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CyberSecEval 2 - A Comprehensive Evaluation Framework for Cybersecurity Risks and Capabilities of Large Language Models

Hugging Face Blog · huggingface.co · May 24, 2024
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MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 15, 2026

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.

Related papers

#machine learning Open access Jun 2025

Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation

Five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics are presented, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.

M. Hasan, Muhammad Waseem, Kai-Kristian Kemell et al. · 10 citations · ⚡1
#artificial intelligence Review Open access 2026

Adversarial Machine Learning: Security Risks and Defense Strategies in AI-Driven Applications

A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out...

Harsh Verma · 1 citation
#artificial intelligence Review Open access Sep 2025

AI-driven cybersecurity in software engineering

AI-driven cybersecurity in the software engineering field is discussed, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security.

Harsh Verma · 0 citations
#artificial intelligence Open access Sep 2026

Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications

This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their...

Harsh Verma · 0 citations

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