University of Geneva

Defending against adversarial attacks by randomized diversification

The chain of the first 3 blocks can be organized in a parallel multi-channel structure that is followed by one or several aggregation blocks. The final decision about the class is made based on the ...
Nature

Adversarial Attacks in Deep Learning for Wireless Communication

The integration of deep learning techniques into wireless communication systems has catalysed notable advancements in tasks such as modulation classification and spectrum sensing. However, the ...
JD Supra

Adversarial Machine Learning in Focus: Novel Risks, Straightforward Legal Approaches

The Artificial Intelligence and Machine Learning (“AI/ML”) risk environment is in flux. One reason is that regulators are shifting from AI safety to AI innovation approaches, as a recent DataPhiles ...
The National Law Review

NIST Finalizes Cyber Attack Guidance for Adversarial Machine Learning

We collaborate with the world's leading lawyers to deliver news tailored for you. Sign Up for any (or all) of our 25+ Newsletters. Some states have laws and ethical rules regarding solicitation and ...
Devdiscourse

Cyber threat intelligence must evolve to protect AI systems

According to the researchers, the ultimate goal is to build a comprehensive cyber threat intelligence ecosystem for artificial intelligence systems. Such a system would allow security tools to scan AI ...

Cybersecurity Leader Dima Shaposhnykov Unveils AI-vs-AI Strategy to Tackle Emerging Threats

Cybersecurity strategist Dima Shaposhnykov argues that organizations can no longer rely on conventional threat detection models when facing adversaries using generative AI, automated reconnaissance, ...
Security Boulevard

Beyond Prompt Injection: The Hidden AI Security Threats in Machine Learning Platforms

What’s the first thing you think of when you hear about ai security threats and vulnerabilities? If you’re like most people, your mind probably jumps to Large Language Model (LLM) ...
Homeland Security Today

NIST Releases Final Report on AI/ML Cybersecurity Threats and Mitigations

The National Institute of Standards and Technology (NIST) has published its final report on adversarial machine learning (AML), offering a comprehensive taxonomy and shared terminology to help ...
Security Boulevard

Model Inversion Attacks: Growing AI Business Risk

In an era where artificial intelligence (AI) and machine learning (ML) are driving unprecedented innovation and efficiency, a ...
EurekAlert!

A new AI-based attack framework advances multi-agent reinforcement learning by amplifying vulnerability and bypassing defenses

Researchers have developed a new artificial intelligence approach that exposes critical weaknesses in multi-agent reinforcement learning systems, enabling stronger coordinated attacks with broad ...

Sophos Active Adversary Report 2026: Identity attacks dominate as threat groups proliferate

Sophos, a global leader of innovative security solutions for defeating cyberattacks, today released the 2026 Sophos Active Adversary Report. It reveals that 67% of all incidents investigated by Sophos ...