Integrated Simulation Framework for Adversarial Attacks on Autonomous Vehicles

Autonomous vehicles AVs rely on complex perception and communication systems, making them vulnerable to adversarial attacks that can compromise safety. While simulation offers a scalable and safe environment for robustness testing, existing frameworks typically lack comprehensive supportfor...

VULSOVER: Vulnerability Detection Via LLM-Driven Constraint Solving

Traditional vulnerability detection methods rely heavily on predefined rule matching, which often fails to capture vulnerabilities accurately. With the rise of large language models LLMs, leveraging their ability to understand code semantics has emerged as a promising direction for achieving more...

Virtual Reality, Real Problems: a Longitudinal Security Analysis of VR Firmware

Virtual Reality VR technology is rapidly growing in recent years. VR devices such as Meta Quest 3 utilize numerous sensors to collect users' data to provide an immersive experience. Due to the extensive data collection and the immersive nature, the security of VR devices is paramount. Leading VR...

Human-Written Vs. AI-Generated Code: a Large-Scale Study of Defects, Vulnerabilities, and Complexity

As AI code assistants become increasingly integrated into software development workflows, understanding how their code compares to human-written programs is critical for ensuring reliability, maintainability, and security. In this paper, we present a large-scale comparison of code authored by hum...

Risks and Compliance with the EU'S Core Cyber Security Legislation

The European Union EU has long favored a risk-based approach to regulation. Such an approach is also used in recent cyber security legislation enacted in the EU. Risks are also inherently related to compliance with the new legislation. Objective: The paper investigates how risks are framed in the...

Detecting Stealthy Data Poisoning Attacks in AI Code Generators

Deep learning DL models for natural language-to-code generation have become integral to modern software development pipelines. However, their heavy reliance on large amounts of data, often collected from unsanitized online sources, exposes them to data poisoning attacks, where adversaries inject...

Agentic Discovery and Validation of Android App Vulnerabilities

Existing Android vulnerability detection tools overwhelm teams with thousands of low-signal warnings yet uncover few true positives. Analysts spend days triaging these results, creating a bottleneck in the security pipeline. Meanwhile, genuinely exploitable vulnerabilities often slip through,...

Evope 1.1.3.20 Hardcoded Cryptographic Key

The component Evope Core in Evope version 1.1.3.20 uses a hardcoded cryptographic key, which means that encryption/decryption keys are permanently embedded in the source code, rather than being securely managed. This creates a critical security flaw because anyone who gains access to or...

Cybersecurity AI: Hacking the AI Hackers Via Prompt Injection

We demonstrate how AI-powered cybersecurity tools can be turned against themselves through prompt injection attacks. Prompt injection is reminiscent of cross-site scripting XSS: malicious text is hidden within seemingly trusted content, and when the system processes it, that text is transformed...

Hybrid Cryptographic Monitoring System for Side-Channel Attack Detection on PYNQ SoCs

AES-128 encryption is theoretically secure but vulnerable in practical deployments due to timing and fault injection attacks on embedded systems. This work presents a lightweight dual-detection framework combining statistical thresholding and machine learning ML for real-time anomaly detection. B...

Wireshark Analyzer 4.4.9

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...

An Empirical Study of Vulnerable Package Dependencies in LLM Repositories

Large language models LLMs have developed rapidly in recent years, revolutionizing various fields. Despite their widespread success, LLMs heavily rely on external code dependencies from package management systems, creating a complex and interconnected LLM dependency supply chain. Vulnerabilities ...

The WASM Cloak: Evaluating Browser Fingerprinting Defenses under WebAssembly Based Obfuscation

Browser fingerprinting defenses have historically focused on detecting JavaScriptJS-based tracking techniques. However, the widespread adoption of WebAssembly WASM introduces a potential blind spot, as adversaries can convert JS to WASM's low-level binary format to obfuscate malicious logic. This...

Microarchitecture Design and Benchmarking of Custom SHA-3 Instruction for RISC-V

Integrating cryptographic accelerators into modern CPU architectures presents unique microarchitectural challenges, particularly when extending instruction sets with complex and multistage operations. Hardware-assisted cryptographic instructions, such as Intel's AES-NI and ARM's custom instructio...

AegisShield: Democratizing Cyber Threat Modeling with Generative AI

The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with limited resources. This paper develops and evaluates AegisShield, a generative AI enhanced threat modeling tool that implements STRIDE and MITRE ATT&CK to...

CyberSleuth: Autonomous Blue-Team LLM Agent for Web Attack Forensics

Large Language Model LLM agents are powerful tools for automating complex tasks. In cybersecurity, researchers have primarily explored their use in red-team operations such as vulnerability discovery and penetration tests. Defensive uses for incident response and forensics have received...

