Authority Backdoor: A Certifiable Backdoor Mechanism for Authoring DNNs

Deep Neural Networks DNNs, as valuable intellectual property, face unauthorized use. Existing protections, such as digital watermarking, are largely passive; they provide only post-hoc ownership verification and cannot actively prevent the illicit use of a stolen model. This work proposes a...

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features

Phishing is a cybercrime in which individuals are deceived into revealing personal information, often resulting in financial loss. These attacks commonly occur through fraudulent messages, misleading advertisements, and compromised legitimate websites. This study proposes a Quantile Regression De...

UBUNTU-CVE-2025-40288

In the Linux kernel, the following vulnerability has been resolved: drm/amdgpu: Fix NULL pointer dereference in VRAM logic for APU devices Previously, APU platforms and other scenarios with uninitialized VRAM managers triggered a NULL pointer dereference in ttmresourcemanagerusage. The root cause...

Web Technologies Security in the AI Era: A Survey of CDN-Enhanced Defenses

The modern web stack, which is dominated by browser-based applications and API-first backends, now operates under an adversarial equilibrium where automated, AI-assisted attacks evolve continuously. Content Delivery Networks CDNs and edge computing place programmable defenses closest to users and...

The Road of Adaptive AI for Precision in Cybersecurity

Cybersecurity's evolving complexity presents unique challenges and opportunities for AI research and practice. This paper shares key lessons and insights from designing, building, and operating production-grade GenAI pipelines in cybersecurity, with a focus on the continual adaptation required to...

Large Language Models Cannot Reliably Detect Vulnerabilities in JavaScript: The First Systematic Benchmark and Evaluation

Researchers have proposed numerous methods to detect vulnerabilities in JavaScript, especially those assisted by Large Language Models LLMs. However, the actual capability of LLMs in JavaScript vulnerability detection remains questionable, necessitating systematic evaluation and comprehensive...

Frequency Bias Matters: Diving into Robust and Generalized Deep Image Forgery Detection

As deep image forgery powered by AI generative models, such as GANs, continues to challenge today's digital world, detecting AI-generated forgeries has become a vital security topic. Generalizability and robustness are two critical concerns of a forgery detector, determining its reliability when...

Beyond Jailbreak: Unveiling Risks in LLM Applications Arising from Blurred Capability Boundaries

LLM applications i.e., LLM apps leverage the powerful capabilities of LLMs to provide users with customized services, revolutionizing traditional application development. While the increasing prevalence of LLM-powered applications provides users with unprecedented convenience, it also brings fort...

Certified but Fooled! Breaking Certified Defences with Ghost Certificates

Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better understand the limits of guarantee provisions. Now, the objective is to not only mislead a classifier, but also manipulate the certification process t...

Efficient Adversarial Malware Defense Via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reduction

The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and computational efficiency. While recent advances in adversarial defenses have demonstrated strong robustness improvements, they often introduce computationa...

Phantom Menace: Exploring and Enhancing the Robustness of VLA Models against Physical Sensor Attacks

Vision-Language-Action VLA models revolutionize robotic systems by enabling end-to-end perception-to-action pipelines that integrate multiple sensory modalities, such as visual signals processed by cameras and auditory signals captured by microphones. This multi-modality integration allows VLA...

Robustness of LLM-Enabled Vehicle Trajectory Prediction under Data Security Threats

The integration of large language models LLMs into automated driving systems has opened new possibilities for reasoning and decision-making by transforming complex driving contexts into language-understandable representations. Recent studies demonstrate that fine-tuned LLMs can accurately predict...

MTAttack: Multi-Target Backdoor Attacks against Large Vision-Language Models

Recent advances in Large Visual Language Models LVLMs have demonstrated impressive performance across various vision-language tasks by leveraging large-scale image-text pretraining and instruction tuning. However, the security vulnerabilities of LVLMs have become increasingly concerning,...

JPRO: Automated Multimodal Jailbreaking Via Multi-Agent Collaboration Framework

The widespread application of large VLMs makes ensuring their secure deployment critical. While recent studies have demonstrated jailbreak attacks on VLMs, existing approaches are limited: they require either white-box access, restricting practicality, or rely on manually crafted patterns, leadin...

RAG-Targeted Adversarial Attack on LLM-Based Threat Detection and Mitigation Framework

The rapid expansion of the Internet of Things IoT is reshaping communication and operational practices across industries, but it also broadens the attack surface and increases susceptibility to security breaches. Artificial Intelligence has become a valuable solution in securing IoT networks, wit...

Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Models

Phishing emails continue to pose a persistent challenge to online communication, exploiting human trust and evading automated filters through realistic language and adaptive tactics. While large language models LLMs such as GPT-4 and LLaMA-3-8B achieve strong accuracy in text classification, thei...

Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era

Quantum machine learning QML is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanics, device noise in NISQ hardware, and hybrid quantum-classical execution pipelines introduce new risks that prevent...

SecureLearn - an Attack-Agnostic Defense for Multiclass Machine Learning against Data Poisoning Attacks

Data poisoning attacks are a potential threat to machine learning ML models, aiming to manipulate training datasets to disrupt their performance. Existing defenses are mostly designed to mitigate specific poisoning attacks or are aligned with particular ML algorithms. Furthermore, most defenses a...

Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses

With the wide application of deep reinforcement learning DRL techniques in complex fields such as autonomous driving, intelligent manufacturing, and smart healthcare, how to improve its security and robustness in dynamic and changeable environments has become a core issue in current research...

Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems

Adversarial attacks pose significant challenges to Machine Learning ML systems and especially Deep Neural Networks DNNs by subtly manipulating inputs to induce incorrect predictions. This paper investigates whether increasing the layer depth of deep neural networks affects their robustness agains...