Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy

Large Language Models LLMs have gained significant popularity due to their remarkable capabilities in text understanding and generation. However, despite their widespread deployment in inference services such as ChatGPT, concerns about the potential leakage of sensitive user data have arisen...

Implementation of Shor Algorithm: Factoring a 4096-Bit Integer under Specific Constraints

In recent years, advancements in quantum chip technology, such as Willow, have contributed to reducing quantum computation error rates, potentially accelerating the practical adoption of quantum computing. As a result, the design of quantum algorithms suitable for real-world applications has beco...

DataSentinel: a Game-Theoretic Detection of Prompt Injection Attacks

LLM-integrated applications and agents are vulnerable to prompt injection attacks, where an attacker injects prompts into their inputs to induce attacker-desired outputs. A detection method aims to determine whether a given input is contaminated by an injected prompt. However, existing detection...

WordPress Digits OTP Authentication Bypass

WordPress Digits plugin versions prior to 8.4.6.1 suffer from an OTP authentication bypass vulnerability...

Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data

The integration of large language models LLMs into cyber security applications presents significant opportunities, such as enhancing threat analysis and malware detection, but can also introduce critical risks and safety concerns, including personal data leakage and automated generation of new...

RAN Tester UE: an Automated Declarative UE Centric Security Testing Platform

Cellular networks require strict security procedures and measures across various network components, from core to radio access network RAN and end-user devices. As networks become increasingly complex and interconnected, as in O-RAN deployments, they are exposed to a numerous security threats...

Sybil-Based Virtual Data Poisoning Attacks in Federated Learning

Federated learning is vulnerable to poisoning attacks by malicious adversaries. Existing methods often involve high costs to achieve effective attacks. To address this challenge, we propose a sybil-based virtual data poisoning attack, where a malicious client generates sybil nodes to amplify the...

ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems

Foreign information operations conducted by Russian and Chinese actors exploit the United States' permissive information environment. These campaigns threaten democratic institutions and the broader Westphalian model. Yet, existing detection and mitigation strategies often fail to identify active...

Automating Security Audit Using Large Language Model Based Agent: an Exploration Experiment

In the current rapidly changing digital environment, businesses are under constant stress to ensure that their systems are secured. Security audits help to maintain a strong security posture by ensuring that policies are in place, controls are implemented, gaps are identified for cybersecurity...

A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network

Connected and Autonomous Vehicles CAVs enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network CAN bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robus...

AutoPentest: Enhancing Vulnerability Management with Autonomous LLM Agents

A recent area of increasing research is the use of Large Language Models LLMs in penetration testing, which promises to reduce costs and thus allow for higher frequency. We conduct a review of related work, identifying best practices and common evaluation issues. We then present AutoPentest, an...

GenAI Security: Outsmarting the Bots with a Proactive Testing Framework

The increasing sophistication and integration of Generative AI GenAI models into diverse applications introduce new security challenges that traditional methods struggle to address. This research explores the critical need for proactive security measures to mitigate the risks associated with...

Instantiating Standards: Enabling Standard-Driven Text TTP Extraction with Evolvable Memory

Extracting MITRE ATT&CK Tactics, Techniques, and Procedures TTPs from natural language threat reports is crucial yet challenging. Existing methods primarily focus on performance metrics using data-driven approaches, often neglecting mechanisms to ensure faithful adherence to the official standard...

Cybersecurity Threat Detection Based on a UEBA Framework Using Deep Autoencoders

User and Entity Behaviour Analytics UEBA is a broad branch of data analytics that attempts to build a normal behavioural profile in order to detect anomalous events. Among the techniques used to detect anomalies, Deep Autoencoders constitute one of the most promising deep learning models on UEBA...

Privacy-Preserving Runtime Verification

Runtime verification offers scalable solutions to improve the safety and reliability of systems. However, systems that require verification or monitoring by a third party to ensure compliance with a specification might contain sensitive information, causing privacy concerns when usual runtime...

DNS Query Forgery: a Client-Side Defense against Mobile App Traffic Profiling

Mobile applications continuously generate DNS queries that can reveal sensitive user behavioral patterns even when communications are encrypted. This paper presents a privacy enhancement framework based on query forgery to protect users against profiling attempts that leverage these background...

CANTXSec: a Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations

Despite being a legacy protocol with various known security issues, Controller Area Network CAN still represents the de-facto standard for communications within vehicles, ships, and industrial control systems. Many research works have designed Intrusion Detection Systems IDSs to identify attacks ...

Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification

The widespread use of smartphones in daily life has raised concerns about privacy and security among researchers and practitioners. Privacy issues are generally highly prevalent in mobile applications, particularly targeting the Android platform, the most popular mobile operating system. For this...

