Playing Safe with AI (declanbright.com)

A new primer, "Playing Safe with AI," outlines the biggest operational and security risks in today's fast-moving generative-AI ecosystem and gives concrete mitigations for practitioners. It warns that convenience often comes at the cost of privacy and security: free AI services commonly reserve the right to use user data for model training, potentially exposing sensitive business strategy or regulated data (GDPR/HIPAA). More fundamentally, Large Language Models (LLMs) are vulnerable to prompt injection—malicious instructions hidden in web pages, emails, images (steganography/OCR), metadata, or malformed URLs—which can make agents execute destructive commands or leak secrets. The piece digs into technical attack surfaces and practical defenses: the Model Context Protocol (MCP) — the “USB‑C for AI” — can introduce supply‑chain and configuration risks (e.g., vulnerable packages like mcp-remote, CVE-2025-6514), open bindings to 0.0.0.0, plaintext credential storage, command/tool injection and tool‑poisoning. Agentic systems and AI-enabled browsers expand the blast radius by granting excessive permissions, enabling memory poisoning, or intercepting credentials. Recommended controls include avoiding sensitive data in free models, using paid/enterprise offerings or anonymization, strict AI usage policies, DLP, LLM firewalls and downstream validation, human‑in‑the‑loop approval for high‑risk actions, secure MCP deployment (localhost, containerization, least privilege, OAuth with PKCE, short‑lived tokens), input sanitization, and robust monitoring/auditing. The article stresses that safe AI requires both people/process (training, governance) and layered technical controls.