7945 matches found
Comparative Evaluation of VAE, GAN, and SMOTE for Tor Detection in Encrypted Network Traffic
Encrypted network traffic poses significant challenges for intrusion detection due to the lack of payload visibility, limited labeled datasets, and high class imbalance between benign and malicious activities. Traditional data augmentation methods struggle to preserve the complex temporal and...
Evil-M5Project WiFi Exploration Tool
This is the latest archive as of 2025/01/02 of the Evil-M5Project, an innovative tool developed for ethical testing and exploration of WiFi networks. It harnesses the power of the M5Core2 device to scan, monitor, and interact with WiFi networks in a controlled environment. This project is designe...
Application-Specific Power Side-Channel Attacks and Countermeasures: A Survey
Side-channel attacks try to extract secret information from a system by analyzing different side-channel signatures, such as power consumption, electromagnetic emanation, thermal dissipation, acoustics, time, etc. Power-based side-channel attack is one of the most prominent side-channel attacks i...
When RSA Fails: Exploiting Prime Selection Vulnerabilities in Public Key Cryptography
This paper explores vulnerabilities in RSA cryptosystems that arise from improper prime number selection during key generation. We examine two primary attack vectors: Fermat's factorization method, which exploits RSA keys generated with primes that are too close together, and the Greatest Common...
SemCovert: Secure and Covert Video Transmission Via Deep Semantic-Level Hiding
Video semantic communication, praised for its transmission efficiency, still faces critical challenges related to privacy leakage. Traditional security techniques like steganography and encryption are challenging to apply since they are not inherently robust against semantic-level transformations...
Diverse LLMs Vs. Vulnerabilities: Who Detects and Fixes Them Better?
Large Language Models LLMs are increasingly being studied for Software Vulnerability Detection SVD and Repair SVR. Individual LLMs have demonstrated code understanding abilities, but they frequently struggle when identifying complex vulnerabilities and generating fixes. This study presents...
Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features
Phishing is a cybercrime in which individuals are deceived into revealing personal information, often resulting in financial loss. These attacks commonly occur through fraudulent messages, misleading advertisements, and compromised legitimate websites. This study proposes a Quantile Regression De...
OpenSCAP Libraries 1.4.3
The openscap project is a set of open source libraries that support the SCAP Security Content Automation Protocol set of standards from NIST. It supports CPE, CCE, CVE, CVSS, OVAL, and XCCDF...
Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway
As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an edge-centric Intrusion Detection System IDS framework that integrate...
AutoGraphAD: A Novel Approach Using Variational Graph Autoencoders for Anomalous Network Flow Detection
Network Intrusion Detection Systems NIDS are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very...
RampoNN: A Reachability-Guided System Falsification for Efficient Cyber-Kinetic Vulnerability Detection
Detecting kinetic vulnerabilities in Cyber-Physical Systems CPS, vulnerabilities in control code that can precipitate hazardous physical consequences, is a critical challenge. This task is complicated by the need to analyze the intricate coupling between complex software behavior and the system's...
Trustworthy GenAI over 6G: Integrated Applications and Security Frameworks
The integration of generative artificial intelligence GenAI into 6G networks promises substantial performance gains while simultaneously exposing novel security vulnerabilities rooted in multimodal data processing and autonomous reasoning. This article presents a unified perspective on cross-doma...
Security and Privacy Management of IoT Using Quantum Computing
The convergence of the Internet of Things IoT and quantum computing is redefining the security paradigm of interconnected digital systems. Classical cryptographic algorithms such as RSA, Elliptic Curve Cryptography ECC, and Advanced Encryption Standard AES have long provided the foundation for...
HAMLOCK: HArdware-Model LOgically Combined AttacK
The growing use of third-party hardware accelerators e.g., FPGAs, ASICs for deep neural networks DNNs introduces new security vulnerabilities. Conventional model-level backdoor attacks, which only poison a model's weights to misclassify inputs with a specific trigger, are often detectable because...
HarmNet: A Framework for Adaptive Multi-Turn Jailbreak Attacks on Large Language Models
Large Language Models LLMs remain vulnerable to multi-turn jailbreak attacks. We introduce HarmNet, a modular framework comprising ThoughtNet, a hierarchical semantic network; a feedback-driven Simulator for iterative query refinement; and a Network Traverser for real-time adaptive attack...
