GIFDL: Generated Image Fluctuation Distortion Learning for Enhancing Steganographic Security

Minimum distortion steganography is currently the mainstream method for modification-based steganography. A key issue in this method is how to define steganographic distortion. With the rapid development of deep learning technology, the definition of distortion has evolved from manual design to...

Feature Selection Via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation

Distributed Denial of Service DDoS attacks represent a persistent and evolving threat to modern networked systems, capable of causing large-scale service disruptions. The complexity of such attacks, often hidden within high-dimensional and redundant network traffic data, necessitates robust and...

Towards Model Resistant to Transferable Adversarial Examples Via Trigger Activation

Whitepaper called Towards Model Resistant To Transferable Adversarial Examples Via Trigger Activation...

Q-FAKER: Query-Free Hard Black-Box Attack Via Controlled Generation

Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model's output information. They are not applicable in real-world...

Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification

Deep learning DL-based image classification models are essential for autonomous vehicle AV perception modules since incorrect categorization might have severe repercussions. Adversarial attacks are widely studied cyberattacks that can lead DL models to predict inaccurate output, such as incorrect...

DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-Based Intrusion Detection against Adversarial Attacks

The rapid proliferation of the Internet of Things IoT has introduced substantial security vulnerabilities, highlighting the need for robust Intrusion Detection Systems IDS. Machine learning-based intrusion detection systems ML-IDS have significantly improved threat detection capabilities; however...

InjectLab: a Tactical Framework for Adversarial Threat Modeling against Large Language Models

Large Language Models LLMs are changing the way people interact with technology. Tools like ChatGPT and Claude AI are now common in business, research, and everyday life. But with that growth comes new risks, especially prompt-based attacks that exploit how these models process language. InjectLa...

Bypassing Prompt Injection and Jailbreak Detection in LLM Guardrails

Large Language Models LLMs guardrail systems are designed to protect against prompt injection and jailbreak attacks. However, they remain vulnerable to evasion techniques. We demonstrate two approaches for bypassing LLM prompt injection and jailbreak detection systems via traditional character...

How to Enhance Downstream Adversarial Robustness (Almost) without Touching the Pre-Trained Foundation Model?

With the rise of powerful foundation models, a pre-training-fine-tuning paradigm becomes increasingly popular these days: A foundation model is pre-trained using a huge amount of data from various sources, and then the downstream users only need to fine-tune and adapt it to specific downstream...

The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injections

A Large Language Model LLM powered GUI agent is a specialized autonomous system that performs tasks on the user's behalf according to high-level instructions. It does so by perceiving and interpreting the graphical user interfaces GUIs of relevant apps, often visually, inferring necessary sequenc...

R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning

Vision-language models VLMs, such as CLIP, have gained significant popularity as foundation models, with numerous fine-tuning methods developed to enhance performance on downstream tasks. However, due to their inherent vulnerability and the common practice of selecting from a limited set of...

Investigating Cybersecurity Incidents Using Large Language Models in Latest-Generation Wireless Networks

The purpose of research: Detection of cybersecurity incidents and analysis of decision support and assessment of the effectiveness of measures to counter information security threats based on modern generative models. The methods of research: Emulation of signal propagation data in MIMO systems,...

CVE-2025-26644

Automated recognition mechanism with inadequate detection or handling of adversarial input perturbations in Windows Hello allows an unauthorized attacker to perform spoofing locally...

The vulnerability of the biometric authentication function in Windows Hello on Windows operating systems allows attackers to perform spoofing attacks.

The vulnerability of the biometric authentication function in Windows Hello on Windows operating systems is related to insufficient detection or processing of adversarial input anomalies. Exploiting this vulnerability can allow attackers to perform spoofing attacks...

CVE-2025-26644

Automated recognition mechanism with inadequate detection or handling of adversarial input perturbations in Windows Hello allows an unauthorized attacker to perform spoofing locally...

CVE-2025-26644

CVE-2025-26644 affects Windows Hello by allowing local spoofing due to inadequate handling of adversarial input perturbations. Microsoft documents a Windows Hello security fix path via monthly updates (e.g., KB5055528 for Windows 11 22621/22631; KB5055519 for older Windows builds) that enforces v...

⚡ Weekly Recap: Chrome 0-Day, IngressNightmare, Solar Bugs, DNS Tactics, and More

Every week, someone somewhere slips up—and threat actors slip in. A misconfigured setting, an overlooked vulnerability, or a too-convenient cloud tool becomes the perfect entry point. But what happens when the hunters become the hunted? Or when old malware resurfaces with new tricks? Step behind...

A Taxonomy of Adversarial Machine Learning Attacks and Mitigations

NIST just released a comprehensive taxonomy of adversarial machine learning attacks and countermeasures...

Sparring in the Cyber Ring: Using Automated Pentesting to Build Resilience

"A boxer derives the greatest advantage from his sparring partner…" — Epictetus, 50–135 AD Hands up. Chin tucked. Knees bent. The bell rings, and both boxers meet in the center and circle. Red throws out three jabs, feints a fourth, and—BANG—lands a right hand on Blue down the center. This wasn't...

adversarial-attacks-white-black-box (=0.1.7) potentially affected by CVE-2025-25302 via rembg (=2.0.57)

rembg PYPI version =2.0.57 is affected by a known vulnerability. The following packages have a transitive dependency on rembg and may be impacted: - adversarial-attacks-white-black-box =0.1.7 Source cves: CVE-2025-25302 Source advisory: OSV:GHSA-59QH-FMM7-3G9Q...