Adversarial Suffix Filtering: a Defense Pipeline for LLMs

Large Language Models LLMs are increasingly embedded in autonomous systems and public-facing environments, yet they remain susceptible to jailbreak vulnerabilities that may undermine their security and trustworthiness. Adversarial suffixes are considered to be the current state-of-the-art...

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data

Federated learning FL presents an effective solution for collaborative model training while maintaining data privacy across decentralized client datasets. However, data quality issues such as noisy labels, missing classes, and imbalanced distributions significantly challenge its effectiveness. Th...

Guardian Positioning System (GPS) for Location Based Services

Location-based service LBS applications proliferate and support transportation, entertainment, and more. Modern mobile platforms, with smartphones being a prominent example, rely on terrestrial and satellite infrastructures e.g., global navigation satellite system GNSS and crowdsourced Wi-Fi,...

Automated Alert Classification and Triage (AACT): an Intelligent System for the Prioritisation of Cybersecurity Alerts

Enterprise networks are growing ever larger with a rapidly expanding attack surface, increasing the volume of security alerts generated from security controls. Security Operations Centre SOC analysts triage these alerts to identify malicious activity, but they struggle with alert fatigue due to t...

Correlating Account on Ethereum Mixing Service Via Domain-Invariant Feature Learning

The untraceability of transactions facilitated by Ethereum mixing services like Tornado Cash poses significant challenges to blockchain security and financial regulation. Existing methods for correlating mixing accounts suffer from limited labeled data and vulnerability to noisy annotations, whic...

Security and Privacy Measurement on Chinese Consumer IoT Traffic Based on Device Lifecycle

In recent years, consumer Internet of Things IoT devices have become widely used in daily life. With the popularity of devices, related security and privacy risks arise at the same time as they collect user-related data and transmit it to various service providers. Although China accounts for a...

Compact Lattice-Coded (Multi-Recipient) Kyber without CLT Independence Assumption

Whitepaper called Compact Lattice-Coded Multi-Recipient Kyber Without CLT Independence Assumption...

WhatsAI: Transforming Meta Ray-Bans into an Extensible Generative AI Platform for Accessibility

Multi-modal generative AI models integrated into wearable devices have shown significant promise in enhancing the accessibility of visual information for blind or visually impaired BVI individuals, as evidenced by the rapid uptake of Meta Ray-Bans among BVI users. However, the proprietary nature ...

Securing P4 Programs by Information Flow Control

Software-Defined Networking SDN has transformed network architectures by decoupling the control and data-planes, enabling fine-grained control over packet processing and forwarding. P4, a language designed for programming data-plane devices, allows developers to define custom packet processing...

GNU Privacy Guard 2.4.8

GnuPG the GNU Privacy Guard or GPG is GNU's tool for secure communication and data storage. It can be used to encrypt data and to create digital signatures. It includes an advanced key management facility and is compliant with the proposed OpenPGP Internet standard as described in RFC2440. As suc...

Optimizing DDoS Detection in SDNs through Machine Learning Models

The emergence of Software-Defined Networking SDN has changed the network structure by separating the control plane from the data plane. However, this innovation has also increased susceptibility to DDoS attacks. Existing detection techniques are often ineffective due to data imbalance and accurac...

Detecting Sybil Addresses in Blockchain Airdrops: a Subgraph-Based Feature Propagation and Fusion Approach

Sybil attacks pose a significant security threat to blockchain ecosystems, particularly in token airdrop events. This paper proposes a novel sybil address identification method based on subgraph feature extraction lightGBM. The method first constructs a two-layer deep transaction subgraph for eac...

Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration

Malicious Unmanned Aerial Vehicles UAVs present a significant threat to next-generation networks NGNs, posing risks such as unauthorized surveillance, data theft, and the delivery of hazardous materials. This paper proposes an integrated AE-classifier system to detect malicious UAVs. The proposed...

Triple-Identity Authentication: the Future of Secure Access

In a typical authentication process, the local system verifies the user's identity using a stored hash value generated by a cross-system hash algorithm. This article shifts the research focus from traditional password encryption to the establishment of gatekeeping mechanisms for effective...

Adversarial Attack on Large Language Models Using Exponentiated Gradient Descent

As Large Language Models LLMs are widely used, understanding them systematically is key to improving their safety and realizing their full potential. Although many models are aligned using techniques such as reinforcement learning from human feedback RLHF, they are still vulnerable to jailbreakin...

Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems

Machine learning is a key tool for Android malware detection, effectively identifying malicious patterns in apps. However, ML-based detectors are vulnerable to evasion attacks, where small, crafted changes bypass detection. Despite progress in adversarial defenses, the lack of comprehensive...

