PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning

Decentralized learning is vulnerable to poison attacks, where malicious clients manipulate local updates to degrade global model performance. Existing defenses mainly detect and filter malicious models, aiming to prevent a limited number of attackers from corrupting the global model. However,...

EVA-S2PMLP: Secure and Scalable Two-Party MLP via Spatial Transformation

Whitepaper called EVA-S2PMLP: Secure and Scalable Two-Party MLP via Spatial Transformation...

Flexible Hardware-Enabled Guarantees for AI Compute

As artificial intelligence systems become increasingly powerful, they pose growing risks to international security, creating urgent coordination challenges that current governance approaches struggle to address without compromising sensitive information or national security. We propose flexible...

CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets

In the ever-expanding domain of 5G-NR wireless cellular networks, over-the-air jamming attacks are prevalent as security attacks, compromising the quality of the received signal. We simulate a jamming environment by incorporating additive white Gaussian noise AWGN into the real-world In-phase and...

Linear and Numerical SDoF Bounds of Active RIS-Assisted MIMO Wiretap Interference Channel

The multiple-input multiple-output MIMO wiretap interference channel IC serves as a canonical model for information-theoretic security, where a multiple-antenna eavesdropper attempts to intercept communications in a two-user MIMO IC system. The secure degrees-of-freedom SDoF of an active...

Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging

The rapid advancements in autonomous vehicle AV technology promise enhanced safety and operational efficiency. However, frequent lane changes and merging maneuvers continue to pose significant safety risks and disrupt traffic flow. This paper introduces the Minimizing Lane Change Algorithm MLCA, ...

Private Continual Counting of Unbounded Streams

We study the problem of differentially private continual counting in the unbounded setting where the input size $n$ is not known in advance. Current state-of-the-art algorithms based on optimal instantiations of the matrix mechanism cannot be directly applied here because their privacy guarantees...

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...

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...

Real-Time, Low-Latency Surveillance Using Entropy-Based Adaptive Buffering and MobileNetV2 on Edge Devices

This paper describes a high-performance, low-latency video surveillance system designed for resource-constrained environments. We have proposed a formal entropy-based adaptive frame buffering algorithm and integrated that with MobileNetV2 to achieve high throughput with low latency. The system is...

Optimistic MEV in Ethereum Layer 2s: Why Blockspace Is Always in Demand

Layer 2 rollups are rapidly absorbing DeFi activity, securing over $40 billion and accounting for nearly half of Ethereum's DEX volume by Q1 2025, yet their MEV dynamics remain understudied. We address this gap by defining and quantifying optimistic MEV, a form of speculative, on-chain cyclic...

AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions

The rapid advancement of vision-language models VLMs and their integration into embodied agents have unlocked powerful capabilities for decision-making. However, as these systems are increasingly deployed in real-world environments, they face mounting safety concerns, particularly when responding...

AIRTBench: Measuring Autonomous AI Red Teaming Capabilities in Language Models

We introduce AIRTBench, an AI red teaming benchmark for evaluating language models' ability to autonomously discover and exploit Artificial Intelligence and Machine Learning AI/ML security vulnerabilities. The benchmark consists of 70 realistic black-box capture-the-flag CTF challenges from the...

Now More Than Ever, Foundational AI Research and Infrastructure Depends on the Federal Government

Leadership in the field of AI is vital for our nation's economy and security. Maintaining this leadership requires investments by the federal government. The federal investment in foundation AI research is essential for U.S. leadership in the field. Providing accessible AI infrastructure will...

An Advanced Reliability Reserve Incentivizes Flexibility Investments While Safeguarding the Electricity Market

To ensure security of supply in the power sector, many countries are already using or discussing the introduction of capacity mechanisms. Two main types of such mechanisms include capacity markets and capacity reserves. Simultaneously, the expansion of variable renewable energy sources increases...

Navigating the Growing Field of Research on AI for Software Testing

In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test...

Certified Randomness from Quantum Speed Limits

Quantum speed limits are usually regarded as fundamental restrictions, constraining the amount of computation that can be achieved within some given time and energy. Complementary to this intuition, here we show that these limitations are also of operational value: they enable the secure generati...

LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops

Multimodal Large Language Models MLLMs have shown great promise but require substantial computational resources during inference. Attackers can exploit this by inducing excessive output, leading to resource exhaustion and service degradation. Prior energy-latency attacks aim to increase generatio...

MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem

Malicious package detection has become a critical task in ensuring the security and stability of the PyPI. Existing detection approaches have focused on advancing model selection, evolving from traditional machine learning ML models to large language models LLMs. However, as the complexity of the...

Excessive Reasoning Attack on Reasoning LLMs

Recent reasoning large language models LLMs, such as OpenAI o1 and DeepSeek-R1, exhibit strong performance on complex tasks through test-time inference scaling. However, prior studies have shown that these models often incur significant computational costs due to excessive reasoning, such as...

Quantum Enhanced Entropy Pool for Cryptographic Applications and Proofs

This paper investigates the integration of quantum randomness into Verifiable Random Functions VRFs using the Ed25519 elliptic curve to strengthen cryptographic security. By replacing traditional pseudorandom number generators with quantum entropy sources, we assess the impact on key security and...

LLM-Powered Intent-Based Categorization of Phishing Emails

Phishing attacks remain a significant threat to modern cybersecurity, as they successfully deceive both humans and the defense mechanisms intended to protect them. Traditional detection systems primarily focus on email metadata that users cannot see in their inboxes. Additionally, these systems...

Vulnerability Disclosure or Notification? Best Practices for Reaching Stakeholders at Scale

Security researchers are interested in security vulnerabilities, but these security vulnerabilities create risks for stakeholders. Coordinated Vulnerability Disclosure has been an accepted best practice for many years in disclosing newly discovered vulnerabilities. This practice has mostly worked...

ImpReSS: Implicit Recommender System for Support Conversations

Following recent advancements in large language models LLMs, LLM-based chatbots have transformed customer support by automating interactions and providing consistent, scalable service. While LLM-based conversational recommender systems CRSs have attracted attention for their ability to enhance th...

The Redundancy of Full Nodes in Bitcoin: a Network-Theoretic Demonstration of Miner-Centric Propagation Topologies

This paper formally examines the network structure of Bitcoin CORE BTC and Bitcoin Satoshi Vision BSV using complex graph theory to demonstrate that home-hosted full nodes are incapable of participating in or influencing the propagation topology. Leveraging established models such as scale-free...

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,...

The Trip to ZigBee Backscatter across a Decade, a Systematic Review

The field of backscatter communication has undergone a profound transformation, evolving from a niche technology for radio-frequency identification RFID into a sophisticated paradigm poised to enable a truly battery-free Internet of Things IoT. This evolution is built upon a deepening understandi...

Personalized Constitutionally-Aligned Agentic Superego: Secure AI Behavior Aligned to Diverse Human Values

Agentic AI systems, possessing capabilities for autonomous planning and action, exhibit immense potential across diverse domains. However, their practical deployment is significantly hampered by challenges in aligning their behavior with varied human values, complex safety requirements, and...

A Halpha Metric for Identifying Dormant Black Holes in X-Ray Transients

Dormant black holes BHs in X-ray transients can be identified by the presence of broad Ha emission lines from quiescent accretion discs. Unfortunately, short-period cataclysmic variables CVs can also produce broad Ha lines, especially when viewed at high inclinations, and are thus a major source ...

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability

Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the...

Weakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models

The introduction of advanced reasoning capabilities have improved the problem-solving performance of large language models, particularly on math and coding benchmarks. However, it remains unclear whether these reasoning models are more or less vulnerable to adversarial prompt attacks than their...

Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning

Federated learning FL shows great promise in large-scale machine learning but introduces new privacy and security challenges. We propose ByITFL and LoByITFL, two novel FL schemes that enhance resilience against Byzantine users while keeping the users' data private from eavesdroppers. To ensure...

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers

Although Foundation Models FMs, such as GPT-4, are increasingly used in domains like finance and software engineering, reliance on textual interfaces limits these models' real-world interaction. To address this, FM providers introduced tool calling-triggering a proliferation of frameworks with...

