PhreshPhish: a Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark

Phishing remains a pervasive and growing threat, inflicting heavy economic and reputational damage. While machine learning has been effective in real-time detection of phishing attacks, progress is hindered by lack of large, high-quality datasets and benchmarks. In addition to poor-quality due to...

Questionnaire Mate 2.0

Questionnaire Mate is a cool script that lets you read in a list of questions and uses OpenAI to answer them based on a private knowledge base. Useful for a less informed individual to feed AI audit questions and extract proper answers...

Reporte De Vulnerabilidades En IIoT. Proyecto DEFENDER

The main objective of this technical report is to conduct a comprehensive study on devices operating within Industrial Internet of Things IIoT environments, describing the scenarios that define this category and analysing the vulnerabilities that compromise their security. To this end, the report...

BandFuzz: an ML-Powered Collaborative Fuzzing Framework

Collaborative fuzzing has recently emerged as a technique that combines multiple individual fuzzers and dynamically chooses the appropriate combinations suited for different programs. Unlike individual fuzzers, which rely on specific assumptions to maintain their effectiveness, collaborative...

DNN Unicode Path Normalization NTLM Hash Disclosure

This exploit targets a vulnerability in DNN formerly DotNetNuke versions 6.0.0 to before 10.0.1 that allows attackers to disclose NTLM hashes through Unicode path normalization attacks...

The Man behind the Sound: Demystifying Audio Private Attribute Profiling Via Multimodal Large Language Model Agents

Our research uncovers a novel privacy risk associated with multimodal large language models MLLMs: the ability to infer sensitive personal attributes from audio data -- a technique we term audio private attribute profiling. This capability poses a significant threat, as audio can be covertly...

Crypto-Assisted Graph Degree Sequence Release under Local Differential Privacy

Whitepaper called Crypto-Assisted Graph Degree Sequence Release Under Local Differential Privacy...

Optimal Debiased Inference on Privatized Data Via Indirect Estimation and Parametric Bootstrap

We design a debiased parametric bootstrap framework for statistical inference from differentially private data. Existing usage of the parametric bootstrap on privatized data ignored or avoided handling the effect of clamping, a technique employed by the majority of privacy mechanisms. Ignoring th...

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack

Graph Neural Network GNN-based network intrusion detection systems NIDS are often evaluated on single datasets, limiting their ability to generalize under distribution drift. Furthermore, their adversarial robustness is typically assessed using synthetic perturbations that lack realism. This...

"Is It Always Watching? Is It Always Listening?" Exploring Contextual Privacy and Security Concerns toward Domestic Social Robots

Equipped with artificial intelligence AI and advanced sensing capabilities, social robots are gaining interest among consumers in the United States. These robots seem like a natural evolution of traditional smart home devices. However, their extensive data collection capabilities, anthropomorphic...

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework

The growing reliance on data-driven applications in sectors such as healthcare, finance, and law enforcement underscores the need for secure, privacy-preserving, and scalable mechanisms for data generation and sharing. Synthetic data generation SDG has emerged as a promising approach but often...

Differentially Private Federated Low Rank Adaptation beyond Fixed-Matrix

Large language models LLMs typically require fine-tuning for domain-specific tasks, and LoRA offers a computationally efficient approach by training low-rank adapters. LoRA is also communication-efficient for federated LLMs when multiple users collaboratively fine-tune a global LLM model without...

From Alerts to Intelligence: a Novel LLM-Aided Framework for Host-Based Intrusion Detection

Host-based intrusion detection system HIDS is a key defense component to protect the organizations from advanced threats like Advanced Persistent Threats APT. By analyzing the fine-grained logs with approaches like data provenance, HIDS has shown successes in capturing sophisticated attack traces...

DVFS: a Dynamic Verifiable Fuzzy Search Service for Encrypted Cloud Data

Cloud storage introduces critical privacy challenges for encrypted data retrieval, where fuzzy multi-keyword search enables approximate matching while preserving data confidentiality. Existing solutions face fundamental trade-offs between security and efficiency: linear-search mechanisms provide...

Exploring User Security and Privacy Attitudes and Concerns toward the Use of General-Purpose LLM Chatbots for Mental Health

Individuals are increasingly relying on large language model LLM-enabled conversational agents for emotional support. While prior research has examined privacy and security issues in chatbots specifically designed for mental health purposes, these chatbots are overwhelmingly "rule-based" offering...

