REDEditing: Relationship-Driven Precise Backdoor Poisoning on Text-To-Image Diffusion Models

The rapid advancement of generative AI highlights the importance of text-to-image T2I security, particularly with the threat of backdoor poisoning. Timely disclosure and mitigation of security vulnerabilities in T2I models are crucial for ensuring the safe deployment of generative models. We...

Anonymous Public Announcements

We formalise the notion of an anonymous public announcement in the tradition of public announcement logic. Such announcements can be seen as in-between a public announcement from "the outside" an announcement of $φ$ and a public announcement by one of the agents an announcement of $Kaφ$: we get...

CSI2Dig: Recovering Digit Content from Smartphone Loudspeakers Using Channel State Information

Eavesdropping on sounds emitted by mobile device loudspeakers can capture sensitive digital information, such as SMS verification codes, credit card numbers, and withdrawal passwords, which poses significant security risks. Existing schemes either require expensive specialized equipment, rely on...

Reveal-Or-Obscure: a Differentially Private Sampling Algorithm for Discrete Distributions

We introduce a differentially private DP algorithm called reveal-or-obscure ROO to generate a single representative sample from a dataset of $n$ observations drawn i.i.d. from an unknown discrete distribution $P$. Unlike methods that add explicit noise to the estimated empirical distribution, ROO...

Fast Plaintext-Ciphertext Matrix Multiplication from Additively Homomorphic Encryption

Plaintext-ciphertext matrix multiplication PC-MM is an indispensable tool in privacy-preserving computations such as secure machine learning and encrypted signal processing. While there are many established algorithms for plaintext-plaintext matrix multiplication, efficiently computing...

Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: a Systematic Review

The Internet of Things IoT has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This growth enhances functionality, e.g., automation, remote monitoring, and control, and introduces substantial security...

ScaloWork: Useful Proof-Of-Work with Distributed Pool Mining

Bitcoin blockchain uses hash-based Proof-of-Work PoW that prevents unwanted participants from hogging the network resources. Anyone entering the mining game has to prove that they have expended a specific amount of computational power. However, the most popular Bitcoin blockchain consumes 175.87...

The First VoicePrivacy Attacker Challenge

The First VoicePrivacy Attacker Challenge is an ICASSP 2025 SP Grand Challenge which focuses on evaluating attacker systems against a set of voice anonymization systems submitted to the VoicePrivacy 2024 Challenge. Training, development, and evaluation datasets were provided along with a baseline...

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data

Differentially private DP machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretraining. While public data assumptions may be reasonable in text and image domains, they are less likely to hold for tabul...

How Do Mobile Applications Enhance Security? an Exploratory Analysis of Use Cases and Provided Information

The ubiquity of mobile applications has increased dramatically in recent years, opening up new opportunities for cyber attackers and heightening security concerns in the mobile ecosystem. As a result, researchers and practitioners have intensified their research into improving the security and...

From Cyber Security Incident Management to Cyber Security Crisis Management in the European Union

Incident management is a classical topic in cyber security. Recently, the European Union EU has started to consider also the relation between cyber security incidents and cyber security crises. These considerations and preparations, including those specified in the EU's new cyber security laws,...

A Data-Centric Approach for Safe and Secure Large Language Models against Threatening and Toxic Content

Large Language Models LLM have made remarkable progress, but concerns about potential biases and harmful content persist. To address these apprehensions, we introduce a practical solution for ensuring LLM's safe and ethical use. Our novel approach focuses on a post-generation correction mechanism...

Complexity of Post-Quantum Cryptography in Embedded Systems and Its Optimization Strategies

With the rapid advancements in quantum computing, traditional cryptographic schemes like Rivest-Shamir-Adleman RSA and elliptic curve cryptography ECC are becoming vulnerable, necessitating the development of quantum-resistant algorithms. The National Institute of Standards and Technology NIST ha...

Multi-Stage Retrieval for Operational Technology Cybersecurity Compliance Using Large Language Models: a Railway Casestudy

Operational Technology Cybersecurity OTCS continues to be a dominant challenge for critical infrastructure such as railways. As these systems become increasingly vulnerable to malicious attacks due to digitalization, effective documentation and compliance processes are essential to protect these...

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders

The rapid growth in web-based services has significantly increased security risks related to user information, as web-based attacks become increasingly sophisticated and prevalent. Traditional security methods frequently struggle to detect previously unknown zero-day web attacks, putting sensitiv...

Multi-Class Item Mining under Local Differential Privacy

Item mining, a fundamental task for collecting statistical data from users, has raised increasing privacy concerns. To address these concerns, local differential privacy LDP was proposed as a privacy-preserving technique. Existing LDP item mining mechanisms primarily concentrate on global...

