Antiproof: Synthesizing Vulnerability Detectors and Proofs of Exploitability

Discovering vulnerabilities before attackers exploit them requires high recall and reliable automatic validation, but existing approaches struggle to achieve both without prohibitive cost. We present Antiproof, an end-to-end vulnerability discovery system that combines neuro-symbolic detector...

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...

GDM AI Control Roadmap

AI agents are rapidly accelerating work at frontier AI companies, helping with AI R&D, cyber-defence, and advancing scientific discoveries. As these agents become more tightly integrated into our systems, unlocking their full potential requires rethinking how we do security. We should not assume...

Noise Resilience of Quantum Key Distribution Protocols Secured against Independent Attacks with One-Way Communication

We investigate the resilience to noise of single-qubit quantum key distribution QKD protocols in the scenario of security against independent eavesdropping attacks and key distillation based on one-way classical communication. To this end, we introduce a noise-based metric that quantifies the...

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...

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...

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...

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...

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...

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...

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...

LifeOS 7.1.1

LifeOS is a Life Operating System. It knows your goals, the people who matter to you, and where you are right now, and it works to move you toward where you want to be. The engine underneath is a verifiable loop: turn any request into testable criteria, then climb until they pass. This is the fir...

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...

Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study

AI code assistants are transforming software development, but their implications for software security remain a major concern, particularly in the context of security APIs. These APIs are critical for safeguarding software systems, yet their complexity often leads to incorrect use and serious...

TestSSL 3.2.4

testssl.sh is a free command line tool which checks a server's service on any port for the support of TLS/SSL ciphers, protocols as well as recent cryptographic flaws, and much more. It is written in pure bash, makes only use of standard Unix utilities, openssl and last but not least bash sockets...

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...

MindReader: Using LLMs to Encourage Memorable and Secure Password Replacement

We report on the design and evaluation of MindReader, a tool that helps a user replace her password when she is required to do so. Left to their own devices, users tend to replace their previous passwords with predictable variations of the original ones. MindReader leverages LLMs to suggest...

Mako: A Self-Evolving Agentic Operating System (SE-AOS) for Autonomous Web Exploitation

We introduce the Self-Evolving Agentic Operating System SE-AOS: a new class of AI agent that treats exploit capability as a mutable, versioned kernel it extends at runtime, observing its own failures, synthesising new capabilities, proving them against a live target, and hot-loading them back int...

Securing LLMs in the Wild: Privacy and Security Challenges at the Edge

Large Language Models LLMs are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edge platforms. While cloud deployments offer scalable compute, concerns over data sovereignty, compliance, latency, and third-party dependence are...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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 ...

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...

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...

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...

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...