Active Honeypot Guardrail System: Probing and Confirming Multi-Turn LLM Jailbreaks
Large language models LLMs are increasingly vulnerable to multi-turn jailbreak attacks, where adversaries iteratively elicit harmful behaviors that bypass single-turn safety filters. Existing defenses predominantly rely on passive rejection, which either fails against adaptive attackers or overly...
WireTap: Breaking Server SGX via DRAM Bus Interposition
Whitepaper that delves into Intel’s Software Guard eXtension SGX. A common misconception is that physical attacks on SGX require expensive laboratory equipment, thus putting them out of reach of hobbyist-level attackers. In this work, the authors challenge this belief, showing how simple memory b...
RedCodeAgent: Automatic Red-Teaming Agent against Diverse Code Agents
Code agents have gained widespread adoption due to their strong code generation capabilities and integration with code interpreters, enabling dynamic execution, debugging, and interactive programming capabilities. While these advancements have streamlined complex workflows, they have also...
FreeBSD Security Advisory - FreeBSD-SA-25:08.openssl
FreeBSD Security Advisory - FreeBSD includes software from the OpenSSL Project. OpenSSL suffers from some new vulnerabilities. An application trying to decrypt cryptographic message syntax CMS messages encrypted using password based encryption can trigger an out-of-bounds read and write. A timing...
FuncPoison: Poisoning Function Library to Hijack Multi-Agent Autonomous Driving Systems
Autonomous driving systems increasingly rely on multi-agent architectures powered by large language models LLMs, where specialized agents collaborate to perceive, reason, and plan. A key component of these systems is the shared function library, a collection of software tools that agents use to...
Digital Sovereignty Control Framework for Military AI-Based Cyber Security
In today's evolving threat landscape, ensuring digital sovereignty has become mandatory for military organizations, especially given their increased development and investment in AI-driven cyber security solutions. To this end, a multi-angled framework is proposed in this article in order to defi...
Systematic Timing Leakage Analysis of NIST PQDSS Candidates: Tooling and Lessons Learned
The PQDSS standardization process requires cryptographic primitives to be free from vulnerabilities, including timing and cache side-channels. Resistance to timing leakage is therefore an essential property, and achieving this typically relies on software implementations that follow constant-time...
Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model
This paper forecasts future Distributed Denial of Service DDoS attacks using deep learning models. Although several studies address forecasting DDoS attacks, they remain relatively limited compared to detection-focused research. By studying the current trends and forecasting based on newer and...
When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs
As large language models become increasingly integrated into daily life, audio has emerged as a key interface for human-AI interaction. However, this convenience also introduces new vulnerabilities, making audio a potential attack surface for adversaries. Our research introduces WhisperInject, a...
Prompt Injection 2.0: Hybrid AI Threats
Prompt injection attacks, where malicious input is designed to manipulate AI systems into ignoring their original instructions and following unauthorized commands instead, were first discovered by Preamble, Inc. in May 2022 and responsibly disclosed to OpenAI. Over the last three years, these...
AI Agent Smart Contract Exploit Generation
We present A1, an agentic execution driven system that transforms any LLM into an end-to-end exploit generator. A1 has no hand-crafted heuristics and provides the agent with six domain-specific tools that enable autonomous vulnerability discovery. The agent can flexibly leverage these tools to...
FuncVul: an Effective Function Level Vulnerability Detection Model Using LLM and Code Chunk
Software supply chain vulnerabilities arise when attackers exploit weaknesses by injecting vulnerable code into widely used packages or libraries within software repositories. While most existing approaches focus on identifying vulnerable packages or libraries, they often overlook the specific...
QGuard:Question-Based Zero-Shot Guard for Multi-Modal LLM Safety
The recent advancements in Large Language ModelsLLMs have had a significant impact on a wide range of fields, from general domains to specialized areas. However, these advancements have also significantly increased the potential for malicious users to exploit harmful and jailbreak prompts for...
SoK: the Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
Large language models LLMs are sophisticated artificial intelligence systems that enable machines to generate human-like text with remarkable precision. While LLMs offer significant technological progress, their development using vast amounts of user data scraped from the web and collected from...
LLM vs. SAST: a Technical Analysis on Detecting Coding Bugs of GPT4-Advanced Data Analysis
With the rapid advancements in Natural Language Processing NLP, large language models LLMs like GPT-4 have gained significant traction in diverse applications, including security vulnerability scanning. This paper investigates the efficacy of GPT-4 in identifying software vulnerabilities compared...