Towards Stateless Clients in Ethereum: Benchmarking Verkle Trees and Binary Merkle Trees with SNARKs

Ethereum, the leading platform for decentralized applications, faces challenges in maintaining decentralization due to the significant hardware requirements for validators to store Ethereum's entire state. To address this, the concept of stateless clients is under exploration, enabling validators...

Scoring Azure Permissions with Metric Spaces

In this work, we introduce two complementary metrics for quantifying and scoring privilege risk in Microsoft Azure. In the Control Plane, we define the WAR distance, a superincreasing distance over Write, Action, and Read control permissions, which yields a total ordering of principals by their...

Designing a Reliable Lateral Movement Detector Using a Graph Foundation Model

Foundation models have recently emerged as a new paradigm in machine learning ML. These models are pre-trained on large and diverse datasets and can subsequently be applied to various downstream tasks with little or no retraining. This allows people without advanced ML expertise to build ML...

ROFBS$Α$: Real Time Backup System Decoupled from ML Based Ransomware Detection

This study introduces ROFBS$α$, a new defense architecture that addresses delays in detection in ransomware detectors based on machine learning. It builds on our earlier Real Time Open File Backup System, ROFBS, by adopting an asynchronous design that separates backup operations from detection...

Cybersquatting in Web3: the Case of NFT

Cybersquatting refers to the practice where attackers register a domain name similar to a legitimate one to confuse users for illegal gains. With the growth of the Non-Fungible Token NFT ecosystem, there are indications that cybersquatting tactics have evolved from targeting domain names to NFTs...

Monitor and Recover: a Paradigm for Future Research on Distribution Shift in Learning-Enabled Cyber-Physical Systems

With the known vulnerability of neural networks to distribution shift, maintaining reliability in learning-enabled cyber-physical systems poses a salient challenge. In response, many existing methods adopt a detect and abstain methodology, aiming to detect distribution shift at inference time so...

Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Prediction

As the number of web applications and API endpoints exposed to the Internet continues to grow, so does the number of exploitable vulnerabilities. Manually identifying such vulnerabilities is tedious. Meanwhile, static security scanners tend to produce many false positives. While machine...

GRR 3.4.9.1

GRR Rapid Response is an incident response framework focused on remote live forensics. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR consists of 2 parts: client and server. GRR...

Breaking ECDSA with Two Affinely Related Nonces

The security of the Elliptic Curve Digital Signature Algorithm ECDSA depends on the uniqueness and secrecy of the nonce, which is used in each signature. While it is well understood that nonce $k$ reuse across two distinct messages can leak the private key, we show that even if a distinct value i...

A Blockchain-Based Approach for Secure and Transparent E-Faktur Issuance in Indonesia'S VAT Reporting System

The implementation of blockchain technology in tax administration offers promising improvements in security, transparency, and efficiency. This paper presents the design of a blockchain-based e-Faktur system aimed at addressing the challenges of issuing and verifying tax invoices within Indonesia...

Benchmarking Differentially Private Tabular Data Synthesis

Differentially private DP tabular data synthesis generates artificial data that preserves the statistical properties of private data while safeguarding individual privacy. The emergence of diverse algorithms in recent years has introduced challenges in practical applications, such as inconsistent...

Q-FAKER: Query-Free Hard Black-Box Attack Via Controlled Generation

Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model's output information. They are not applicable in real-world...

Post Quantum Cryptography (PQC) Signatures without Trapdoors

Some of our current public key methods use a trap door to implement digital signature methods. This includes the RSA method, which uses Fermat's little theorem to support the creation and verification of a digital signature. The problem with a back-door is that the actual trap-door method could, ...

Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks

As cyber threats continue to evolve, securing edge networks has become increasingly challenging due to their distributed nature and resource limitations. Many AI-driven threat detection systems rely on complex deep learning models, which, despite their high accuracy, suffer from two major...

Access Control for Data Spaces

Data spaces represent an emerging paradigm that facilitates secure and trusted data exchange through foundational elements of data interoperability, sovereignty, and trust. Within a data space, data items, potentially owned by different entities, can be interconnected. Concurrently, data consumer...

Bitcoin'S Edge: Embedded Sentiment in Blockchain Transactional Data

Cryptocurrency blockchains, beyond their primary role as distributed payment systems, are increasingly used to store and share arbitrary content, such as text messages and files. Although often non-financial, this hidden content can impact price movements by conveying private information, shaping...

PT-Mark: Invisible Watermarking for Text-To-Image Diffusion Models Via Semantic-Aware Pivotal Tuning

Watermarking for diffusion images has drawn considerable attention due to the widespread use of text-to-image diffusion models and the increasing need for their copyright protection. Recently, advanced watermarking techniques, such as Tree Ring, integrate watermarks by embedding traceable pattern...

