Optimal Allocation of Privacy Budget on Hierarchical Data Release

Releasing useful information from datasets with hierarchical structures while preserving individual privacy presents a significant challenge. Standard privacy-preserving mechanisms, and in particular Differential Privacy, often require careful allocation of a finite privacy budget across differen...

Server-Side Template Injection Vulnerabilities and Exploitation Techniques

Research article called Server-Side Template Injection SSTI Vulnerabilities and Exploitation Techniques. The paper provides a structured methodology for detecting and exploiting SSTI vulnerabilities across multiple template engines, along with real-world case studies and mitigation strategies...

Understanding and Characterizing Obfuscated Funds Transfers in Ethereum Smart Contracts

Scam contracts on Ethereum have rapidly evolved alongside the rise of DeFi and NFT ecosystems, utilizing increasingly complex code obfuscation techniques to avoid early detection. This paper systematically investigates how obfuscation amplifies the financial risks of fraudulent contracts and...

From Trade-Off to Synergy: a Versatile Symbiotic Watermarking Framework for Large Language Models

The rise of Large Language Models LLMs has heightened concerns about the misuse of AI-generated text, making watermarking a promising solution. Mainstream watermarking schemes for LLMs fall into two categories: logits-based and sampling-based. However, current schemes entail trade-offs among...

S3C2 Summit 2024-09: Industry Secure Software Supply Chain Summit

While providing economic and software development value, software supply chains are only as strong as their weakest link. Over the past several years, there has been an exponential increase in cyberattacks, specifically targeting vulnerable links in critical software supply chains. These attacks...

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...

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...

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...

Neural-Inspired Advances in Integral Cryptanalysis

The study by Gohr et.al at CRYPTO 2019 and sunsequent related works have shown that neural networks can uncover previously unused features, offering novel insights into cryptanalysis. Motivated by these findings, we employ neural networks to learn features specifically related to integral...

Dark LLMs: the Growing Threat of Unaligned AI Models

Large Language Models LLMs rapidly reshape modern life, advancing fields from healthcare to education and beyond. However, alongside their remarkable capabilities lies a significant threat: the susceptibility of these models to jailbreaking. The fundamental vulnerability of LLMs to jailbreak...

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...

On Technique Identification and Threat-Actor Attribution Using LLMs and Embedding Models

Attribution of cyber-attacks remains a complex but critical challenge for cyber defenders. Currently, manual extraction of behavioral indicators from dense forensic documentation causes significant attribution delays, especially following major incidents at the international scale. This research...

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...

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...

Managerial Insights on Investment Strategy in Cybersecurity: Findings from Multi-Country Research

This study examines the strategic role of cybersecurity based on survey data from 1,083 managers across Europe, the UK, and the United States. The findings indicate growing recognition of cybersecurity as a source of competitive advantage, although firms continue to face barriers such as limited...

Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data

Due to the rapid growth in the number of Internet of Things IoT networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. A high rate of missed threats can be the result, as traditional machine learnin...

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...

Cutting through Privacy: a Hyperplane-Based Data Reconstruction Attack in Federated Learning

Federated Learning FL enables collaborative training of machine learning models across distributed clients without sharing raw data, ostensibly preserving data privacy. Nevertheless, recent studies have revealed critical vulnerabilities in FL, showing that a malicious central server can manipulat...

One for All: Formally Verifying Protocols Which Use Aggregate Signatures (Extended Version)

Aggregate signatures are digital signatures that compress multiple signatures from different parties into a single signature, thereby reducing storage and bandwidth requirements. BLS aggregate signatures are a popular kind of aggregate signature, deployed by Ethereum, Dfinity, and Cloudflare...

SecReEvalBench: a Multi-Turned Security Resilience Evaluation Benchmark for Large Language Models

The increasing deployment of large language models in security-sensitive domains necessitates rigorous evaluation of their resilience against adversarial prompt-based attacks. While previous benchmarks have focused on security evaluations with limited and predefined attack domains, such as...

Enhancing Secrecy Energy Efficiency in RIS-Aided Aerial Mobile Edge Computing Networks: a Deep Reinforcement Learning Approach

This paper studies the problem of securing task offloading transmissions from ground users against ground eavesdropping threats. Our study introduces a reconfigurable intelligent surface RIS-aided unmanned aerial vehicle UAV-mobile edge computing MEC scheme to enhance the secure task offloading...

Private Transformer Inference in MLaaS: a Survey

Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service MLaaS raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private...

Agent Name Service (ANS): a Universal Directory for Secure AI Agent Discovery and Interoperability

The proliferation of AI agents requires robust mechanisms for secure discovery. This paper introduces the Agent Name Service ANS, a novel architecture based on DNS addressing the lack of a public agent discovery framework. ANS provides a protocol-agnostic registry infrastructure that leverages...

WordPress Digits OTP Authentication Bypass

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

Quantized Approximate Signal Processing (QASP): Towards Homomorphic Encryption for Audio

Audio and speech data are increasingly used in machine learning applications such as speech recognition, speaker identification, and mental health monitoring. However, the passive collection of this data by audio listening devices raises significant privacy concerns. Fully homomorphic encryption...

AC-LoRA: (Almost) Training-Free Access Control-Aware Multi-Modal LLMs

Corporate LLMs are gaining traction for efficient knowledge dissemination and management within organizations. However, as current LLMs are vulnerable to leaking sensitive information, it has proven difficult to apply them in settings where strict access control is necessary. To this end, we desi...

The Tangent Space Attack

We propose a new method for retrieving the algebraic structure of a generic alternant code given an arbitrary generator matrix, provided certain conditions are met. We then discuss how this challenges the security of the McEliece cryptosystem instantiated with this family of codes. The central...

Random Client Selection on Contrastive Federated Learning for Tabular Data

Vertical Federated Learning VFL has revolutionised collaborative machine learning by enabling privacy-preserving model training across multiple parties. However, it remains vulnerable to information leakage during intermediate computation sharing. While Contrastive Federated Learning CFL was...

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...

Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning

Federated learning FL enhances privacy and reduces communication cost for resource-constrained edge clients by supporting distributed model training at the edge. However, the heterogeneous nature of such devices produces diverse, non-independent, and identically distributed non-IID data, making t...

The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems Powered by Deep Learning

We study the security of stock price forecasting using Deep Learning DL in computational finance. Despite abundant prior research on the vulnerability of DL to adversarial perturbations, such work has hitherto hardly addressed practical adversarial threat models in the context of DL-powered...

SafeTrans: LLM-Assisted Transpilation from C to Rust

Rust is a strong contender for a memory-safe alternative to C as a "systems" programming language, but porting the vast amount of existing C code to Rust is a daunting task. In this paper, we evaluate the potential of large language models LLMs to automate the transpilation of C code to idiomatic...

AttentionGuard: Transformer-Based Misbehavior Detection for Secure Vehicular Platoons

Vehicle platooning, with vehicles traveling in close formation coordinated through Vehicle-to-Everything V2X communications, offers significant benefits in fuel efficiency and road utilization. However, it is vulnerable to sophisticated falsification attacks by authenticated insiders that can...

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...

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...

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...

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...

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...

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...

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,...

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...

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...

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...

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...

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

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

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...

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...

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...