CVE-2025-52890 Incus vulnerable to antispoofing nftables firewall rule bypass on bridge networks with ACLs

Incus is a system container and virtual machine manager. When using an ACL on a device connected to a bridge, Incus versions 6.12 and 6.13generates nftables rules that partially bypass security options security.macfiltering, security.ipv4filtering and security.ipv6filtering. This can lead to ARP...

CVE-2025-52890

Incus CVE-2025-52890 affects the Incus system container/VM manager; versions 6.12 and 6.13 generate nftables rules when an ACL is used on a bridge-connected device, which partially bypasses security.mac_filtering, security.ipv4_filtering and security.ipv6_filtering. This can enable ARP spoofing o...

CVE-2025-52889

Incus (system container/VM manager) on versions 6.12–6.13 is vulnerable when an ACL on a bridge-connected device is used: nftables rules for local services can bypass security.mac_filtering, security.ipv4_filtering, and security.ipv6_filtering, enabling DHCP pool exhaustion and potential further ...

Perry: a High-Level Framework for Accelerating Cyber Deception Experimentation

Cyber deception aims to distract, delay, and detect network attackers with fake assets such as honeypots, decoy credentials, or decoy files. However, today, it is difficult for operators to experiment, explore, and evaluate deception approaches. Existing tools and platforms have non-portable and...

Poster: Enhancing GNN Robustness for Network Intrusion Detection Via Agent-Based Analysis

Graph Neural Networks GNNs show great promise for Network Intrusion Detection Systems NIDS, particularly in IoT environments, but suffer performance degradation due to distribution drift and lack robustness against realistic adversarial attacks. Current robustness evaluations often rely on...

Generative AI for Vulnerability Detection in 6G Wireless Networks: Advances, Case Study, and Future Directions

The rapid advancement of 6G wireless networks, IoT, and edge computing has significantly expanded the cyberattack surface, necessitating more intelligent and adaptive vulnerability detection mechanisms. Traditional security methods, while foundational, struggle with zero-day exploits, adversarial...

DHS Warns Pro-Iranian Hackers Likely to Target U.S. Networks After Iranian Nuclear Strikes

The United States government has warned of cyber attacks mounted by pro-Iranian groups after it launched airstrikes on Iranian nuclear sites as part of the Iran–Israel war that commenced on June 13, 2025. Stating that the ongoing conflict has created a "heightened threat environment" in the...

Vulnerability Assessment Combining CVSS Temporal Metrics and Bayesian Networks

Vulnerability assessment is a critical challenge in cybersecurity, particularly in industrial environments. This work presents an innovative approach by incorporating the temporal dimension into vulnerability assessment, an aspect neglected in existing literature. Specifically, this paper focuses...

Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs

Adversarial examples have attracted significant attention over the years, yet understanding their frequency-based characteristics remains insufficient. In this paper, we investigate the intriguing properties of adversarial examples in the frequency domain for the image classification task, with t...

LiSec-RTF: Reinforcing RPL Resilience against Routing Table Falsification Attack in 6LoWPAN

Routing Protocol for Low-Power and Lossy Networks RPL is an energy-efficient routing solution for IPv6 over Low-Power Wireless Personal Area Networks 6LoWPAN, recommended for resource-constrained devices. While RPL offers significant benefits, its security vulnerabilities pose challenges,...

Technical Evaluation of a Disruptive Approach in Homomorphic AI

We present a technical evaluation of a new, disruptive cryptographic approach to data security, known as HbHAI Hash-based Homomorphic Artificial Intelligence. HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rel...

An Attack Method for Medical Insurance Claim Fraud Detection Based on Generative Adversarial Network

Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security and efficiency of insurance systems. Although AI and machine learning algorithm...

TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs

Chip manufacturing is a complex process, and to achieve a faster time to market, an increasing number of untrusted third-party tools and designs from around the world are being utilized. The use of these untrusted third party intellectual properties IPs and tools increases the risk of adversaries...

OSI Stack Redesign for Quantum Networks: Requirements, Technologies, Challenges, and Future Directions

Quantum communication is poised to become a foundational element of next-generation networking, offering transformative capabilities in security, entanglement-based connectivity, and computational offloading. However, the classical OSI model-designed for deterministic and error-tolerant...

Watermarking Quantum Neural Networks Based on Sample Grouped and Paired Training

Quantum neural networks QNNs leverage quantum computing to create powerful and efficient artificial intelligence models capable of solving complex problems significantly faster than traditional computers. With the fast development of quantum hardware technology, such as superconducting qubits,...

Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders

Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders AE show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction...

Busting the Paper Ballot: Voting Meets Adversarial Machine Learning

We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is deciding whether a mark does or does not appear on a bubble associated to an alternative in a contest on the ballot. Barrett...

Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles

As Internet of Vehicles IoV technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or...

Bridging Unsupervised and Semi-Supervised Anomaly Detection: a Theoretically-Grounded and Practical Framework with Synthetic Anomalies

Anomaly detection AD is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to train classifiers to distinguish normal data from synthetic anomalies. We extend this principle to semi-supervised AD,...

Movable Antennas Meet Low-Altitude Wireless Networks: Fundamentals, Opportunities, and Future Directions

With the rapid development of low-altitude applications, there is an increasing demand for low-altitude wireless networks LAWNs to simultaneously achieve high-rate communication, precise sensing, and reliable control in the low-altitude airspace. In this paper, we first present a typical system...