Anticipating Decoder Side-Channel Attacks in Fault-Tolerant Quantum Computers

As quantum computing emerges as an applied technology, there is a growing need to protect quantum computers against information security attacks. This work identifies a new class of side-channel attacks against fault-tolerant quantum computers, in which the syndrome data that is sent to the decod...

Baselines Before Architecture: Evaluating Coding Agents for Autonomous Penetration Testing

Recent autonomous penetration testing papers report high benchmark scores while adding multi-component security harnesses around frontier LLMs. Because these systems often change both architecture and backbone model, it is difficult to tell how much performance comes from the harness rather than...

Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability

The Model Context Protocol MCP has rapidly established itself as a standard interface for enabling LLM-based agents to interact with external tools and services. As MCP servers are increasingly entrusted with security-sensitive operations, understanding their real-world risks has become critical...

SingGuard-NSFA: Extensible Guardrails for Agentic AI Via Generative Reasoning and Real-Time Classification

We present nsfaguard, a guardrail framework for securing agentic AI systems against operational threats, such as prompt injection, sensitive information extraction, malicious code requests, dangerous tool misuse, and resource exhaustion. We first introduce the NSFA taxonomy, which organizes 185...

MTD-Playground: An Attacker-Aware Evaluation Framework for Network Moving Target Defense

Moving Target Defense MTD has emerged as a proactive network cyber defense paradigm that increases attacker uncertainty through dynamic network reconfiguration techniques such as Software-Defined Networking SDN-enabled path randomization. However, existing evaluations remain fragmented due to...

IronCurtain 0.13.0

IronCurtain is an early-stage research project exploring how to make AI agents safe enough to be genuinely useful. It is a runtime for autonomous AI agents, where security policy is derived from a human-readable constitution. APIs, configuration formats, and architecture may change...

Explaining Intrusion Alert Decisions of Deep Learning-Based Network Intrusion Detection Systems for Security Analysts

In this paper, we present EXP-SEC, a novel framework which can explain the intrusion detection decisions of DL-based NIDS which lead to security alerts in a way that is aligned with the domain knowledge of analysts working in Security Operations Center SOC. We highlight the following features of...

GNSS Spoofing Detection in TDD Networks: A 3GPP Standards-Based Security Framework

Time Division Duplex TDD mobile networks require synchronization accuracy of $\pm$1.5 $μ$s 3GPP TS 38.104, with GNSS-disciplined grandmaster clocks as the predominant timing source. GNSS spoofing -- now a documented operational threat -- can corrupt timing across all downstream base stations, yet...

Prezta: Provable Remote Execution of Zero-Trust Authorization Using SNARKs

Modernizing the security of operational technology systems that control critical infrastructure has become a pressing challenge. Because edge devices have limited capabilities, modernization has relied on application gateways that interface with identity management systems and enforce access...

AMT-X: Phase-Structured Multi-Turn Red-Teaming with Checklist-Gated Evaluation

Safety evaluation of large language models LLMs relies largely on single-turn attack datasets and single-judge scoring, underestimating risk from adaptive multi-turn adversaries and reporting a single success rate that does not separate partially actionable outputs from those carrying complete...

Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming

Production LLM agents such as Claude Code and Codex operate over untrusted content, files, commands, and workspace state, making safety failures directly actionable. Red-teaming must therefore keep pace with evolving models and tools. Existing approaches mainly optimize attack success and preserv...

HarmQ: Harmonic Backdoor Attacks against Quantum Neural Networks

Quantum Neural Networks QNNs have emerged as a promising paradigm for quantum machine learning in the Noisy Intermediate-Scale Quantum NISQ era, leveraging quantum phenomena such as superposition and entanglement to process information in exponentially large Hilbert spaces. However, QNNs inherit...

Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud

Scientific fraud is the instrument of doubt that malicious entities can use to establish controversy in science. Historically, it required the resources of a company: deep pockets, ghostwritten articles, and corrupt academics. Today, Artificial Intelligence AI is increasingly automating scientifi...

SoK: Federated Learning for Intrusion Detection in Vehicular Networks

Modern vehicular networks face an expanding attack surface across internal Electronic Control Units ECUs and external Vehicle-to-Everything V2X communication. Federated Learning FL has emerged as a decentralized paradigm to deploy Intrusion Detection Systems IDS without compromising data privacy...

Quantum-Enhanced Physical-Layer Threat Detection in Metropolitan-Scale Fiber Networks

Network security is widely recognized as a key application of quantum technology. However, its large-scale deployment is hindered by the need for tight coordination between fundamentally different quantum and classical processing steps in conventional protocols. This requirement introduces strong...