Everything You Wanted to Know about LLM-Based Vulnerability Detection but Were Afraid to Ask

Large Language Models are a promising tool for automated vulnerability detection, thanks to their success in code generation and repair. However, despite widespread adoption, a critical question remains: Are LLMs truly effective at detecting real-world vulnerabilities? Current evaluations, which...

DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-Based Intrusion Detection against Adversarial Attacks

The rapid proliferation of the Internet of Things IoT has introduced substantial security vulnerabilities, highlighting the need for robust Intrusion Detection Systems IDS. Machine learning-based intrusion detection systems ML-IDS have significantly improved threat detection capabilities; however...

The Impact of AI on the Cyber Offense-Defense Balance and the Character of Cyber Conflict

Unlike other domains of conflict, and unlike other fields with high anticipated risk from AI, the cyber domain is intrinsically digital with a tight feedback loop between AI training and cyber application. Cyber may have some of the largest and earliest impacts from AI, so it is important to...

Chypnosis: Stealthy Secret Extraction Using Undervolting-Based Static Side-Channel Attacks

There is a growing class of static physical side-channel attacks that allow adversaries to extract secrets by probing the persistent state of a circuit. Techniques such as laser logic state imaging LLSI, impedance analysis IA, and static power analysis fall into this category. These attacks requi...

SoK: Security of EMV Contactless Payment Systems

The widespread adoption of EMV Europay, Mastercard, and Visa contactless payment systems has greatly improved convenience for both users and merchants. However, this growth has also exposed significant security challenges. This SoK provides a comprehensive analysis of security vulnerabilities in...

GraphAttack: Exploiting Representational Blindspots in LLM Safety Mechanisms

Large Language Models LLMs have been equipped with safety mechanisms to prevent harmful outputs, but these guardrails can often be bypassed through "jailbreak" prompts. This paper introduces a novel graph-based approach to systematically generate jailbreak prompts through semantic transformations...

Leveraging Functional Encryption and Deep Learning for Privacy-Preserving Traffic Forecasting

Over the past few years, traffic congestion has continuously plagued the nation's transportation system creating several negative impacts including longer travel times, increased pollution rates, and higher collision risks. To overcome these challenges, Intelligent Transportation Systems ITS aim ...

GraphQLer: Enhancing GraphQL Security with Context-Aware API Testing

GraphQL is an open-source data query and manipulation language for web applications, offering a flexible alternative to RESTful APIs. However, its dynamic execution model and lack of built-in security mechanisms expose it to vulnerabilities such as unauthorized data access, denial-of-service DoS...

Omnissa Horizon Client for Windows Privilege Escalation

A local privilege escalation vulnerability was privately reported in Omnissa Horizon Client for Windows. Updates are available to remediate this vulnerability in impacted versions of the client. The fixed version is 2503...

Attack-Defense Trees with Offensive and Defensive Attributes (With Appendix)

Effective risk management in cybersecurity requires a thorough understanding of the interplay between attacker capabilities and defense strategies. Attack-Defense Trees ADTs are a commonly used methodology for representing this interplay; however, previous work in this domain has only focused on...

Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts

Ensuring the ethical deployment of text-to-image models requires effective techniques to prevent the generation of harmful or inappropriate content. While concept erasure methods offer a promising solution, existing finetuning-based approaches suffer from notable limitations. Anchor-free methods...

GNUnet P2P Framework 0.24.1

GNUnet is a peer-to-peer framework with focus on providing security. All peer-to-peer messages in the network are confidential and authenticated. The framework provides a transport abstraction layer and can currently encapsulate the network traffic in UDP IPv4 and IPv6, TCP IPv4 and IPv6, HTTP, o...

Malicious Code Detection in Smart Contracts Via Opcode Vectorization

With the booming development of blockchain technology, smart contracts have been widely used in finance, supply chain, Internet of things and other fields in recent years. However, the security problems of smart contracts become increasingly prominent. Security events caused by smart contracts...

Algorithms for the Shortest Vector Problem in $2$-Dimensional Lattices, Revisited

Whitepaper called Algorithms For The Shortest Vector Problem In $2$-Dimensional Lattices, Revisited...

Wireshark Analyzer 4.4.6

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...

Trusted Identities for AI Agents: Leveraging Telco-Hosted ESIM Infrastructure

The rise of autonomous AI agents in enterprise and industrial environments introduces a critical challenge: how to securely assign, verify, and manage their identities across distributed systems. Existing identity frameworks based on API keys, certificates, or application-layer credentials lack t...

Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification

Deep learning DL-based image classification models are essential for autonomous vehicle AV perception modules since incorrect categorization might have severe repercussions. Adversarial attacks are widely studied cyberattacks that can lead DL models to predict inaccurate output, such as incorrect...