PTSan: A Practical Memory Safety Sanitizer for C/C++ with Pointer-Object Authority

Memory safety errors remain the dominant source of severe vulnerabilities in C and C++. Pointer-based sanitizers provide stronger guarantees than location-based tools such as LLVM's ASan, but their overhead and compatibility limitations have constrained production use. We present PTSan, an LLVM...

Exploiting Load/Store Leakage of Sparse Vectors for Key Recovery in HQC

Hamming Quasi-Cyclic HQC is a code-based key encapsulation mechanism selected by NIST for standardization, making its resistance to implementation attacks critically important. We present a side-channel attack that exploits load/store leakage in the manipulation of HQC's sparse secret vectors...

Investigation of STEEP for Secure Communications over SIMO and MISO Channels Subject to Full-Duplex Jamming and Eavesdropping

Secure communications over SIMO and MISO channels between wireless nodes are commonly encountered in applications and widely considered in the literature. This paper investigates such a problem by considering a newly proposed scheme called secrecy-message transmission by echoing encrypted probes...

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection

Malicious anomalous activity detection is a fundamental challenge for cyber security systems. Both tensor decomposition under statistical framework with CANDECOMP-PARAFAC alternating Poisson regression CP-APR and normalizing flows have proven to be powerful unsupervised machine learning methods...

tcpreplay 4.5.3

tcpreplay is a BSD-style licensed tool to replay saved tcpdump files at arbitrary speeds. It provides a variety of features for replaying traffic for both passive sniffer devices as well as inline devices such as routers, firewalls, and intrusion detection systems. Many NIDSs fare poorly when...

Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT

Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them. Most reported detection accuracies, however, are measured on clean, in-distribution test data rather than on emails deliberately altered to evade detection. Th...

Chiral Analysis of Smart Contracts: Detecting Vulnerabilities from Relational Inconsistencies across Business Paths

Smart-contract vulnerabilities often arise from inconsistencies between business paths that should correspond to one another, such as single and batch entry points, direct and adapter-based flows, quote and execution paths, or inverse operations such as buy and sell. Existing analyzers are...

Salience Induction against Multi-Hop RAG Agents: Threat and Defense

Agentic retrieval-augmented generation RAG systems increasingly retrieve external evidence and orchestrate tools for knowledge-intensive applications. In Multi-Hop question answering, agents chain facts across documents. Existing defenses focus on content poisoning, which injects false facts, and...

GARAGE: Characterizing the Automation Boundary in LLM-Based Attack Graph Generation

While modern vehicle security depends on effective Cyber Threat Intelligence CTI synthesis, current automated tools struggle with unstructured data and automotive-specific architectural nuances. To bridge this gap, we introduce GARAGE, a RAG-powered framework that converts fragmented CTI into an...

RRAM-DP: Device-Calibrated Differential Privacy for In-Memory Edge Learning

Edge Artificial Intelligence of Things AIoT systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory RRAM is an attractive substrate for efficient AIoT thanks to its multi-bit storage and compute-in-memory CiM capabilities, while its...

RECEIPT: Deterministic, Reward-Hacking-Resistant Verification for White-Box Agentic XSS Discovery

Cross-Site Scripting XSS remains one of the most prevalent and damaging classes of web vulnerabilities. LLM-based coding agents offer a promising approach to XSS discovery by combining source-code reasoning with interactive testing against a running application. However, a coding agent's claims...

Xalgorix Autonomous AI Pentesting Agent 4.5.67

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...

Residual Observability and Attack Detectability in Encrypted OPC UA Traffic

OPC Unified Architecture OPC UA encryption conceals application-layer semantics and restricts intrusion detection to residual communication structure. Although machine learning-based intrusion detection systems IDSs can detect attacks in encrypted OPC UA traffic, the relationship between residual...

Specter 2.0

Specter turns your Flipper Zero into a pocket counter-surveillance bug-sweep for active 13.56 MHz NFC readers - a hidden card skimmer slipped into a payment terminal, a covert reader behind a door panel, a rogue logger taped under a desk. It passively senses the RF carrier that any powered-on...

Nebula AI-Powered Penetration Testing 2.0.1b2

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...

Stegano 3.0.0

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...

WordPress 5.6+ SkelerSecurity Core Security Plugin REST Batch API Mitigation

This WordPress plugin provides a temporary mitigation for the REST Batch API vulnerability chain commonly referred to as "wp2shell." It requires authentication before requests to the /wp-json/batch/v1 and ?restroute=/batch/v1 endpoints are processed, preventing unauthenticated exploitation of the...

Broken Gates: Re-Evaluating Web Bot Defenses in the Age of LLM Agents

LLM-based browser agents are rapidly changing the threat landscape for web security. Unlike traditional automation frameworks that execute predefined scripts, these agents can autonomously navigate websites, reason about page content, and interact with web interfaces using natural-language...

Self-State Attacks on Self-Hosted AI Agents: How Far Can OS Defenses Go?

