A Study on Audio Synchronous Steganography Detection and Distributed Guide Inference Model Based on Sliding Spectral Features and Intelligent Inference Drive

With the rise of short video platforms in global communication, embedding steganographic data in audio synchronization streams has emerged as a new covert communication method. To address the limitations of traditional techniques in detecting synchronized steganography, this paper proposes a...

Differential Privacy for Network Assortativity

The analysis of network assortativity is of great importance for understanding the structural characteristics of and dynamics upon networks. Often, network assortativity is quantified using the assortativity coefficient that is defined based on the Pearson correlation coefficient between vertex...

BadLingual: a Novel Lingual-Backdoor Attack against Large Language Models

In this paper, we present a new form of backdoor attack against Large Language Models LLMs: lingual-backdoor attacks. The key novelty of lingual-backdoor attacks is that the language itself serves as the trigger to hijack the infected LLMs to generate inflammatory speech. They enable the precise...

SKALD: Scalable K-Anonymisation for Large Datasets

Data privacy and anonymisation are critical concerns in today's data-driven society, particularly when handling personal and sensitive user data. Regulatory frameworks worldwide recommend privacy-preserving protocols such as k-anonymisation to de-identify releases of tabular data. Available...

CB-CPIR: Code-Based Computational Private Information Retrieval

A private information retrieval PIR scheme is a protocol that allows a user to retrieve a file from a database without revealing the identity of the desired file to a curious database. Given a distributed data storage system, efficient PIR can be achieved by making assumptions about the colluding...

The Steganographic Potentials of Language Models

The potential for large language models LLMs to hide messages within plain text steganography poses a challenge to detection and thwarting of unaligned AI agents, and undermines faithfulness of LLMs reasoning. We explore the steganographic capabilities of LLMs fine-tuned via reinforcement learnin...

AI-Driven Security in Cloud Computing: Enhancing Threat Detection, Automated Response, and Cyber Resilience

Cloud security concerns have been greatly realized in recent years due to the increase of complicated threats in the computing world. Many traditional solutions do not work well in real-time to detect or prevent more complex threats. Artificial intelligence is today regarded as a revolution in...

MergeGuard: Efficient Thwarting of Trojan Attacks in Machine Learning Models

This paper proposes MergeGuard, a novel methodology for mitigation of AI Trojan attacks. Trojan attacks on AI models cause inputs embedded with triggers to be misclassified to an adversary's target class, posing a significant threat to model usability trained by an untrusted third party. The core...

Rollbaccine : Herd Immunity against Storage Rollback Attacks in TEEs [Technical Report]

Today, users can "lift-and-shift" unmodified applications into modern, VM-based Trusted Execution Environments TEEs in order to gain hardware-based security guarantees. However, TEEs do not protect applications against disk rollback attacks, where persistent storage can be reverted to an earlier...

Elevating Cyber Threat Intelligence against Disinformation Campaigns with LLM-Based Concept Extraction and the FakeCTI Dataset

The swift spread of fake news and disinformation campaigns poses a significant threat to public trust, political stability, and cybersecurity. Traditional Cyber Threat Intelligence CTI approaches, which rely on low-level indicators such as domain names and social media handles, are easily evaded ...

Directed Greybox Fuzzing Via Large Language Model

Directed greybox fuzzing DGF focuses on efficiently reaching specific program locations or triggering particular behaviors, making it essential for tasks like vulnerability detection and crash reproduction. However, existing methods often suffer from path explosion and randomness in input mutatio...

Empc: Effective Path Prioritization for Symbolic Execution with Path Cover

Symbolic execution is a powerful program analysis technique that can formally reason the correctness of program behaviors and detect software bugs. It can systematically explore the execution paths of the tested program. But it suffers from an inherent limitation: path explosion. Path explosion...

Mitigating Backdoor Triggered and Targeted Data Poisoning Attacks in Voice Authentication Systems

Voice authentication systems remain susceptible to two major threats: backdoor triggered attacks and targeted data poisoning attacks. This dual vulnerability is critical because conventional solutions typically address each threat type separately, leaving systems exposed to adversaries who can...

Bridging Expertise Gaps: the Role of LLMs in Human-AI Collaboration for Cybersecurity

This study investigates whether large language models LLMs can function as intelligent collaborators to bridge expertise gaps in cybersecurity decision-making. We examine two representative tasks-phishing email detection and intrusion detection-that differ in data modality, cognitive complexity,...

Detecting Quishing Attacks with Machine Learning Techniques through QR Code Analysis

The rise of QR code based phishing "Quishing" poses a growing cybersecurity threat, as attackers increasingly exploit QR codes to bypass traditional phishing defenses. Existing detection methods predominantly focus on URL analysis, which requires the extraction of the QR code payload, and may...