DNS Tunneling: Threat Landscape and Improved Detection Solutions

Detecting Domain Name System DNS tunneling is a significant challenge in security due to its capacity to hide harmful actions within DNS traffic that appears to be normal and legitimate. Traditional detection methods are based on rule-based approaches or signature matching methods that are often...

Akamai CloudTest XML Injection

This is a Python-based exploit for CVE-2025-49493, which affects Akamai CloudTest versions before 60 2025.06.02 12988. The vulnerability allows for XML External Entity XXE injection through the SOAP service endpoint...

3S-Attack: Spatial, Spectral and Semantic Invisible Backdoor Attack against DNN Models

Backdoor attacks involve either poisoning the training data or directly modifying the model in order to implant a hidden behavior, that causes the model to misclassify inputs when a specific trigger is present. During inference, the model maintains high accuracy on benign samples but misclassifie...

Vulnerability Mitigation System (VMS): LLM Agent and Evaluation Framework for Autonomous Penetration Testing

As the frequency of cyber threats increases, conventional penetration testing is failing to capture the entirety of todays complex environments. To solve this problem, we propose the Vulnerability Mitigation System VMS, a novel agent based on a Large Language Model LLM capable of performing...

Contrastive-KAN: a Semi-Supervised Intrusion Detection Framework for Cybersecurity with Scarce Labeled Data

In the era of the Fourth Industrial Revolution, cybersecurity and intrusion detection systems are vital for the secure and reliable operation of IoT and IIoT environments. A key challenge in this domain is the scarcity of labeled cyber-attack data, as most industrial systems operate under normal...

ExCyTIn-Bench: Evaluating LLM Agents on Cyber Threat Investigation

We present ExCyTIn-Bench, the first benchmark to Evaluate an LLM agent x on the task of Cyber Threat Investigation through security questions derived from investigation graphs. Real-world security analysts must sift through a large number of heterogeneous alert signals and security logs, follow...

OpenBlow Missing Headers

Multiple public deployments of the OpenBlow whistleblowing software lack critical HTTP security headers. These configurations expose users to client-side vulnerabilities including cross site scripting, clickjacking, API misuse, and referer leakage. Given the extreme sensitivity of users...

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models

Automated Program Repair APR is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of...

PRM-Free Security Alignment of Large Models Via Red Teaming and Adversarial Training

Large Language Models LLMs have demonstrated remarkable capabilities across diverse applications, yet they pose significant security risks that threaten their safe deployment in critical domains. Current security alignment methodologies predominantly rely on Process Reward Models PRMs to evaluate...

Secure and Efficient Quantum Signature Scheme Based on the Controlled Unitary Operations Encryption

Quantum digital signatures ensure unforgeable message authenticity and integrity using quantum principles, offering unconditional security against both classical and quantum attacks. They are crucial for secure communication in high-stakes environments, ensuring trust and long-term protection in...

ARMOR: Aligning Secure and Safe Large Language Models Via Meticulous Reasoning

Large Language Models LLMs have demonstrated remarkable generative capabilities. However, their susceptibility to misuse has raised significant safety concerns. While post-training safety alignment methods have been widely adopted, LLMs remain vulnerable to malicious instructions that can bypass...

DM-RSA: an Extension of RSA with Dual Modulus

We introduce DM-RSA Dual Modulus RSA, a variant of the RSA cryptosystem that employs two distinct moduli symmetrically to enhance security. By leveraging the Chinese Remainder Theorem CRT for decryption, DM-RSA provides increased robustness against side-channel attacks while preserving the...

WordPress WPBookit 1.0.4 Arbitrary File Upload

WordPress WPBookit plugin versions 1.0.4 and below suffer from an arbitrary file upload vulnerability...

Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

Split Learning SL -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning ML processes. Though promising, SL has proven vulnerable to different attacks, thus raising concerns about how effective it may be in terms of data privacy. Recent works have...

PLA: Prompt Learning Attack against Text-To-Image Generative Models

Text-to-Image T2I models have gained widespread adoption across various applications. Despite the success, the potential misuse of T2I models poses significant risks of generating Not-Safe-For-Work NSFW content. To investigate the vulnerability of T2I models, this paper delves into adversarial...

DESIGN: Encrypted GNN Inference Via Server-Side Input Graph Pruning

Graph Neural Networks GNNs have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption FHE, typically incurs substantial computational overhead, rendering real-time and...

EventHunter: Dynamic Clustering and Ranking of Security Events from Hacker Forum Discussions

Hacker forums provide critical early warning signals for emerging cybersecurity threats, but extracting actionable intelligence from their unstructured and noisy content remains a significant challenge. This paper presents an unsupervised framework that automatically detects, clusters, and...

Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC

Private inference based on Secure Multi-Party Computation MPC addresses data privacy risks in Machine Learning as a Service MLaaS. However, existing MPC-based private inference frameworks focuses on semi-honest or honest majority models, whose threat models are overly idealistic, while malicious...

Endorsement-Driven Blockchain SSI Framework for Dynamic IoT Ecosystems

Self-Sovereign Identity SSI offers significant potential for managing identities in the Internet of Things IoT, enabling decentralized authentication and credential management without reliance on centralized entities. However, existing SSI frameworks often limit credential issuance and revocation...

AICrypto: a Comprehensive Benchmark for Evaluating Cryptography Capabilities of Large Language Models

Whitepaper called AICrypto: A Comprehensive Benchmark For Evaluating Cryptography Capabilities Of Large Language Models...

LaSM: Layer-Wise Scaling Mechanism for Defending Pop-Up Attack on GUI Agents

Graphical user interface GUI agents built on multimodal large language models MLLMs have recently demonstrated strong decision-making abilities in screen-based interaction tasks. However, they remain highly vulnerable to pop-up-based environmental injection attacks, where malicious visual element...

Secure and Efficient UAV-Based Face Detection Via Homomorphic Encryption and Edge Computing

This paper aims to propose a novel machine learning ML approach incorporating Homomorphic Encryption HE to address privacy limitations in Unmanned Aerial Vehicles UAV-based face detection. Due to challenges related to distance, altitude, and face orientation, high-resolution imagery and...

CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System GPS data, which is susceptible to GPS data outage. This paper...

PromptChain: a Decentralized Web3 Architecture for Managing AI Prompts As Digital Assets

We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair...

AdvGrasp: Adversarial Attacks on Robotic Grasping from a Physical Perspective

Adversarial attacks on robotic grasping provide valuable insights into evaluating and improving the robustness of these systems. Unlike studies that focus solely on neural network predictions while overlooking the physical principles of grasping, this paper introduces AdvGrasp, a framework for...

A Login Page Transparency and Visual Similarity Based Zero Day Phishing Defense Protocol

Phishing is a prevalent cyberattack that uses look-alike websites to deceive users into revealing sensitive information. Numerous efforts have been made by the Internet community and security organizations to detect, prevent, or train users to avoid falling victim to phishing attacks. Most of thi...

Interpreting Differential Privacy in Terms of Disclosure Risk

As the use of differential privacy DP becomes widespread, the development of effective tools for reasoning about the privacy guarantee becomes increasingly critical. In pursuit of this goal, we demonstrate novel relationships between DP and measures of statistical disclosure risk. We suggest how...

Several New Classes of Self-Orthogonal Minimal Linear Codes Violating the Ashikhmin-Barg Condition

Whitepaper called Several New Classes Of Self-Orthogonal Minimal Linear Codes Violating The Ashikhmin-Barg Condition...

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats

Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical...

A Mixture of Linear Corrections Generates Secure Code

Large language models LLMs have become proficient at sophisticated code-generation tasks, yet remain ineffective at reliably detecting or avoiding code vulnerabilities. Does this deficiency stem from insufficient learning about code vulnerabilities, or is it merely a result of ineffective...

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs

The rapid expansion of Internet of Things IoT networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging...

Hybrid Quantum Security for IPsec

Quantum Key Distribution QKD offers information-theoretic security against quantum computing threats, but integrating QKD into existing security protocols remains an unsolved challenge due to fundamental mismatches between pre-distributed quantum keys and computational key exchange paradigms. Thi...

LLM-Stackelberg Games: Conjectural Reasoning Equilibria and Their Applications to Spearphishing

We introduce the framework of LLM-Stackelberg games, a class of sequential decision-making models that integrate large language models LLMs into strategic interactions between a leader and a follower. Departing from classical Stackelberg assumptions of complete information and rational agents, ou...

Implementing and Evaluating Post-Quantum DNSSEC in CoreDNS

The emergence of quantum computers poses a significant threat to current secure service, application and/or protocol implementations that rely on RSA and ECDSA algorithms, for instance DNSSEC, because public-key cryptography based on number factorization or discrete logarithm is vulnerable to...

Demo: Secure Edge Server for Network Slicing and Resource Allocation in Open RAN

Next-Generation Radio Access Networks NGRAN aim to support diverse vertical applications with strict security, latency, and Service-Level Agreement SLA requirements. These demands introduce challenges in securing the infrastructure, allocating resources dynamically, and enabling real-time...