GPML: Graph Processing for Machine Learning

The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML Graph Processing for Machine Learning library addresses this need by transforming raw network traffic traces into graph representations, enabling...

Inference Attacks for X-Vector Speaker Anonymization

We revisit the privacy-utility tradeoff of x-vector speaker anonymization. Existing approaches quantify privacy through training complex speaker verification or identification models that are later used as attacks. Instead, we propose a novel inference attack for de-anonymization. Our attack is...

SAFE-SiP: Secure Authentication Framework for System-In-Package Using Multi-Party Computation

The emergence of chiplet-based heterogeneous integration is transforming the semiconductor, AI, and high-performance computing industries by enabling modular designs and improved scalability. However, assembling chiplets from multiple vendors after fabrication introduces a complex supply chain th...

Multiparty Selective Disclosure Using Attribute-Based Encryption

This study proposes a mechanism for encrypting SD-JWT Selective Disclosure JSON Web Token Disclosures using Attribute-Based Encryption ABE to enable flexible access control on the basis of the Verifier's attributes. By integrating Ciphertext-Policy ABE CP-ABE into the existing SD-JWT framework, t...

Unencrypted Flying Objects: Security Lessons from University Small Satellite Developers and Their Code

Satellites face a multitude of security risks that set them apart from hardware on Earth. Small satellites may face additional challenges, as they are often developed on a budget and by amateur organizations or universities that do not consider security. We explore the security practices and...

Modeling Interdependent Cybersecurity Threats Using Bayesian Networks: a Case Study on In-Vehicle Infotainment Systems

Cybersecurity threats are increasingly marked by interdependence, uncertainty, and evolving complexity challenges that traditional assessment methods such as CVSS, STRIDE, and attack trees fail to adequately capture. This paper reviews the application of Bayesian Networks BNs in cybersecurity ris...

Area Comparison of CHERIoT and PMP in Ibex

Memory safety is a critical concern for modern embedded systems, particularly in security-sensitive applications. This paper explores the area impact of adding memory safety extensions to the Ibex RISC-V core, focusing on physical memory protection PMP and Capability Hardware Extension to RISC-V...

ROSA: Finding Backdoors with Fuzzing

A code-level backdoor is a hidden access, programmed and concealed within the code of a program. For instance, hard-coded credentials planted in the code of a file server application would enable maliciously logging into all deployed instances of this application. Confirmed software supply chain...

MUBox: a Critical Evaluation Framework of Deep Machine Unlearning

Recent legal frameworks have mandated the right to be forgotten, obligating the removal of specific data upon user requests. Machine Unlearning has emerged as a promising solution by selectively removing learned information from machine learning models. This paper presents MUBox, a comprehensive...

Information Leakage in Data Linkage

The process of linking databases that contain sensitive information about individuals across organisations is an increasingly common requirement in the health and social science research domains, as well as with governments and businesses. To protect personal data, protocols have been developed t...

Cryptologic Techniques and Associated Risks in Public and Private Security. an Italian and European Union Perspective with an Overview of the Current Legal Framework

This article examines the evolution of cryptologic techniques and their implications for public and private security, focusing on the Italian and EU legal frameworks. It explores the roles of cryptography, steganography, and quantum technologies in countering cybersecurity threats, emphasising th...

Blockchain Technology: Core Mechanisms, Evolution, and Future Implementation Challenges

Blockchain technology has emerged as one of the most transformative digital innovations of the 21st century. This paper presents a comprehensive review of blockchain's fundamental architecture, tracing its development from Bitcoin's initial implementation to current enterprise applications. We...

Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning

The security of cloud environments, such as Amazon Web Services AWS, is complex and dynamic. Static security policies have become inadequate as threats evolve and cloud resources exhibit elasticity 1. This paper addresses the limitations of static policies by proposing a security policy managemen...

LibVulnWatch: a Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries

Open-source AI libraries are foundational to modern AI systems but pose significant, underexamined risks across security, licensing, maintenance, supply chain integrity, and regulatory compliance. We present LibVulnWatch, a graph-based agentic assessment framework that performs deep,...

On the Interplay of Explainability, Privacy and Predictive Performance with Explanation-Assisted Model Extraction

Machine Learning as a Service MLaaS has gained important attraction as a means for deploying powerful predictive models, offering ease of use that enables organizations to leverage advanced analytics without substantial investments in specialized infrastructure or expertise. However, MLaaS...

Removing Watermarks with Partial Regeneration Using Semantic Information

As AI-generated imagery becomes ubiquitous, invisible watermarks have emerged as a primary line of defense for copyright and provenance. The newest watermarking schemes embed semantic signals - content-aware patterns that are designed to survive common image manipulations - yet their true...