CVE-2025-32100

CVE-2025-32100 affects Samsung Mobile Processor, Wearable Processor, and Modem Exynos/Modem families (multiple models listed). Root cause: a buffer copy programming mistake leading to out-of-bounds writes via malformed ROHC packets. Impact per NVD metrics: Network attack vector, low attack comple...

PT-2025-35595

Name of the Vulnerable Software and Affected Versions: Samsung Mobile Processor, Wearable Processor, and Modem Exynos 980 Samsung Mobile Processor, Wearable Processor, and Modem Exynos 990 Samsung Mobile Processor, Wearable Processor, and Modem Exynos 850 Samsung Mobile Processor, Wearable...

VULSOVER: Vulnerability Detection Via LLM-Driven Constraint Solving

Traditional vulnerability detection methods rely heavily on predefined rule matching, which often fails to capture vulnerabilities accurately. With the rise of large language models LLMs, leveraging their ability to understand code semantics has emerged as a promising direction for achieving more...

False Data-Injection Attack Detection in Cyber-Physical Systems: a Wasserstein Distributionally Robust Reachability Optimization Approach

Cyber-physical system CPS is the foundational backbone of modern critical infrastructures, so ensuring its security and resilience against cyber-attacks is of pivotal importance. This paper addresses the challenge of designing anomaly detectors for CPS under false-data injection FDI attacks and...

MAL-2025-32335 Malicious code in robust-free-bootstrap-admin-template (npm)

The package robust-free-bootstrap-admin-template was found to contain malicious code...

MirGuard: Towards a Robust Provenance-Based Intrusion Detection System against Graph Manipulation Attacks

Learning-based Provenance-based Intrusion Detection Systems PIDSes have become essential tools for anomaly detection in host systems due to their ability to capture rich contextual and structural information, as well as their potential to detect unknown attacks. However, recent studies have shown...

AuthPrint: Fingerprinting Generative Models against Malicious Model Providers

Generative models are increasingly adopted in high-stakes domains, yet current deployments offer no mechanisms to verify the origin of model outputs. We address this gap by extending model fingerprinting techniques beyond the traditional collaborative setting to one where the model provider may a...

LENS-DF: Deepfake Detection and Temporal Localization for Long-Form Noisy Speech

This study introduces LENS-DF, a novel and comprehensive recipe for training and evaluating audio deepfake detection and temporal localization under complicated and realistic audio conditions. The generation part of the recipe outputs audios from the input dataset with several critical...

SHIELD: a Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks

Audio plays a crucial role in applications like speaker verification, voice-enabled smart devices, and audio conferencing. However, audio manipulations, such as deepfakes, pose significant risks by enabling the spread of misinformation. Our empirical analysis reveals that existing methods for...

Multi-Trigger Poisoning Amplifies Backdoor Vulnerabilities in LLMs

Recent studies have shown that Large Language Models LLMs are vulnerable to data poisoning attacks, where malicious training examples embed hidden behaviours triggered by specific input patterns. However, most existing works assume a phrase and focus on the attack's effectiveness, offering limite...

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...

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-Of-Distribution Trap Is All You Need

Data-free knowledge distillation DFKD transfers knowledge from a teacher to a student without access the real in-distribution ID data. Its common solution is to use a generator to synthesize fake data and use them as a substitute for real ID data. However, existing works typically assume teachers...

VideoMark: a Distortion-Free Robust Watermarking Framework for Video Diffusion Models

Whitepaper called VideoMark: A Distortion-Free Robust Watermarking Framework For Video Diffusion Models...

Generalization under Byzantine and Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning

Whitepaper called Generalization Under Byzantine and Poisoning Attacks: Tight Stability Bounds In Robust Distributed Learning...

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,...

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext...

Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT

Address Resolution Protocol ARP spoofing attacks severely threaten Internet of Things IoT networks by allowing attackers to intercept, modify, or block communications. Traditional detection methods are insufficient due to high false positives and poor adaptability. This research proposes a...

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

Despite federated learning FL's potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning CFL has emerged to address this challenge by partitioning users into clusters according to their...

EditLord: Learning Code Transformation Rules for Code Editing

Code editing is a foundational task in software development, where its effectiveness depends on whether it introduces desired code property changes without changing the original code's intended functionality. Existing approaches often formulate code editing as an implicit end-to-end task, omittin...

Watermarking Autoregressive Image Generation

Watermarking the outputs of generative models has emerged as a promising approach for tracking their provenance. Despite significant interest in autoregressive image generation models and their potential for misuse, no prior work has attempted to watermark their outputs at the token level. In thi...