314 matches found
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...
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,...
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...
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...
Can Transformer Memory Be Corrupted? Investigating Cache-Side Vulnerabilities in Large Language Models
Even when prompts and parameters are secured, transformer language models remain vulnerable because their key-value KV cache during inference constitutes an overlooked attack surface. This paper introduces Malicious Token Injection MTI, a modular framework that systematically perturbs cached key...
Security-Robustness Trade-Offs in Diffusion Steganography: A Comparative Analysis of Pixel-Space and VAE-Based Architectures
Current generative steganography research mainly pursues computationally expensive mappings to perfect Gaussian priors within single diffusion model architectures. This work introduces an efficient framework based on approximate Gaussian mapping governed by a scale factor calibrated through...
Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race
As advances in synthetic voice generation accelerate, an increasing variety of fake voice generators have emerged, producing audio that is often indistinguishable from real human speech. This evolution poses new and serious threats across sectors where audio recordings serve as critical evidence...
CVE-2023-53536
CVE-2023-53536 affects the Linux kernel in the blk-crypto subsystem. The issue stems from blk_crypto_evict_key() sometimes returning early without unlinking the key from the keyslot management structures, while the caller proceeds to free the blk_crypto_key. This mismatch can cause a use-after-fr...
EUVD-2025-15859
Malicious code in bioql PyPI...