Custom API Generator for Cross Platform and Import Export in WP 2.0.3 Privilege Escalation
WordPress REST API | Custom API Generator For Cross Platform And Import Export In WP plugin versions 1.0.0 through 2.0.3 are susceptible to a privilege escalation vulnerability due to a missing capability check on the processhandler...
ReGA: Representation-Guided Abstraction for Model-Based Safeguarding of LLMs
Large Language Models LLMs have achieved significant success in various tasks, yet concerns about their safety and security have emerged. In particular, they pose risks in generating harmful content and vulnerability to jailbreaking attacks. To analyze and monitor machine learning models,...
RADEP: a Resilient Adaptive Defense Framework against Model Extraction Attacks
Machine Learning as a Service MLaaS enables users to leverage powerful machine learning models through cloud-based APIs, offering scalability and ease of deployment. However, these services are vulnerable to model extraction attacks, where adversaries repeatedly query the application programming...
The Hidden Dangers of Browsing AI Agents
Autonomous browsing agents powered by large language models LLMs are increasingly used to automate web-based tasks. However, their reliance on dynamic content, tool execution, and user-provided data exposes them to a broad attack surface. This paper presents a comprehensive security evaluation of...
HChain: Blockchain Based Large Scale EHR Data Sharing with Enhanced Security and Privacy
Concerns regarding privacy and data security in conventional healthcare prompted alternative technologies. In smart healthcare, blockchain technology addresses existing concerns with security, privacy, and electronic healthcare transmission. Integration of Blockchain Technology with the Internet ...
MalVis: a Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification
As technology advances, Android malware continues to pose significant threats to devices and sensitive data. The open-source nature of the Android OS and the availability of its SDK contribute to this rapid growth. Traditional malware detection techniques, such as signature-based, static, and...
WordPress Digits OTP Authentication Bypass
WordPress Digits plugin versions prior to 8.4.6.1 suffer from an OTP authentication bypass vulnerability...
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...
AI-Driven IRM: Transforming Insider Risk Management with Adaptive Scoring and LLM-Based Threat Detection
Insider threats pose a significant challenge to organizational security, often evading traditional rule-based detection systems due to their subtlety and contextual nature. This paper presents an AI-powered Insider Risk Management IRM system that integrates behavioral analytics, dynamic risk...
LASHED: LLMs and Static Hardware Analysis for Early Detection of RTL Bugs
While static analysis is useful in detecting early-stage hardware security bugs, its efficacy is limited because it requires information to form checks and is often unable to explain the security impact of a detected vulnerability. Large Language Models can be useful in filling these gaps by...
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems
Digital twins DTs help improve real-time monitoring and decision-making in water distribution systems. However, their connectivity makes them easy targets for cyberattacks such as scanning, denial-of-service DoS, and unauthorized access. Small and medium-sized enterprises SMEs that manage these...
Fast and Robust Speckle Pattern Authentication by Scale Invariant Feature Transform Algorithm in Physical Unclonable Functions
Nowadays, due to the growing phenomenon of forgery in many fields, the interest in developing new anti-counterfeiting device and cryptography keys, based on the Physical Unclonable Functions PUFs paradigm, is widely increased. PUFs are physical hardware with an intrinsic, irreproducible disorder...
Bandit on the Hunt: Dynamic Crawling for Cyber Threat Intelligence
Public information contains valuable Cyber Threat Intelligence CTI that is used to prevent future attacks. While standards exist for sharing this information, much appears in non-standardized news articles or blogs. Monitoring online sources for threats is time-consuming and source selection is...
Private Federated Learning Using Preference-Optimized Synthetic Data
In practical settings, differentially private Federated learning DP-FL is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperformed by methods that use DP synthetic data Wu et al., 2024; Hou et al., 2024. The...
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...
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...
WordPress Newscrunch Theme 1.8.4.1 Shell Upload
WordPress Newscrunch theme version 1.8.4.1 suffers from a remote shell upload vulnerability...
Linux 4.2 Out-Of-Bounds Write
The USB CDC-ACM driver in Linux versions starting at 4.12 suffers from a missing size check in acmctrlirq that leads to an out-of-bounds write...