Ransomware 3.0: Self-Composing and LLM-Orchestrated

Using automated reasoning, code synthesis, and contextual decision-making, we introduce a new threat that exploits large language models LLMs to autonomously plan, adapt, and execute the ransomware attack lifecycle. Ransomware 3.0 represents the first threat model and research prototype of...

Measuring Ransomware Lateral Movement Susceptibility Via Privilege-Weighted Adjacency Matrix Exponentiation

Ransomware impact hinges on how easily an intruder can move laterally and spread to the maximum number of assets. We present a graph-theoretic method to measure lateral-movement susceptibility and estimate blast radius. We build a directed multigraph where vertices represent assets and edges...

Kemp Loadmaster Command Injection

This Python script is designed as a proof of concept PoC to demonstrate an unauthenticated command injection vulnerability in Kemp LoadMaster, identified as CVE-2024-1212...

Multi-Agent Penetration Testing AI for the Web

AI-powered development platforms are making software creation accessible to a broader audience, but this democratization has triggered a scalability crisis in security auditing. With studies showing that up to 40% of AI-generated code contains vulnerabilities, the pace of development now vastly...

Locus: Agentic Predicate Synthesis for Directed Fuzzing

Directed fuzzing aims to find program inputs that lead to specified target program states. It has broad applications, such as debugging system crashes, confirming reported bugs, and generating exploits for potential vulnerabilities. This task is inherently challenging because target states are...

Best Salon Management System 1.0 Cross Site Scripting

Best Salon Management System version 1.0 suffers from a persistent cross site scripting vulnerability...

Human-AI Collaborative Bot Detection in MMORPGs

In Massively Multiplayer Online Role-Playing Games MMORPGs, auto-leveling bots exploit automated programs to level up characters at scale, undermining gameplay balance and fairness. Detecting such bots is challenging, not only because they mimic human behavior, but also because punitive actions...

PromptSleuth: Detecting Prompt Injection Via Semantic Intent Invariance

Large Language Models LLMs are increasingly integrated into real-world applications, from virtual assistants to autonomous agents. However, their flexibility also introduces new attack vectors-particularly Prompt Injection PI, where adversaries manipulate model behavior through crafted inputs. As...

AI Agentic Vulnerability Injection and Transformation with Optimized Reasoning

The increasing complexity of software systems and the sophistication of cyber-attacks have underscored the critical need for effective automated vulnerability detection and repair systems. Traditional methods, such as static program analysis, face significant challenges related to scalability,...

Secure Satellite Communications Via Multiple Aerial RISs: Joint Optimization of Reflection, Association, and Deployment

Satellite communication is envisioned as a key enabler of future 6G networks, yet its wide coverage with high link attenuation poses significant challenges for physical layer security. In this paper, we investigate secure multi-beam, multi-group satellite communications assisted by aerial...

WordPress RingCentral Communications 1.6.8 Authentication Bypass

WordPress RingCentral Communications plugin versions 1.5 through 1.6.8 have a missing server-side verification that allows for authentication bypass...

The Art of Hide and Seek: Making Pickle-Based Model Supply Chain Poisoning Stealthy Again

Pickle deserialization vulnerabilities have persisted throughout Python's history, remaining widely recognized yet unresolved. Due to its ability to transparently save and restore complex objects into byte streams, many AI/ML frameworks continue to adopt pickle as the model serialization protocol...

FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture

Applying deep learning to malware detection has drawn great attention due to its notable performance. With the increasing prevalence of cyberattacks targeting IoT devices, there is a parallel rise in the development of malware across various Instruction Set Architectures ISAs. It is thus importan...

Faraday 5.16.0

Faraday is a tool that introduces a new concept called IPE, or Integrated Penetration-Test Environment. It is a multiuser penetration test IDE designed for distribution, indexation and analysis of the generated data during the process of a security audit. The main purpose of Faraday is to re-use...

Zeek 8.0.1

Zeek is a powerful network analysis framework that is much different from the typical IDS you may know. While focusing on network security monitoring, Zeek provides a comprehensive platform for more general network traffic analysis as well. Well grounded in more than 15 years of research, Zeek ha...

Addressing Deepfake Issue in Selfie Banking through Camera Based Authentication

Fake images in selfie banking are increasingly becoming a threat. Previously, it was just Photoshop, but now deep learning technologies enable us to create highly realistic fake identities, which fraudsters exploit to bypass biometric systems such as facial recognition in online banking. This pap...

The Influence of Code Comments on the Perceived Helpfulness of Stack Overflow Posts

Question-and-answer platforms such as Stack Overflow have become an important way for software developers to share and retrieve knowledge. However, reusing poorly understood code can lead to serious problems, such as bugs or security vulnerabilities. To better understand how code comments affect...

