Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses under White-Box and Black-Box Threats

Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, the adversarial robustness of drift-adaptive detectors, remains unexplored. We address this problem with AdvDA, a rece...

Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environments

This study introduces a hybrid deep learning model for intrusion detection in Industrial IoT IIoT systems, combining ResNet-1D, BiGRU, and Multi-Head Attention MHA for effective spatial-temporal feature extraction and attention-based feature weighting. To address class imbalance, SMOTE was applie...

Windows Service for User (S4U) Scheduled Task Persistence Logon Trigger

This Metasploit module creates a scheduled task that will run using service-for-user S4U. This allows the scheduled task to run even as an unprivileged user that is not logged into the device. This will result in lower security context, allowing access to local resources only. The module requires...

Aether - Adaptive Exploit and Threat Hunting Engine for EVM-based Repositories 5.0

Aether is a Python-based framework for analyzing Solidity smart contracts, generating vulnerability findings, producing Foundry-based proof-of-concept PoC tests, and validating exploits on mainnet forks. It combines Solidity AST parsing, taint analysis, control flow graph analysis, cross-contract...

LanG -- a Governance-Aware Agentic AI Platform for Unified Security Operations

Modern Security Operations Centers struggle with alert fatigue, fragmented tooling, and limited cross-source event correlation. Challenges that current Security Information Event Management and Extended Detection and Response systems only partially address through fragmented tools. This paper...

Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions

CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on Commercial Off-The-Shelf COTS components and open-source software has introduced significant cybersecurity vulnerabilities. Ensuring the cybersecuri...

SoK: Understanding Anti-Forensics Concepts and Research Practices across Forensic Subdomains

Anti-forensics includes a growing set of techniques designed to obstruct forensic analysis. While cybercriminals increasingly rely on these methods, they also help researchers identify and remedy weaknesses in forensic tools, advancing the overall robustness of digital forensics. Despite repeated...

Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents

With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team methods mostly rely on modifying user prompts, which lack adaptability to new data and may impact the agent's performance. To address the challenge,...

Auditable Agents

LLM agents call tools, query databases, delegate tasks, and trigger external side effects. Once an agent system can act in the world, the question is no longer only whether harmful actions can be prevented--it is whether those actions remain answerable after deployment. We distinguish...

PQC-Enhanced QKD Networks: A Layered Approach

We present a layered and modular network architecture that combines Quantum Key Distribution QKD and Post-Quantum Cryptography PQC to provide scalable end-to-end security across long distance multi-hop, trusted-node quantum networks. To ensure interoperability and efficient practical deployment,...

OpenSSL Security Advisory 20260407

OpenSSL Security Advisory 20260407 - Applications using RSASVE key encapsulation to establish a secret encryption key can send contents of an uninitialized memory buffer to a malicious peer. Applications using AES-CFB128 encryption or decryption on systems with AVX-512 and VAES support can trigge...

Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing

The rapid advancement of Large Language Models LLMs has created new opportunities for Automated Penetration Testing AutoPT, spawning numerous frameworks aimed at achieving end-to-end autonomous attacks. However, despite the proliferation of related studies, existing research generally lacks...

SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments

With the rapid growth of interconnected devices in Industrial and Medical Internet of Things IIoT and MIoT ecosystems, ensuring timely and accurate detection of cyber threats has become a critical challenge. This study presents an advanced intrusion detection framework based on a hybrid...

Broken by Default: A Formal Verification Study of Security Vulnerabilities in AI-Generated Code

AI coding assistants are now used to generate production code in security-sensitive domains, yet the exploitability of their outputs remains unquantified. We address this gap with Broken by Default: a formal verification study of 3,500 code artifacts generated by seven frontier LLMs across 500...

Comprehensive List of User Deception Techniques in Emails

Email remains a central communication medium, yet its long-standing design and interface conventions continue to enable deceptive attacks. This research note presents a structured list of 42 email-based deception techniques, documented with 64 concrete example implementations, organized around th...

Explainable Autonomous Cyber Defense Using Adversarial Multi-Agent Reinforcement Learning

Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat APT actors exploit "Living off the Land" techniques and targeted telemetry perturbations ...

ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems

Existing research on LLM agent security mainly focuses on prompt injection and unsafe input/output behaviors. However, as agents increasingly rely on third-party tools and MCP servers, a new class of supply-chain threats has emerged, where malicious behaviors are embedded in seemingly benign tool...

Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities

LLM agents with tool access can discover and exploit security vulnerabilities. This is known. What is not known is which features of a system prompt trigger this behaviour, and which do not. We present a systematic taxonomy based on approximately 10,000 trials across seven models, 37 prompt...

pstrip64.sys Privilege Escalation

The pstrip64.sys kernel driver exposes an IOCTL that allows low-privileged users to map arbitrary ranges of physical memory into their own virtual address space. This primitive allows full read/write access to the system's physical RAM, enabling attackers to modify critical kernel structures and...

A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection

Open-source libraries are widely used in modern software development, introducing significant security vulnerabilities. While static analysis tools can identify potential vulnerabilities at scale, they often generate overwhelming reports with high false positive rates. Automated Exploit Generatio...

Digital Privacy in IoT: Exploring Challenges, Approaches and Open Issues

Privacy has always been a critical issue in the digital era, particularly with the increasing use of Internet of Things IoT devices. As the IoT continues to transform industries such as healthcare, smart cities, and home automation, it has also introduced serious challenges regarding the security...

SALLIE: Safeguarding against Latent Language and Image Exploits

Large Language Models LLMs and Vision-Language Models VLMs remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections arXiv:2307.15043, Greshake et al., 2023, arXiv:2306.13213. Existing defenses often degrade performance through complex input transformations or treat...

METATRON AI Penetration Testing

Metatron is a CLI-based AI penetration testing assistant that runs entirely on your local machine - no cloud, no API keys, no subscriptions. You give it a target IP or domain. It runs real recon tools nmap, whois, whatweb, curl, dig, nikto, feeds all results to a locally running AI model, and the...

SigCorr 0.1.0

SigCorr detects cross-protocol attack chains spanning SS7/MAP, Diameter S6a, and GTPv2-C interfaces in mobile core networks. It performs unified subscriber identity correlation across protocol boundaries to detect multi-stage attacks that single-interface monitors miss. It is written in Java 17 a...

Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs

Virtual Private Networks VPNs are widely used for censorship evasion and traffic protection. VPN users expect to be provided with adequate security protection, and at the same time not be affected by other users connected to the same VPN server, which can be illustrated as the non-interference...

Merkle Tree Certificate Post-Quantum PKI for Kubernetes and Cloud-Native 5G/B5G Core

Post-quantum signature schemes such as ML-DSA-65 produce signatures of 3,309 bytes and public keys of 1,952 bytes over 50 times larger than classical Ed25519. In TLS-authenticated environments like Kubernetes control planes and 5G Core networks, where every inter-component connection is mutually...

Evaluating Future Air Traffic Management Security

The L-Band Digital Aviation Communication System LDACS aims to modernize communications between the aircraft and the tower. Besides digitizing this type of communication, the contributors also focus on protecting them against cyberattacks. There are several proposals regarding LDACS security, and...

Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning

Large language models LLMs possess strong semantic understanding, driving significant progress in data mining applications. This is further enhanced by large reasoning models LRMs, which provide explicit multi-step reasoning traces. On the other hand, the growing need for the right to be forgotte...

LLM-Enabled Open-Source Systems in the Wild: An Empirical Study of Vulnerabilities in GitHub Security Advisories

Large language models LLMs are increasingly embedded in open-source software OSS ecosystems, creating complex interactions among natural language prompts, probabilistic model outputs, and execution-capable components. However, it remains unclear whether traditional vulnerability disclosure...

NetSecBed: A Container-Native Testbed for Reproducible Cybersecurity Experimentation

Cybersecurity research increasingly depends on reproducible evidence, such as traffic traces, logs, and labeled datasets, yet most public datasets remain static and offer limited support for controlled re-execution and traceability, especially in heterogeneous multi-protocol environments. This...

CoopGuard: Stateful Cooperative Agents Safeguarding LLMs against Evolving Multi-Round Attacks

As Large Language Models LLMs are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolving over multi-round interactions. Existing defenses are largely reactive and struggle to adapt as adversaries refine...

Semantics over Syntax: Uncovering Pre-Authentication 5G Baseband Vulnerabilities

Modern 5G user equipment UE processes Radio Resource Control RRC configuration messages during early control-plane exchanges, before authentication and integrity protection are established. Prior work for testing 5G UEs has largely focused on constructing syntactically invalid inputs. In contrast...

Beamforming Feedback As a Novel Attack Surface for Wi-Fi Physical-Layer Security

With the rapid evolution of wireless technologies, Wi-Fi has expanded beyond its original role in data transmission to support various emerging applications, particularly in physical-layer security, including device authentication, user authentication, and secret key generation. Despite extensive...

SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement

LLM-based agent systems increasingly rely on agent skills sourced from open registries to extend their capabilities, yet the openness of such ecosystems makes skills difficult to thoroughly vet. Existing attacks rely on injecting malicious instructions into skills, making them easily detectable b...

Triggering and Detecting Exploitable Library Vulnerability from the Client by Directed Greybox Fuzzing

Developers utilize third-party libraries to improve productivity, which also introduces potential security risks. Existing approaches generate tests for public functions to trigger library vulnerabilities from client programs, yet they depend on proof-of-concepts PoCs, which are often unavailable...

Defending Buffer Overflows in WebAssembly: A Transpiler Approach

WebAssembly is quickly becoming a popular compilation target for a variety of code. However, vulnerabilities in the source languages translate to vulnerabilities in the WebAssembly binaries. This work proposes a methodology and a WebAssembly transpiler to prevent buffer overflows in the unmanaged...

Perceptual Gaps: ASCII Art and Overlapping Audio As CAPTCHA

As multimodal large language models LLMs advance, traditional CAPTCHAs have become obsolete at distinguishing humans from bots. To address this shift, this paper aims to investigate the possibility of using tasks for which humans have evolved highly specialised neural processing. We introduce two...

SecPI: Secure Code Generation with Reasoning Models Via Security Reasoning Internalization

Reasoning language models RLMs are increasingly used in programming. Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code. Prior training-based approaches for secure code generation face a critical limitation that prevents their direct applicati...

AttackEval: A Systematic Empirical Study of Prompt Injection Attack Effectiveness against Large Language Models

Prompt injection has emerged as a critical vulnerability in large language model LLM deployments, yet existing research is heavily weighted toward defenses. The attack side -- specifically, which injection strategies are most effective and why -- remains insufficiently studied.We address this gap...

Explainable PQC: A Layered Interpretive Framework for Post-Quantum Cryptographic Security Assumptions

This paper studies how post-quantum cryptographic PQC security assumptions can be represented and communicated through a structured, layered framework that is useful for technical interpretation but does not replace formal cryptographic proofs. We propose "Explainable PQC,'' an interdisciplinary...

Automating Cloud Security and Forensics through a Secure-By-Design Generative AI Framework

As cloud environments become increasingly complex, cybersecurity and forensic investigations must evolve to meet emerging threats. Large Language Models LLMs have shown promise in automating log analysis and reasoning tasks, yet they remain vulnerable to prompt injection attacks and lack forensic...

Your Agent Is More Brittle Than You Think: Uncovering Indirect Injection Vulnerabilities in Agentic LLMs

The rapid deployment of open-source frameworks has significantly advanced the development of modern multi-agent systems. However, expanded action spaces, including uncontrolled privilege exposure and hidden inter-system interactions, pose severe security challenges. Specifically, Indirect Prompt...

Improving ML Attacks on LWE with Data Repetition and Stepwise Regression

The Learning with Errors LWE problem is a hard math problem in lattice-based cryptography. In the simplest case of binary secrets, it is the subset sum problem, with error. Effective ML attacks on LWE were demonstrated in the case of binary, ternary, and small secrets, succeeding on fairly sparse...

Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs

Software supply chain security compromises often stem from cascaded interactions of vulnerabilities, for example, between multiple vulnerable components. Yet, Software Bill of Materials SBOM-based pipelines for security analysis typically treat scanner findings as independent per-CVE Common...

Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode

Claude Code's auto mode is the first deployed permission system for AI coding agents, using a two-stage transcript classifier to gate dangerous tool calls. Anthropic reports a 0.4% false positive rate and 17% false negative rate on production traffic. We present the first independent evaluation o...

Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs

Transformer-based malware detection systems operating on graph modalities such as control flow graphs CFGs achieve strong performance by modeling structural relationships in program behavior. However, their robustness to adversarial evasion attacks remains underexplored. This paper examines the...

A Tsetlin Machine-Driven Intrusion Detection System for Next-Generation IoMT Security

The rapid adoption of the Internet of Medical Things IoMT is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it also introduces serious cybersecurity and patient safety concerns as attackers increasingly exploit new methods and...

A Systematic Security Evaluation of OpenClaw and Its Variants

Tool-augmented AI agents substantially extend the practical capabilities of large language models, but they also introduce security risks that cannot be identified through model-only evaluation. In this paper, we present a systematic security assessment of six representative OpenClaw-series agent...

Towards Secure Agent Skills: Architecture, Threat Taxonomy, and Security Analysis

Agent Skills is an emerging open standard that defines a modular, filesystem-based packaging format enabling LLM-based agents to acquire domain-specific expertise on demand. Despite rapid adoption across multiple agentic platforms and the emergence of large community marketplaces, the security...

Supply-Chain Poisoning Attacks against LLM Coding Agent Skill Ecosystems

LLM-based coding agents extend their capabilities via third-party agent skills distributed through open marketplaces without mandatory security review. Unlike traditional packages, these skills are executed as operational directives with system-level privileges, so a single malicious skill can...