WordPress Flynax Bridge 2.2.0 Privilege Escalation

WordPress Flynax Bridge plugin versions 2.2.0 and below suffer from an unauthenticated privilege escalation vulnerability...

Implementation and Security Analysis of Cryptocurrencies Based on Ethereum

Blockchain technology has set off a wave of decentralization in the world since its birth. The trust system constructed by blockchain technology based on cryptography algorithm and computing power provides a practical and powerful solution to solve the trust problem in human society. In order to...

CodeBC: a More Secure Large Language Model for Smart Contract Code Generation in Blockchain

Large language models LLMs excel at generating code from natural language instructions, yet they often lack an understanding of security vulnerabilities. This limitation makes it difficult for LLMs to avoid security risks in generated code, particularly in high-security programming tasks such as...

A Chaos Driven Metric for Backdoor Attack Detection

The advancement and adoption of Artificial Intelligence AI models across diverse domains have transformed the way we interact with technology. However, it is essential to recognize that while AI models have introduced remarkable advancements, they also present inherent challenges such as their...

Publicly Verifiable Secret Sharing: Generic Constructions and Lattice-Based Instantiations in the Standard Model

Publicly verifiable secret sharing PVSS allows a dealer to share a secret among a set of shareholders so that the secret can be reconstructed later from any set of qualified participants. In addition, any public verifier should be able to check the correctness of the sharing and reconstruction...

LlamaFirewall: an Open Source Guardrail System for Building Secure AI Agents

Large language models LLMs have evolved from simple chatbots into autonomous agents capable of performing complex tasks such as editing production code, orchestrating workflows, and taking higher-stakes actions based on untrusted inputs like webpages and emails. These capabilities introduce new...

LLMs' Suitability for Network Security: a Case Study of STRIDE Threat Modeling

Artificial Intelligence AI is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are almost nonexistent studies that analyze the suitability of Large Language...

SolPhishHunter: Towards Detecting and Understanding Phishing on Solana

Solana is a rapidly evolving blockchain platform that has attracted an increasing number of users. However, this growth has also drawn the attention of malicious actors, with some phishers extending their reach into the Solana ecosystem. Unlike platforms such as Ethereum, Solana has distinct...

Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: a Scoping Review

Explainable Artificial Intelligence XAI has emerged as a pillar of Trustworthy AI and aims to bring transparency in complex models that are opaque by nature. Despite the benefits of incorporating explanations in models, an urgent need is found in addressing the privacy concerns of providing this...

Stegano 1.0.1

Stegano is a basic Python Steganography module. Stegano implements two methods of hiding: using the red portion of a pixel to hide ASCII messages, and using the Least Significant Bit LSB technique. It is possible to use a more advanced LSB method based on integers sets. The sets Sieve of...

An Efficient Hybrid Key Exchange Mechanism

Whitepaper called An Efficient Hybrid Key Exchange Mechanism...

Towards Dataset Copyright Evasion Attack against Personalized Text-To-Image Diffusion Models

Text-to-image T2I diffusion models have rapidly advanced, enabling high-quality image generation conditioned on textual prompts. However, the growing trend of fine-tuning pre-trained models for personalization raises serious concerns about unauthorized dataset usage. To combat this, dataset...

Antifragility of RIS-Assisted Communication Systems under Jamming Attacks

Antifragility of communication systems is defined as measure of benefits gained from the adverse events and variability of its environment. In this paper, we introduce the notion of antifragility in Reconfigurable Intelligent Surface RIS assisted communication systems affected by a jamming attack...

Targeted Fuzzing for Unsafe Rust Code: Leveraging Selective Instrumentation

Rust is a promising programming language that focuses on concurrency, usability, and security. It is used in production code by major industry players and got recommended by government bodies. Rust provides strong security guarantees achieved by design utilizing the concepts of ownership and...

Attestable Builds: Compiling Verifiable Binaries on Untrusted Systems Using Trusted Execution Environments

In this paper we present attestable builds, a new paradigm to provide strong source-to-binary correspondence in software artifacts. We tackle the challenge of opaque build pipelines that disconnect the trust between source code, which can be understood and audited, and the final binary artifact,...

SoK: Stealing Cars since Remote Keyless Entry Introduction and How to Defend from It

Remote Keyless Entry RKE systems have been the target of thieves since their introduction in automotive industry. Robberies targeting vehicles and their remote entry systems are booming again without a significant advancement from the industrial sector being able to protect against them...

A Slicing-Based Approach for Detecting and Patching Vulnerable Code Clones

Code cloning is a common practice in software development, but it poses significant security risks by propagating vulnerabilities across cloned segments. To address this challenge, we introduce srcVul, a scalable, precise detection approach that combines program slicing with Locality-Sensitive...