Self-hosted AI agents read and write their own memory and configuration files to function. An agent may get compromised via corruption of its own state -- a compromise realized via legitimate OS system call invocation. We refer to this class of threats as self-state attacks. In this paper, we...

Nimux 1.0.2

Nimux is a native command surface for authorized security assessments. It combines network enumeration, credential validation, Active Directory operations, Kerberos workflows, remote execution, file movement, secrets collection, DCSync, GPO operations, database clients, and SOCKS routing into one...

Russian State Actors Are Compromising IP Cameras in Europe for Military Purposes

Intelligence from the Netherlands General Intelligence and Security Service AIVD and the Netherlands Defence Intelligence and Security Service MIVD - the Dutch services - illustrates that Russian state actors are systematically conducting digital espionage operations via IP cameras cameras with...

Trusted Credentials, Untrusted Behavior: Benchmarking LLM-Agent Security in High-Performance Computing

Large language model LLM agents are starting to take on routine work in high-performance computing HPC, including monitoring Slurm jobs, diagnosing failed builds, inspecting simulation output, and coordinating scientific workflows. To do this work, an agent commonly acts under its user's...

Towards an Automated Test of LLM Security Knowledge

Large language models LLMs are increasingly used for a range of software, hardware and human-centered security tasks. Consequently, LLM performance on security tasks is an active area of measurement and research, often with a focus on identifying areas in which LLM security "knowledge'' may be...

CryptanalysisBench: Can LLMs Do Cryptanalysis?

Cryptanalysis - the task of finding attacks against cryptographic schemes - sits at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced fastest. Cryptanalysis represents both a clean testbed for frontier reasoning as practical attacks can be...

Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-Based Code Generation

LLM-based systems increasingly incorporate long-term memory to improve cross-session continuity. However, once insecure coding preferences are stored, they may silently influence security-critical decisions in subsequent generations. In this study, we conduct the first systematic empirical study ...

(A)ISpy: Parasitic Trojans for Machine Learning Infrastructure

Modern machine learning ML pipelines depend heavily on third party libraries for graph compilation and hardware acceleration. While current practices audit data and model artifacts or rely on file integrity checks, the execution environment remains implicitly trusted. This blind spot enables acti...

Measuring and Evaluating the Performance of Generative AI Models for Scam Detection

Online scams continue to cause substantial financial and personal harm. As a result, detection systems based on Large Language Models LLMs have been integrated into security products ranging from email gateways and browser extensions to fraud-monitoring dashboards. As this adoption accelerates, a...

The Finite Key Effect of Side-Channel-Secure Quantum Key Distribution beyond Post-Selection Technique

By applying the framework of entropic uncertainty relation EUR and the Quantum Leftover Hash Lemma QLHL, we introduce a security-proof method for variable-length side-channel-secure SCS quantum key distribution QKD against coherent attacks. This method reframes composable security as a statistica...

Shared Vulnerabilities in Robustness-Optimized Defenses: One Breach Exposes the Family

Adversarial robustness optimization aims to preserve correct prediction under adversarial perturbations, and has produced substantial robustness gains through methods such as adversarial training and adversarial purification. However, we identify a new security risk: these gains can create shared...

Between Safe Boundaries: Exploiting Temporal Consistency for Jailbreaking Text-To-Video Generation Models

Recently, text-to-video T2V models have been widely deployed, sparking growing concerns over their robustness against jailbreak attacks. Existing jailbreak methods, mostly adapted from text-to-image attacks, suffer notable drawbacks when applied to T2V systems. They fail to fully leverage tempora...

How Jailbreak Attacks Inform Safety Alignment: A Defender-Centric, Shapley-Based Evaluation of Jailbreak Contributions

Jailbreak attacks on large language models are usually evaluated by attacker-centric metrics such as attack success rate ASR, yet an attack that breaks a model is not necessarily useful for improving its safety. We propose a defender-centric view of jailbreak evaluation, where attacks are evaluat...

Isolation Failure from Shared Storage: Characterizing and Exploiting Page-Cache SCA Leakage across Containers and VMs

Modern cloud platforms increasingly combine strong software isolation mechanisms with shared hardware resources to improve performance and resource efficiency. Conventional containers do this by sharing the host kernel directly, whereas sandboxed runtimes e.g., gVisor and VM-based runtimes e.g.,...

ShadowPickle: Evading Machine Learning Model Scanners Via Stealthy Pickle Deserialization Attacks

Model hosting hubs e.g., Hugging Face are vulnerable to supply chain attacks that enable remote code execution on trusted user environments. Attackers often distribute malicious Pre-trained ML models PTMs via model hubs. In this paper, we present novel attacks against PTMs and model hubs called...

DSA Nonce Vulnerabilities: An Interactive Analysis

Digital signatures are fundamental to identity authentication and data integrity in cybersecurity, and the NIST-standardized Digital Signature Algorithm DSA frequently appears in the cryptography track of CTF competitions. However, DSA relies on number theory, modular arithmetic, and large-intege...

