms, ensuring that content stay authentic whilst avoiding overburdening limited hardware is a key problem. This study introduces hybrid Fast Wavelet Transform & Additive Quantization index Modulation FWT-AQIM scheme, a lightweight watermarking approach that...

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems

Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats APTs, by exposing complex attack patterns. While recent systems combine graph neural networks GNNs with natural language processing NLP to capture structural and semantic features...

Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing

Whitepaper called Dual-Conditional Deep Generation Of Network Traffic Data For Network Intrusion Detection System Balancing...

SDN-Based False Data Detection with Its Mitigation and Machine Learning Robustness for In-Vehicle Networks

As the development of autonomous and connected vehicles advances, the complexity of modern vehicles increases, with numerous Electronic Control Units ECUs integrated into the system. In an in-vehicle network, these ECUs communicate with one another using an standard protocol called Controller Are...

Differentially Private Explanations for Clusters

The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy DP is considered one of the most rigorous and secure notions of privacy, enabling data analysis while preserving the privacy of data contributors. One of the fundamental...

TrustConnect: an In-Vehicle Anomaly Detection Framework through Topology-Based Trust Rating

Modern vehicles are equipped with numerous in-vehicle components that interact with the external environment through remote communications and services, such as Bluetooth and vehicle-to-infrastructure communication. These components form a network, exchanging information to ensure the proper...

Obfuscation-Resilient Binary Code Similarity Analysis Using Dominance Enhanced Semantic Graph

Binary code similarity analysis BCSA serves as a core technique for binary analysis tasks such as vulnerability detection. While current graph-based BCSA approaches capture substantial semantics and show strong performance, their performance suffers under code obfuscation due to the unstable...

Scoring the Unscorables: Cyber Risk Assessment beyond Internet Scans

In this paper we present a study on using novel data types to perform cyber risk quantification by estimating the likelihood of a data breach. We demonstrate that it is feasible to build a highly accurate cyber risk assessment model using public and readily available technology signatures obtaine...

PoCGen: Generating Proof-Of-Concept Exploits for Vulnerabilities in Npm Packages

Security vulnerabilities in software packages are a significant concern for developers and users alike. Patching these vulnerabilities in a timely manner is crucial to restoring the integrity and security of software systems. However, previous work has shown that vulnerability reports often lack...

XWiki 15.10.10 Remote Code Execution

XWiki versions up to 15.10.10 proof of concept remote code execution exploit written in go...

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation RAG systems enhance Large Language Models LLMs by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast, up-to-date external knowledge. However, this reliance on...

Stochastic Training for Side-Channel Resilient AI

The confidentiality of trained AI models on edge devices is at risk from side-channel attacks exploiting power and electromagnetic emissions. This paper proposes a novel training methodology to enhance resilience against such threats by introducing randomized and interchangeable model...

Wireshark Analyzer 4.4.7

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers. Thi...

Saffron-1: Towards an Inference Scaling Paradigm for LLM Safety Assurance

Existing safety assurance research has primarily focused on training-phase alignment to instill safe behaviors into LLMs. However, recent studies have exposed these methods' susceptibility to diverse jailbreak attacks. Concurrently, inference scaling has significantly advanced LLM reasoning...

QualitEye: Public and Privacy-Preserving Gaze Data Quality Verification

Gaze-based applications are increasingly advancing with the availability of large datasets but ensuring data quality presents a substantial challenge when collecting data at scale. It further requires different parties to collaborate, therefore, privacy concerns arise. We propose QualitEye--the...

A Certified Unlearning Approach without Access to Source Data

With the growing adoption of data privacy regulations, the ability to erase private or copyrighted information from trained models has become a crucial requirement. Traditional unlearning methods often assume access to the complete training dataset, which is unrealistic in scenarios where the...

HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions

Large language model LLM watermarks enable authentication of text provenance, curb misuse of machine-generated text, and promote trust in AI systems. Current watermarks operate by changing the next-token predictions output by an LLM. The updated i.e., watermarked predictions depend on random side...

Falco 0.41.1

Sysdig Falco is a behavioral activity monitoring agent that is open source and comes with native support for containers. Falco lets you define highly granular rules to check for activities involving file and network activity, process execution, IPC, and much more, using a flexible syntax. Falco...

A Systematic Review of Poisoning Attacks against Large Language Models

With the widespread availability of pretrained Large Language Models LLMs and their training datasets, concerns about the security risks associated with their usage has increased significantly. One of these security risks is the threat of LLM poisoning attacks where an attacker modifies some part...

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-Level Unlearning Completeness

Growing concerns over data privacy and security highlight the importance of machine unlearning--removing specific data influences from trained models without full retraining. Techniques like Membership Inference Attacks MIAs are widely used to externally assess successful unlearning. However,...

WordPress HyperComments 1.2.2 Privilege Escalation

WordPress HyperComments plugin versions 1.2.2 and below suffer from an unauthenticated remote privilege escalation vulnerability...

Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models

Watermarking becomes one of the pivotal solutions to trace and verify the origin of synthetic images generated by artificial intelligence models, but it is not free of risks. Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial...