Adversarial Prompting Framework for AI Safety Assessment

Artificial Intelligence AI, especially Generative AI GenAI, adoption has increased in industries significantly in recent years. However, the use of these models may also expose systems to new forms of cyberattacks by different malicious actors -- adversarial prompt attack APA being one of the mos...

Security Evaluation of Quantum Distance-Bounding Protocols Via Semidefinite Programming

Quantum distance-bounding QDB protocols let a verifier check that a prover is both genuine and physically nearby. During a timed fast phase of quantum communication, the verifier measures round-trip times to obtain an upper bound on the prover's distance. For a uniform comparison, we isolate the...

Towards Quantum Machine Learning for Assessing the Resilience of Post-Quantum Cryptography

The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adversaries with access to large fault-tolerant quantum computers. However, even though current quantum computers are limited in terms of size and...

Assessing the Forensic Viability of Android Memory Analysis across Production Builds: A Cross-Version Study of Security Hardening and Structure Preservation

Android memory forensics recovers evidence that never touches disk: decrypted messages, session credentials, and the live internal state of a running application. The tools that perform this recovery depend on debug symbols embedded in libart.so, the Android Runtime library, to locate data...

DREA: Decoupled Reasoning and Exploration Agents for Repository-Level Vulnerability Detection

Large language models LLMs are increasingly applied to vulnerability detection due to their strong code comprehension capabilities, but most existing approaches rely on isolated functions or context extracted by fixed program-analysis rules. These methods cannot adaptively explore repository-leve...

The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK

AI coding agents produce code faster than humans can review it. In our approach, the prover is the judge of whether the code is correct. Under a verifier-driven loop, AI agents wrote and verified bare-metal security software in Ada/SPARK spanning classical and post-quantum cryptography, TLS 1.3,...

Rethinking Penetration Testing for AI-Enabled Systems: From Resource Compromise to Behavioral Objective Violation

Penetration testing traditionally evaluates whether adversaries can exploit weaknesses in software, infrastructure, configurations, or operational controls to achieve security-relevant compromise. This paradigm remains necessary for AI-enabled systems, but it is no longer sufficient. In such...

Xalgorix Autonomous AI Pentesting Agent 4.5.60

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...

Semgrep Rules 2.0.0 Static Analysis Rule Collection

A collection of Semgrep rules for identifying potentially vulnerable code patterns during static source code analysis. The rule set focuses on vulnerability research across multiple languages and includes checks for common security issues to assist developers, auditors, and security researchers...

Reveal, Correct, Then Pay: Encrypted Mempools and Perpetual Funding Security

Encrypted mempools are designed to hide transaction contents until execution order is fixed, preventing many victim dependent forms of maximal extractable value. This paper studies a different class of attack in the form of self-authored state manipulation, in which the attacker knows its own...

Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation

Reusable skills are becoming a fundamental building block of Large Language Model LLM agents, enabling capabilities to be packaged, shared, and reused across diverse applications. However, existing security research primarily focuses on prompt injection and runtime execution, leaving security ris...

A SecOps Practitioner's View of Project Glasswing and the Halcyon Imperative

This paper is written from the ground level, from the perspective of someone running a Security Operations Center SOC and an Attack Surface Management program day to day. It is not a strategic overview for the boardroom. It is a practitioner's assessment of what Glasswing changes functionally, wh...

BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection

False alarms remain a major barrier to deploying network intrusion detection systems NIDS. In high-volume environments, even a sub-1% false positive rate can generate tens of thousands of daily alerts. Filter-based feature selection is attractive because it operates upstream of the classifier and...

AI in Cyberpsychology: A Systematic Literature Review of Cybersecurity Enhancement by Using AI for Analyzing Psychology of Victims, Attackers, and Defenders

Cybersecurity is the practice of protecting systems, networks, and data from digital attacks. Cyberpsychology CPSY is defined as the use of psychology to enhance cybersecurity applications. Since the early 2010s, the evolution of Artificial Intelligence AI has increasingly integrated with CPSY,...

Evaluating Frontier AI Agents As Autonomous Clinical Security Auditors

Clinical AI models can expose patients to harm when adversarial vulnerabilities go undetected, yet formal security auditing requires statistical expertise, specialized tools, and significant time. We present an open evaluation task, built on METR Task Standard v0.3.0, that tests whether frontier ...

On the Security Implications of PQC in TLS: Handshake Exhaustion and IDS Degradation

Post-Quantum Cryptography PQC is increasingly being integrated into TLS 1.3 to enhance resilience against quantum-enabled attacks. However, the additional computational and communication overhead introduced by PQC primitives during the handshake phase may also amplify the impact of TLS handshake...

When Binaries Talk Back: Representation-Confusion Attacks on LLM-Assisted Reverse Engineering

LLM-assisted reverse-engineering RE systems analyze strings, decompiler output, and tool reports derived from ttacker-controlled binaries. A binary can make data look like instructions or records from one origin look like independent evidence. We call such failures Representation-Confusion Attack...

Designing a GDPR-Compliant Security Architecture for Remote Elderly Care Systems: A Privacy-By-Design Approach

IoMT-based remote elderly care systems generate continuous streams of sensitive health data, yet existing security architectures have not simultaneously addressed three interdependent challenges: GDPR-compliant edge-layer pseudonymisation, elderly-specific zero-interaction usability as a binding...

Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agents

The increasing adoption of autonomous coding agents accelerates software development but also introduces scoped security risks within high-impact file paths that can outpace traditional human review capacity. While prior research has primarily evaluated these systems in terms of functional...

Joern 4.0.580

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...

Why Not Fix It Once and for All? an Empirical Study of Multiple Patches for Vulnerability Fixes in Open-Source Software

Security patches for open-source software constitute a foundational resource for vulnerability remediation research and practice. However, analyzing and applying multiple patches remains challenging, especially when trying to determine at what point in a patch sequence a vulnerability is fully...

Breaking Refusal in the First Half: A Mechanistic Study of the Prefill Jailbreak

Aligned language models refuse harmful requests, but a one-line prefill "Sure, here is" strips the refusal. We ask where and how it fails. The harm representation stays intact: on the prompts the attack flips to compliance, a linear probe reads harm as high as on the refused ones 0.91-0.98, while...

Xalgorix Autonomous AI Pentesting Agent 4.5.59

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...

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...

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...

When Cheap Gradients Fail: The Measurement Cost of Attacking Quantum Classifiers

Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement statistics shot noise act as a built-in defense against gradient-based test-time attacks whose cost scales unfavorably for the attacker. Because ever...

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...

Nebula AI-Powered Penetration Testing 2.0.0

Nebula is an advanced, AI-powered penetration testing open-source tool that revolutionizes penetration testing by integrating state-of-the-art AI models into your command-line interface. Designed for cybersecurity professionals, ethical hackers, and developers, Nebula automates vulnerability...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...