aiqbench takeAI Safety

This guide addresses the critical shift in application security required as agentic AI and LLM apps introduce emergent behaviors that bypass traditional code-based assumptions. It provides actionable frameworks for mapping attack surfaces on MCP servers and implementing runtime guardrails against novel threats like prompt injection and poisoned tool descriptions. By aligning with standards such as NIST AI RMF and OWASP AIMA, the content offers essential strategies for securing autonomous agents in production environments before they scale.

Sources coveringMarkTechPost, Viakoo, Analytics Vidhya, LessWrong

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How to Secure AI Agents, MCP Servers, and LLM Apps in Production

MarkTechPostAugust 3, 20264 min read

AI agents, MCP servers, and LLM apps break the core AppSec assumption that applications do what their code says. This guide walks through a practical see-fix-protect framework: a five-layer agentic AI attack surface map, a 12-point misconfiguration checklist, an evidence-based triage matrix, runtime guardrails, and system prompt hardening — plus a maturity self-assessment aligned to NIST AI RMF, OWASP AIMA, ISO/IEC 42001, and the EU AI Act. The post How to Secure AI Agents, MCP Servers, and LLM Apps in Production appeared first on MarkTechPost.

Agents, MCP integrations, and LLM-powered applications are entering codebases faster than most security programs can track them. Mend.io’s new practitioner guide, ‘Securing AI agents, MCP servers & LLM apps: A practical framework’, targets that gap. It is organized around three moves: see what matters, fix what matters faster, protect AI in production and ships seven reusable artifacts. AppSec was built on one assumption: applications do what their code says. Agentic AI breaks it.…

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