ARMOR-IMC: Adaptive Resource Mapping for Operational Robustness Via Secure In-Memory Computing

The massive data-movement overhead in traditional architectures has led to the adoption of In-Memory Computing IMC for energy-efficient Deep Neural Network DNN processing. By leveraging emerging devices like Spin-Orbit Torque Magnetic Tunnel Junctions SOT-MTJs, IMC bypasses the "memory wall" and...

Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability

Large language models LLMs have transformed misinformation from a primarily content-centric problem into a broader ecosystem-level security challenge. When misused, LLMs create risks beyond false content generation, enabling attacks on the social contexts, evidence sources, retrieval corpora, and...

Mechanised Operational Semantics of Rowhammer

Rowhammer is a hardware vulnerability in dynamic random-access memory DRAM in which repeated accesses to aggressor rows can induce bit-flips in victim rows. This phenomenon violates a core assumption of conventional programming language semantics: reading or writing one memory location does not...

Firewall3D: A Hardware Firewall for Defending 3D Printers against Firmware Attacks

As the 3D printing market continues to grow rapidly, with an estimated value exceeding $30 billion, cybersecurity risks and attacks targeting additive manufacturing systems are also increasing. These attacks aim to sabotage printed components, steal intellectual property, or even physically damag...

Which Neurons Detect Malicious Code? A Probing Study of LLM Security Knowledge

Background. Large language models LLMs have become increasingly capable of understanding and generating source code, leading to their widespread adoption in software engineering tasks such as code completion, repair, and vulnerability detection. However, despite their strong empirical performance...

Threat Vectors and the State of the Art in Defense Methods for Security in Neurotechnology

Brain-computer interfaces BCIs are a class of diverse hardware modalities, associated software, and connected devices which are widely used in a variety of fields, including neurosurgery, biomedical data analysis, and neuroimaging. Recent years have seen rapid advancements in BCI technology, and...

A Knowledge-Based Multi-Agent Framework for Security Control Recommendation

Hardening IT on-premises environments can be a daunting task for teams without access to adequate cybersecurity expertise. In this regard, Decision Support Systems DSS with embedded expert knowledge can assist users by guiding them with security recommendations to meet their objectives. This work...

Leveraging Interpretable Tsetlin Machine for PDF Malware Detection

In the digital era, Portable Document Format PDF is one of the most widely used file formats for storing and exchanging digital documents due to its platform independence and rich functionality. However, these same capabilities have also made PDF files an attractive attack vector for...

Joern 4.0.579

Joern is the bug hunter's workbench. With this tool, you can uncover attack surface, sloppy coding practices, and variants of known vulnerabilities using an interactive code analysis shell. Joern supports C, C++, LLVM bitcode, x86 binaries via Ghidra, JVM bytecode via Soot, and Javascript...

Flounder 0.3.1

Flounder turns modern coding agents into an end-to-end security audit system. Give it an authorized target boundary - a repository, source tree, package, deployed clue, or prior run - and the agent can prepare the workspace, read the code and supporting material, map the attack surface, dig into...

Evaluating AI Models' Capability to Automate Voice Phishing Attacks

Voice phishing vishing attacks have traditionally been limited by the need for human operators. The rapid emergence of high-quality AI voice synthesis and large language models LLMs reduces this bottleneck and enables scalable, automated scams. In this paper, we conduct a large-scale survey...

VEXAIoT: Autonomous IoT Vulnerability EXploitation Using AI Agents

Internet of Things IoT systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches. While recent adoptions of Large Language Model LLM agents have demonstrated promise ...

SFDS: Selective File Disclosure System

Access control to networked resources has been a longstanding challenge. The conventional solution relies on authentication mechanisms, which introduce additional complexities associated with Identity and Access Management IAM. Such systems require user authentication, identity management, and...

SafeGuard: A Lightweight Client-Server Architecture for Real-Time Endpoint Threat Detection and Response

Endpoint devices remain a primary target for cyberattacks, yet commercial Endpoint Detection and Response EDR platforms are often too costly and operationally complex for small and resource-constrained organizations. This paper presents SafeGuard, a lightweight three-tier client-server architectu...

Xalgorix Autonomous AI Pentesting Agent 4.5.55

Most scanners detect. Xalgorix proves. An autonomous LLM agent works a full pentest methodology, then an independent verifier re-exploits every finding before it's reported - so you get proof, not a pile of maybes to triage. Self-hosted, private, and bring-your-own-LLM. Built in Go + TypeScript...

TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems

The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing interest in trustworthy metaverse analytics, existing datasets typically focus on user behavior, authentication, or...

Stegano 2.5.0

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...

