MAI-Cyber-1-Flash handles up to 90 percent of vulnerability detection tasks independently and enables, in combination with other models, cost savings of 50 percent compared to previous MDASH configurations.
Project Perception combines multiple AI models in an agent-based system to identify vulnerabilities, simulate attacks, detect threats, and automatically develop remediation plans.
Cisco’s Antares model family enables security teams to analyse known vulnerabilities across 500 entries in approximately 15 minutes on a single GPU for under one US dollar — locally and without data transfer to the cloud.
Claude Mythos identifies critical security gaps at scale – Anthropic limits access through Project Glasswing and offers a guardrailed parallel model called Claude Fable 5.
AI-Infra-Guard addresses the fragmented attack surface of AI agents through layer-specific security paradigms: rule-matching for infrastructure, LLM audits for protocols, and behavioral testing for agent conduct.
The US government blocks two high-performance Anthropic AI models for foreign nationals over concerns about a workaround to security restrictions — a step Anthropic criticizes as non-transparent and technically unjustified.
TrendAI leverages Anthropic’s Claude model in Project Glasswing to automate source code analysis, enabling faster identification and coordinated disclosure of vulnerabilities in critical software.
An AI agent identified 21 zero-days in FFmpeg, while Chrome 149 sets a record with 429 patched vulnerabilities — a sign of growing attack surface discovery through automated analysis.
