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CVE-2026-58073: Veeam and Terraform MCP Critical Flaws Patched

CVE-2026-58073: Veeam and Terraform MCP Critical Flaws Patched

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 6.5 The Hacker News

HashiCorp, Veeam, and the Django Software Foundation have patched 11 vulnerabilities, with the most critical being a CVSS 10.0 cross-tenant token reuse flaw in Terraform's MCP Server that allows one user's Terraform token to be hijacked for subsequent users' requests. The Veeam Service Provider Console carries a 9.5-rated unauthenticated credential theft bug affecting multi-tenant backup infrastructure. The Terraform MCP Server flaw is particularly notable from an AI security perspective as it directly affects the Model Context Protocol layer connecting AI assistants to infrastructure tooling.

Microsoft MDASH Brings AI-Powered Windows Vulnerability Discovery

Microsoft MDASH Brings AI-Powered Windows Vulnerability Discovery

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 BleepingComputer

Microsoft has deployed MDASH (Multi-model Agentic Scanning Harness), an AI-powered agentic system that autonomously scans Windows binaries for vulnerabilities and validates findings through multiple AI models before human engineer review. The accelerated discovery pipeline means defenders will see a higher volume of Patch Tuesday fixes, compressing patch deployment windows and increasing pressure on enterprise patch management processes. Simultaneously, the same AI-accelerated vulnerability discovery capability is available to adversaries, raising the risk that threat actors identify and weaponise flaws faster than Microsoft's pipeline can remediate them.

Microsoft MDASH Discovers 16 Windows RCE Flaws

Microsoft MDASH Discovers 16 Windows RCE Flaws

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.8 The Hacker News

Microsoft has disclosed MDASH, a multi-model agentic AI scanning system that autonomously discovered 16 vulnerabilities patched in May 2026's Patch Tuesday, including two critical RCE flaws. The system orchestrates over 100 specialised AI agents in a structured pipeline covering auditing, debating, and proof-of-exploitability stages. MDASH represents a significant shift in how AI is being deployed offensively and defensively within the vulnerability research lifecycle, with direct implications for how agentic AI systems are trusted, scoped, and governed.

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