Federated Learning Architecture: Data Privacy and System Security Approaches

This study explores the integration of homomorphic encryption and differential privacy techniques to enhance data privacy and security in Federated Learning FL systems. FL allows data to remain on local devices, eliminating the need for centralized data collection; however, sensitive information...

Statistically Undetectable Backdoors in Deep Neural Networks

We show how an adversarial model trainer can plant backdoors in a large class of deep, feedforward neural networks. These backdoors are statistically undetectable in the white-box setting, meaning that the backdoored and honestly trained models are close in total variation distance, even given th...

Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning

Recent LLM-based systems have shown promising capabilities for security-focused code analysis. Malware understanding, however, poses a distinct challenge: analysts must reconstruct high-level malicious behaviors under partial observability from sparse, dispersed evidence intertwined with benign...

T3MP3ST Multi-Agent Offensive Security Framework

T3MP3ST is a multi-agent offensive-security framework, built to turn the AI coding agent you already run into a zero-day hunter...

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks

Machine learning ML-based intrusion detection systems IDSs are increasingly used to monitor encrypted industrial communication. However, their behavior under realistic private 5G operating conditions remains insufficiently understood. This paper investigates the impact of benign connectivity...

Federated Cybersecurity Testbed As a Service (FCTaaS): A Framework to Federate Cybersecurity Testbeds

Rapid technological change is reshaping society through emerging domains such as autonomous vehicles and smart manufacturing, creating new research challenges in system design, operation, security, and training. Researchers often rely on testbeds to reproduce experimental scenarios, collect and...

Using LLMs to Adjudicate Static-Analysis Alerts with Error Reduction Techniques

Static analysis is widely used for finding security weaknesses in source code before deployment, but it often produces far more alerts than analysts can review. We study how well large language models LLMs can adjudicate classify as a real bug or a false alarm static-analysis alerts. We use two...

Entropy Bootstrapping for Wireless Embedded Systems

Weak randomness has broken deployed cryptography through implementation bugs, boot entropy scarcity, and backdoored generators. Inexpensive wireless sensors concentrate the risk because many boot or operate in highly deterministic conditions while relying on basic, rudimentary, or opaque RNGs. On...

Reverse Engineering Compliance: A Dual-Graph Verification Framework for Auditing Legacy IT Security Concepts

The NIS-2 Directive increases the need for continuous, auditable compliance evidence and motivates a shift from document-based compliance toward machine-readable compliance artifacts. The Open Security Controls Assessment Language OSCAL is a standard for this purpose, which the German Federal...

vulnx 2.0.2

vulnx is a command-line interface CLI tool designed to provide a structured and easily navigable interface to various vulnerability databases...

SeedSmith: LLM-Driven Seed Synthesis for Directed Fuzzing

Directed fuzzing steers fuzzers toward user-defined sink functions to identify vulnerabilities, but it frequently fails to trigger crashes even after long campaigns. We identify two challenges that prevent directed fuzzers from exposing crashes: incomplete static analysis of indirect calls, which...

Flounder 0.2.0

Flounder turns modern coding agents into an end-to-end security audit system. Give it an authorized target boundary - a repository, source tree, package, deployed clue, or prior run - and the agent can prepare the workspace, read the code and supporting material, map the attack surface, dig into...

Securing Autonomous Vehicle Systems Via Twin-Aware Federated Reinforcement Learning

Federated reinforcement learning FRL is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments. However, poisoning attacks pose a significant threat...

Wireshark Analyzer 4.6.7

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...

Neuro-Agentic Control: A Deep Learning-Based LLM-Powered Agentic AI Framework for Controlling Security Controls

Cyberattacks on operational technology are increasingly causing costly downtime and physical damage, exposing the limitations of traditional rule-based monitoring in industrial IoT environments. While Large Language Models LLMs have strong semantic reasoning abilities to assist in decision suppor...

WPProbe Plugin Enumeration Tool 0.12.5

A fast WordPress plugin and theme scanner that detects installed plugins via REST API enumeration and themes from HTML discovery, then maps them to known vulnerabilities. Over 5,000 plugins detectable without brute-force, thousands more with it...

Prismata: Confining Cross-Site Prompt Injection in Web Agents

Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web's oldest attack surfaces. Cross-Site Scripting proved that mixing trusted and untrusted content is dangerous, even on benign pages. Agents resurface this risk by interpreting natural language as...

Secret Scanner Agent: Extracting Secrets and Access Context from Unstructured Documents

Exposed documents such as emails, chat threads, tickets, and incident notes routinely leak credentials, but during incident response a leaked secret is only half the story. Responders also need to identify the "door'' the secret opens: the account, tenant, endpoint, database, cloud resource, or...