Surveying the Operational Cybersecurity and Supply Chain Threat Landscape When Developing and Deploying AI Systems

The rise of AI has transformed the software and hardware landscape, enabling powerful capabilities through specialized infrastructures, large-scale data storage, and advanced hardware. However, these innovations introduce unique attack surfaces and objectives which traditional cybersecurity...

Every Keystroke You Make: a Tech-Law Measurement and Analysis of Event Listeners for Wiretapping

The privacy community has a long track record of investigating emerging types of web tracking techniques. Recent work has focused on compliance of web trackers with new privacy laws such as Europe's GDPR and California's CCPA. Despite the growing body of research documenting widespread lack of...

A Technical Review on Comparison and Estimation of Steganographic Tools

Steganography is technique of hiding a data under cover media using different steganography tools. Image steganography is hiding of data Text/Image/Audio/Video under a cover as Image. This review paper presents classification of image steganography and the comparison of various Image steganograph...

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift

Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation...

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation

Signature-based Intrusion Detection Systems IDS detect malicious activities by matching network or host activity against predefined rules. These rules are derived from extensive Cyber Threat Intelligence CTI, which includes attack signatures and behavioral patterns obtained through automated tool...

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection

The Internet of Things IoT, with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion detection systems IDS have been extensively studied to enhance IoT security, and machine learning-based IDS ML-IDS show...

Servant, Stalker, Predator: How an Honest, Helpful, and Harmless (3H) Agent Unlocks Adversarial Skills

This paper identifies and analyzes a novel vulnerability class in Model Context Protocol MCP based agent systems. The attack chain describes and demonstrates how benign, individually authorized tasks can be orchestrated to produce harmful emergent behaviors. Through systematic analysis using the...

Towards Quantum Machine Learning for Malicious Code Analysis

Classical machine learning CML has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning QML presents a paradigm-shifting opportunity to improve malware detection, though its application in this domain remains largely unexplored. In...

Formal Verification of Physical Layer Security Protocols for Next-Generation Communication Networks

Formal verification is crucial for ensuring the robustness of security protocols against adversarial attacks. The Needham-Schroeder protocol, a foundational authentication mechanism, has been extensively studied, including its integration with Physical Layer Security PLS techniques such as...

SIExVulTS: Sensitive Information Exposure Vulnerability Detection System Using Transformer Models and Static Analysis

Sensitive Information Exposure SIEx vulnerabilities CWE-200 remain a persistent and under-addressed threat across software systems, often leading to serious security breaches. Existing detection tools rarely target the diverse subcategories of CWE-200 or provide context-aware analysis of code-lev...

Attackers Strike Back? Not Anymore -- an Ensemble of RL Defenders Awakens for APT Detection

Advanced Persistent Threats APTs represent a growing menace to modern digital infrastructure. Unlike traditional cyberattacks, APTs are stealthy, adaptive, and long-lasting, often bypassing signature-based detection systems. This paper introduces a novel framework for APT detection that unites de...

Addressing Weak Authentication like RFID, NFC in EVs and EVCs Using AI-Powered Adaptive Authentication

The rapid expansion of the Electric Vehicles EVs and Electric Vehicle Charging Systems EVCs has introduced new cybersecurity challenges, specifically in authentication protocols that protect vehicles, users, and energy infrastructure. Although widely adopted for convenience, traditional...

LLMs in the SOC: an Empirical Study of Human-AI Collaboration in Security Operations Centres

The integration of Large Language Models LLMs into Security Operations Centres SOCs presents a transformative, yet still evolving, opportunity to reduce analyst workload through human-AI collaboration. However, their real-world application in SOCs remains underexplored. To address this gap, we...

Aligning Core Aspects: Improving Vulnerability Proof-Of-Concepts Via Cross-Source Insights

For vulnerabilities, Proof-of-Concept PoC plays an irreplaceable role in demonstrating the exploitability. PoC reports may include critical information such as specific usage, test platforms, and more, providing essential insights for researchers. However, in reality, due to various PoC templates...

A.S.E: a Repository-Level Benchmark for Evaluating Security in AI-Generated Code

The increasing adoption of large language models LLMs in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks are inadequate, as they focus on isolated code snippets, employ unstable evaluation methods that lack reproducibility, and...

Prompt-In-Content Attacks: Exploiting Uploaded Inputs to Hijack LLM Behavior

Large Language Models LLMs are widely deployed in applications that accept user-submitted content, such as uploaded documents or pasted text, for tasks like summarization and question answering. In this paper, we identify a new class of attacks, prompt in content injection, where adversarial...

Hashcat Advanced Password Recovery 7.1.2 Source Code

Hashcat is an advanced GPU hash cracking utility that includes the World's fastest md5crypt, phpass, mscash2 and WPA / WPA2 cracker. It also has the first and only GPGPU-based rule engine, focuses on highly iterated modern hashes, single dictionary-based attacks, and more. This is the source code...