Enhance the Machine Learning Algorithm Performance in Phishing Detection with Keyword Features

Recently, we can observe a significant increase of the phishing attacks in the Internet. In a typical phishing attack, the attacker sets up a malicious website that looks similar to the legitimate website in order to obtain the end-users' information. This may cause the leakage of the sensitive...

How the Solid Protocol Restores Digital Agency

The current state of digital identity is a mess. Your personal information is scattered across hundreds of locations: social media companies, IoT companies, government agencies, websites you have accounts on, and data brokers you've never heard of. These entities collect, store, and trade your...

SVAgent: AI Agent for Hardware Security Verification Assertion

Verification using SystemVerilog assertions SVA is one of the most popular methods for detecting circuit design vulnerabilities. However, with the globalization of integrated circuit design and the continuous upgrading of security requirements, the SVA development model has exposed major...

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...

CVE-2025-6211 MD5 Hash Collision in run-llama/llama_index

A vulnerability in the DocugamiReader class of the run-llama/llamaindex repository, up to version 0.12.28, involves the use of MD5 hashing to generate IDs for document chunks. This approach leads to hash collisions when structurally distinct chunks contain identical text, resulting in one chunk...

Quantum Properties Trojans (QuPTs) for Attacking Quantum Neural Networks

Quantum neural networks QNN hold immense potential for the future of quantum machine learning QML. However, QNN security and robustness remain largely unexplored. In this work, we proposed novel Trojan attacks based on the quantum computing properties in a QNN-based binary classifier. Our propose...

Understanding Malware Propagation Dynamics through Scientific Machine Learning

Accurately modeling malware propagation is essential for designing effective cybersecurity defenses, particularly against adaptive threats that evolve in real time. While traditional epidemiological models and recent neural approaches offer useful foundations, they often fail to fully capture the...

PotentRegion4MalDetect: Advanced Features from Potential Malicious Regions for Malware Detection

Malware developers exploit the fact that most detection models focus on the entire binary to extract the feature rather than on the regions of potential maliciousness. Therefore, they reverse engineer a benign binary and inject malicious code into it. This obfuscation technique circumvents the...

The Age of Integrity

We need to talk about data integrity. Narrowly, the term refers to ensuring that data isn’t tampered with, either in transit or in storage. Manipulating account balances in bank databases, removing entries from criminal records, and murder by removing notations about allergies from medical record...

PhishingHook: Catching Phishing Ethereum Smart Contracts Leveraging EVM Opcodes

The Ethereum Virtual Machine EVM is a decentralized computing engine. It enables the Ethereum blockchain to execute smart contracts and decentralized applications dApps. The increasing adoption of Ethereum sparked the rise of phishing activities. Phishing attacks often target users through...

Intelligent ARP Spoofing Detection Using Multi-Layered Machine Learning (ML) Techniques for IoT Networks

Address Resolution Protocol ARP spoofing remains a critical threat to IoT networks, enabling attackers to intercept, modify, or disrupt data transmission by exploiting ARP's lack of authentication. The decentralized and resource-constrained nature of IoT environments amplifies this vulnerability,...

Restoring Gaussian Blurred Face Images for Deanonymization Attacks

Gaussian blur is widely used to blur human faces in sensitive photos before the photos are posted on the Internet. However, it is unclear to what extent the blurred faces can be restored and used to re-identify the person, especially under a high-blurring setting. In this paper, we explore this...

InverTune: Removing Backdoors from Multimodal Contrastive Learning Models Via Trigger Inversion and Activation Tuning

Multimodal contrastive learning models like CLIP have demonstrated remarkable vision-language alignment capabilities, yet their vulnerability to backdoor attacks poses critical security risks. Attackers can implant latent triggers that persist through downstream tasks, enabling malicious control ...

NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models

Vision-Language Models VLMs such as CLIP have demonstrated remarkable capabilities in understanding relationships between visual and textual data through joint embedding spaces. Despite their effectiveness, these models remain vulnerable to adversarial attacks, particularly in the image modality,...

Tady: a Neural Disassembler without Structural Constraint Violations

Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this...

Watermarking LLM-Generated Datasets in Downstream Tasks

Large Language Models LLMs have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and question answering. Due to their efficiency and versatility, researchers and companies increasingly employ LLM-generated da...

AndroIDS : Android-Based Intrusion Detection System Using Federated Learning

The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other connected mobile environments. This article presents a federated learning-based intrusion detection framework called...

Chain-Of-Code Collapse: Reasoning Failures in LLMs Via Adversarial Prompting in Code Generation

Large Language Models LLMs have achieved remarkable success in tasks requiring complex reasoning, such as code generation, mathematical problem solving, and algorithmic synthesis -- especially when aided by reasoning tokens and Chain-of-Thought prompting. Yet, a core question remains: do these...

HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions

Large language model LLM watermarks enable authentication of text provenance, curb misuse of machine-generated text, and promote trust in AI systems. Current watermarks operate by changing the next-token predictions output by an LLM. The updated i.e., watermarked predictions depend on random side...

What Really Is a Member? Discrediting Membership Inference Via Poisoning

Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often fail under the strict definition of membership based on exact matching, and have suggested relaxing this definition t...