SteganoSNN: SNN-Based Audio-In-Image Steganography with Encryption

Secure data hiding remains a fundamental challenge in digital communication, requiring a careful balance between computational efficiency and perceptual transparency. The balance between security and performance is increasingly fragile with the emergence of generative AI systems capable of...

Certified Randomness Amplification by Dynamically Probing Remote Random Quantum States

Cryptography depends on truly unpredictable numbers, but physical sources emit biased or correlated bits. Quantum mechanics enables the amplification of imperfect randomness into nearly perfect randomness, but prior demonstrations have required physically co-located, loophole-free Bell tests,...

Defend Smarter, Not Harder: The Power of Curated Vulnerability Intelligence

Let’s be honest, we as an industry spend far too long responding to issues that simply don’t matter. Chasing down false positives, reviewing threat intelligence reports that bear no relation to our sector, and more recently reviewing vulnerability advisories of systems not deployed within the...

[SECURITY] Fedora 42 Update: mupdf-1.26.3-4.fc42

MuPDF is a lightweight PDF viewer and toolkit written in portable C. The renderer in MuPDF is tailored for high quality anti-aliased graphics. MuPDF renders text with metrics and spacing accurate to within fractions of a pixel for the highest fidelity in reproducing the look of a printed page on...

EUVD-2025-13701

Malicious code in bioql PyPI...

EUVD-2024-45512

Malicious code in bioql PyPI...

ExpIDS: a Drift-Adaptable Network Intrusion Detection System with Improved Explainability

Despite all the advantages associated with Network Intrusion Detection Systems NIDSs that utilize machine learning ML models, there is a significant reluctance among cyber security experts to implement these models in real-world production settings. This is primarily because of their opaque natur...

Moderate: kernel-rt security update

The kernel-rt packages provide the Real Time Linux Kernel, which enables fine-tuning for systems with extremely high determinism requirements. Security Fixes: kernel: padata: fix UAF in padatareorder CVE-2025-21727 kernel: ipv6: mcast: extend RCU protection in igmp6send CVE-2025-21759 kernel: can...

LMDG: Advancing Lateral Movement Detection through High-Fidelity Dataset Generation

Lateral Movement LM attacks continue to pose a significant threat to enterprise security, enabling adversaries to stealthily compromise critical assets. However, the development and evaluation of LM detection systems are impeded by the absence of realistic, well-labeled datasets. To address this...

Space Cybersecurity Testbed: Fidelity Framework, Example Implementation, and Characterization

Cyber threats against space infrastructures, including satellites and systems on the ground, have not been adequately understood. Testbeds are important to deepen our understanding and validate space cybersecurity studies. The state of the art is that there are very few studies on building...

Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelines

We present a robust neural watermarking framework for scientific data integrity, targeting high-dimensional fields common in climate modeling and fluid simulations. Using a convolutional autoencoder, binary messages are invisibly embedded into structured data such as temperature, vorticity, and...

CEGA: a Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Graph Neural Networks GNNs have demonstrated remarkable utility across diverse applications, and their growing complexity has made Machine Learning as a Service MLaaS a viable platform for scalable deployment. However, this accessibility also exposes GNN to serious security threats, most notably...

Do Concept Replacement Techniques Really Erase Unacceptable Concepts?

Generative models, particularly diffusion-based text-to-image T2I models, have demonstrated astounding success. However, aligning them to avoid generating content with unacceptable concepts e.g., offensive or copyrighted content, or celebrity likenesses remains a significant challenge. Concept...

Adversarial Text Generation with Dynamic Contextual Perturbation

Adversarial attacks on Natural Language Processing NLP models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while maintaining human readability. Existing methods typically focus on word-level or local text segment alterations,...

MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models

Model extraction attacks aim to replicate the functionality of a black-box model through query access, threatening the intellectual property IP of machine-learning-as-a-service MLaaS providers. Defending against such attacks is challenging, as it must balance efficiency, robustness, and utility...

SimProcess: High Fidelity Simulation of Noisy ICS Physical Processes

Industrial Control Systems ICS manage critical infrastructures like power grids and water treatment plants. Cyberattacks on ICSs can disrupt operations, causing severe economic, environmental, and safety issues. For example, undetected pollution in a water plant can put the lives of thousands at...

NCorr-FP: a Neighbourhood-Based Correlation-Preserving Fingerprinting Scheme for Intellectual Property Protection of Structured Data

Ensuring data ownership and traceability of unauthorised redistribution are central to safeguarding intellectual property in shared data environments. Data fingerprinting addresses these challenges by embedding recipient-specific marks into the data, typically via content modifications. We propos...

From Noise to Action: Introducing Intelligence Hub

Co-authored by Raj Samani Chief Scientist & Craig Adams Chief Product Officer In traditional conflicts, intelligence is both integral and beneficial to decision-making at every level. Unfortunately, in cybersecurity, the impact of threat intelligence as an asset for organizations—and in particula...

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding

Variational Autoencoders VAEs have played a key role in scaling up diffusion-based generative models, as in Stable Diffusion, yet questions regarding their robustness remain largely underexplored. Although adversarial training has been an established technique for enhancing robustness in predicti...

On the Consistency of GNN Explanations for Malware Detection

Control Flow Graphs CFGs are critical for analyzing program execution and characterizing malware behavior. With the growing adoption of Graph Neural Networks GNNs, CFG-based representations have proven highly effective for malware detection. This study proposes a novel framework that dynamically...