Q-AIM: a Unified Portable Workflow for Seamless Integration of Quantum Resources

Quantum computing QC holds the potential to solve classically intractable problems. Although there has been significant progress towards the availability of quantum hardware, a software infrastructure to integrate them is still missing. We present Q-AIM Quantum Access Infrastructure Management to...

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs

Large Language Models LLMs are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simulate adversaries, and generate exploit code. Conversely, blue teams may deploy them for threat intelligence synthesis, ro...

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks

Bitcoin is a representative decentralized currency system. For the security of Bitcoin, fairness in the distribution of mining rewards plays a crucial role in preventing the concentration of computational power in a few miners. Here, fairness refers to the distribution of block rewards in...

Tady: a Neural Disassembler without Structural Constraint Violations

Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this...

On Immutable Memory Systems for Artificial Agents: a Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains

This paper presents a formalized architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we...

Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models

Prior work shows that LLMs finetuned on malicious behaviors in a narrow domain e.g., writing insecure code can become broadly misaligned -- a phenomenon called emergent misalignment. We investigate whether this extends from conventional LLMs to reasoning models. We finetune reasoning models on...

Using LLMs for Security Advisory Investigations: How Far Are We?

Large Language Models LLMs are increasingly used in software security, but their trustworthiness in generating accurate vulnerability advisories remains uncertain. This study investigates the ability of ChatGPT to 1 generate plausible security advisories from CVE-IDs, 2 differentiate real from fa...

On Secure UAV-Aided ISCC Systems

Integrated communication and sensing, which can make full use of the limited spectrum resources to perform communication and sensing tasks simultaneously, is an up-and-coming technology in wireless communication networks. In this work, we investigate the secrecy performance of an uncrewed aerial...

Autonomous 3D Moving Target Encirclement and Interception with Range Measurement

Commercial UAVs are an emerging security threat as they are capable of carrying hazardous payloads or disrupting air traffic. To counter UAVs, we introduce an autonomous 3D target encirclement and interception strategy. Unlike traditional ground-guided systems, this strategy employs autonomous...

Buy It Now, Track Me Later: Attacking User Privacy Via Wi-Fi AP Online Auctions

Static and hard-coded layer-two network identifiers are well known to present security vulnerabilities and endanger user privacy. In this work, we introduce a new privacy attack against Wi-Fi access points listed on secondhand marketplaces. Specifically, we demonstrate the ability to remotely...

Position: Certified Robustness Does Not (Yet) Imply Model Security

While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world applications. We identify critical gaps in current research, including the parad...

LexiMark: Robust Watermarking via Lexical Substitutions to Enhance Membership Verification of an LLM's Textual Training Data

Large language models LLMs can be trained or fine-tuned on data obtained without the owner's consent. Verifying whether a specific LLM was trained on particular data instances or an entire dataset is extremely challenging. Dataset watermarking addresses this by embedding identifiable modification...

CEGA: a Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Graph Neural Networks GNNs have demonstrated remarkable utility across diverse applications, and their growing complexity has made Machine Learning as a Service MLaaS a viable platform for scalable deployment. However, this accessibility also exposes GNN to serious security threats, most notably...

SAVANT: Vulnerability Detection in Application Dependencies through Semantic-Guided Reachability Analysis

The integration of open-source third-party library dependencies in Java development introduces significant security risks when these libraries contain known vulnerabilities. Existing Software Composition Analysis SCA tools struggle to effectively detect vulnerable API usage from these libraries d...

Efficient Retail Video Annotation: a Robust Key Frame Generation Approach for Product and Customer Interaction Analysis

Accurate video annotation plays a vital role in modern retail applications, including customer behavior analysis, product interaction detection, and in-store activity recognition. However, conventional annotation methods heavily rely on time-consuming manual labeling by human annotators,...

SoK: Stablecoin Designs, Risks, and the Stablecoin LEGO

Stablecoins have become significant assets in modern finance, with a market capitalization exceeding USD 246 billion May 2025. Yet, despite their systemic importance, a comprehensive and risk-oriented understanding of crucial aspects like their design trade-offs, security dynamics, and...

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments

With the growing integration of vision-language models VLMs, mobile agents are now widely used for tasks like UI automation and camera-based user assistance. These agents are often fine-tuned on limited user-generated datasets, leaving them vulnerable to covert threats during the training process...