Impact Analysis of Inference Time Attack of Perception Sensors on Autonomous Vehicles

As a safety-critical cyber-physical system, cybersecurity and related safety issues for Autonomous Vehicles AVs have been important research topics for a while. Among all the modules on AVs, perception is one of the most accessible attack surfaces, as drivers and AVs have no control over the...

RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents

Uncontrollable autonomous replication of language model agents poses a critical safety risk. To better understand this risk, we introduce RepliBench, a suite of evaluations designed to measure autonomous replication capabilities. RepliBench is derived from a decomposition of these capabilities...

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models

Email spam detection is a critical task in modern communication systems, essential for maintaining productivity, security, and user experience. Traditional machine learning and deep learning approaches, while effective in static settings, face significant limitations in adapting to evolving spam...

Towards a Standardized Methodology and Dataset for Evaluating LLM-Based Digital Forensic Timeline Analysis

Large language models LLMs have seen widespread adoption in many domains including digital forensics. While prior research has largely centered on case studies and examples demonstrating how LLMs can assist forensic investigations, deeper explorations remain limited, i.e., a standardized approach...

Balancing Security and Liquidity: a Time-Weighted Snapshot Framework for DAO Governance Voting

As new project upgrading the blockchain industry, novel forms of attack challenges developers to rethink about the design of their innovations. In the growth stage of the development, Decentralized Autonomous Organizations DAO introduces different approaches in managing fund through voting in...

Unveiling the Landscape of LLM Deployment in the Wild: an Empirical Study

Background: Large language models LLMs are increasingly deployed via open-source and commercial frameworks, enabling individuals and organizations to self-host advanced AI capabilities. However, insecure defaults and misconfigurations often expose LLM services to the public Internet, posing...

Towards Effective Identification of Attack Techniques in Cyber Threat Intelligence Reports Using Large Language Models

This work evaluates the performance of Cyber Threat Intelligence CTI extraction methods in identifying attack techniques from threat reports available on the web using the MITRE ATT&CK framework. We analyse four configurations utilising state-of-the-art tools, including the Threat Report ATT&CK...

Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems

Machine learning ML-based intrusion detection systems IDS are vulnerable to adversarial attacks. It is crucial for an IDS to learn to recognize adversarial examples before malicious entities exploit them. In this paper, we generated adversarial samples using the Jacobian Saliency Map Attack JSMA...

Encrypted Federated Search Using Homomorphic Encryption

The sharing of information between agencies is effective in dealing with cross-jurisdictional criminal activities; however, such sharing is often restricted due to concerns about data privacy, ownership, and compliance. Towards this end, this work has introduced a privacy-preserving federated...

Firefox XSLTProcessor JavaScript Use-After-Free

Firefox suffers from a use-after-free vulnerability when running JavaScript during XSLT transform. Included is a proof of concept that triggers a use-after-free by deleting nodes from the source document during transform, while those nodes are referenced in Xpath node list. Various other proof of...

Dynamic Graph-Based Fingerprinting of In-Browser Cryptomining

The decentralized and unregulated nature of cryptocurrencies, combined with their monetary value, has made them a vehicle for various illicit activities. One such activity is cryptojacking, an attack that uses stolen computing resources to mine cryptocurrencies without consent for profit...

Acoustic Side-Channel Attacks on a Computer Mouse

Acoustic Side-Channel Attacks ASCAs extract sensitive information by using audio emitted from a computing devices and their peripherals. Attacks targeting keyboards are popular and have been explored in the literature. However, similar attacks targeting other human interface peripherals, such as...

Post-Quantum Cryptography: an Analysis of Code-Based and Lattice-Based Cryptosystems

Most modern cryptographic systems, such as RSA and the Diffie-Hellman Key Exchange, rely on "trapdoor" mathematical functions that are presumed to be computationally difficult with existing tools. However, quantum computers will be able to break these systems using Shor's Algorithm, necessitating...

Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of...

Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents

Whitepaper called Open Challenges In Multi-Agent Security: Towards Secure Systems Of Interacting AI Agents...

A Comprehensive Analysis of Adversarial Attacks against Spam Filters

Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates t...

Risk Assessment and Threat Modeling for Safe Autonomous Driving Technology

This research paper delves into the field of autonomous vehicle technology, examining the vulnerabilities inherent in each component of these transformative vehicles. Autonomous vehicles AVs are revolutionizing transportation by seamlessly integrating advanced functionalities such as sensing,...

Backdoor Attacks against Patch-Based Mixture of Experts

As Deep Neural Networks DNNs continue to require larger amounts of data and computational power, Mixture of Experts MoE models have become a popular choice to reduce computational complexity. This popularity increases the importance of considering the security of MoE architectures. Unfortunately,...