Topic
Prompt injection in LLM and agent applications - untrusted content, tool-output attacks, guardrails, and practical mitigations.
7 resources - 6 posts, 1 guide

Copilot for Word AI worm: hidden prompts in a Word document hijack Copilot, alter financial data and self-propagate across documents. What HN thinks.

Prompt injection is role confusion: models do not reliably tell instructions from tool output, user content or their own reasoning, so better prompts fail.

Prompt injection through a bank transfer memo: how a €0.02 transfer compromised a banking AI assistant, and the five defense layers agent builders need.

The ChatGPT for Google Sheets exfiltration report shows spreadsheet agents need permission ledgers: action-scoped, logged, revocable, and visible permissions.

Prompt injection in agent apps is an architecture problem. Practical defenses: data boundaries, structured handoffs, tool guardrails, and approval gates.

Prompt injection in CONTRIBUTING.md and AGENTS.md: AI coding agents read repo docs before opening PRs, so those files are now part of the security boundary.
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