A Multi-Model Hybrid Defense Approach against White-Box Adversarial Attacks in Computer Network Traffic

It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks. An essential tool for protecting computer networks against unknown cyber threats is Network Intrusion Detection System NIDS. However, NIDS faces a major security concern due to its...

Federated Lightweight Intrusion Detection in Drone Swarms with Knowledge Distillation

Drone swarms are increasingly deployed in critical applications such as surveillance, disaster response, and infrastructure monitoring. However, their reliance on open communication channels and their limited computational resources make them vulnerable to a wide range of cyber-threats. There is ...

Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture

While the Internet of Things IoT has become essential, they introduced serious security and privacy challenges, especially for mission-critical environments. Legacy devices are vulnerable to viruses, data breaches, and unauthorized access, and updating these devices would be infeasibly costly. As...

Adaptive Incident Prioritization for Security Operations at Scale

Large security operations centers SOCs often face hundreds of active incidents per day, creating substantial cognitive and operational demands for analysts. Analysts must quickly decide which incidents deserve attention within long, constantly changing queues, yet incidents are commonly ordered b...

How Do You Choose Your AI Component? an Interview Study of Secure AI Integration in Practice

The increasing adoption of Large Language Models LLMs as AI components in modern software systems introduces distinct security risks to the software supply chain. While many considerations and safety mechanisms are in place for components of the traditional software supply chain, the recent rapid...

A Non-Intrusive Traffic Analysis Framework for Authorization Risk Detection and Coordinated Response in Web Applications

Authorization violations under valid Web sessions are difficult to identify and handle in real time from traffic because they depend strongly on business semantics and exhibit few distinctive protocol-level features. This paper proposes a non-intrusive traffic analysis framework for authorization...

Converging Safety and Security: IO-Link Wireless and OPC UA over 5G under PrEN 50742

The integration of wireless communication technologies in industrial automation offers greater flexibility, but also exposes safety systems to a broader threat vector. Emerging regulations, such as the draft standard prEN 50742, mandate the convergence of functional safety and cybersecurity by...

The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs

Large Language Models LLMs are increasingly used for source code generation despite their outputs often exhibiting security vulnerabilities. Prior work shows that prompt engineering can mitigate such risks, yet 1 they focused on high-level prompting strategies, neglecting recent evidence that...

AI Watermark Evidence Fails Forensic Readiness: An Empirical Evaluation

Governments are increasingly mandating that LLM-generated content carry watermarks. The EU AI Act calls for markings that are "sufficiently reliable and robust." California's SB 942 requires disclosure that is "permanent or extraordinarily difficult to remove." Both mandates rest on an untested...

DICOMHawk: A Cyber Deception Framework for Medical Imaging Infrastructure

Cyber-attacks against exposed healthcare infrastructure threaten sensitive patient data and clinical operations, yet existing defensive tools for DICOM-based medical imaging systems provide limited interaction and are easily fingerprinted. We introduce DICOMHawk, a cyber-deception framework that...

Code-Poisoning Property Inference Attacks

The flourishing code hosting platforms and coding agents enable even beginners with private data to build tailored Machine Learning ML models using available code quickly. The training data for ML models, often regarded as private property e.g., clinical records, transaction information, is at...

Vogls: A Fast Interactive Full-Timing Simulator for Pre-Silicon Power Side-Channel Analysis

Designing hardware circuits resistant to side-channel attacks increasingly relies on simulation to predict device leakage before fabrication. Current functional verification simulators are designed for extended correctness-checking runs and are ill-suited for producing large numbers of short trac...

Evaluating Open-Weight LLMs for Generating Structured Threat Information for Autonomous Vehicle Vulnerabilities

Connected and Autonomous Vehicles CAVs rely on interconnected software and hardware components, including sensors, Electronic Control Units, in-vehicle infotainment systems, and telematics units, where vulnerabilities can compromise assets, users, and vehicle operations. These vulnerabilities are...

Natural Backdoor Attacks on Speech Recognition Models

With the rapid development of deep learning, its vulnerability has gradually emerged in recent years. This work focuses on backdoor attacks on speech recognition systems. We adopt sounds that are ordinary in nature or in our daily life as triggers for natural backdoor attacks. We conduct...

Nimux 1.0.1

Nimux is a native command surface for authorized security assessments. It combines network enumeration, credential validation, Active Directory operations, Kerberos workflows, remote execution, file movement, secrets collection, DCSync, GPO operations, database clients, and SOCKS routing into one...

DoSQ: A Cross-Layer Denial of Service Quality Attack by Exploiting Side Channels in 5G NR

The 3rd Generation Partnership Project 3GPP's Fifth Generation New Radio 5G NR is critical to supporting mission-critical services. However, 5G systems are vulnerable to smart jamming attacks that can propagate to applications running on top of these networks i.e., cross-layer. The 5G gNB...