Topic
AI SECURITY
Security for AI agents and LLM apps - prompt injection, tool permissions, audit logs, sandboxing, and rollback.
11 resources - 11 posts
Blog Posts
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Prompt Injection Is Really Role Confusion
New role-confusion research explains why prompt injection keeps surviving better prompts. Models do not reliably perceive which text is instruction, tool output, user content, or their own reasoning.
8 min read
Prompt Injection is Role Confusion - New ICML Research Explains Why LLMs Can't Tell Friend from Foe
New research from MIT reveals that LLMs identify speakers by writing style, not by tags - meaning attackers who sound like the system effectively become the system. The findings explain why prompt injection remains unsolved.
7 min read
Zero-Touch OAuth Is the MCP Feature Enterprises Were Waiting For
MCP's new enterprise-managed authorization flow is not just less login friction. It moves agent tool access into identity, policy, and audit systems enterprises already understand.
8 min read
Security Agents Need Repro Harnesses, Not More Scan Prompts
Anthropic's open-source vulnerability harness shows where AI security work is going: reproducible exploit loops, separate verification agents, and patch receipts.
9 min read
AI Agent Containment Needs a Capability Ledger
Anthropic's Claude containment writeup points to the next security layer for coding agents: deterministic capability ledgers, not another approval prompt.
9 min read
Spreadsheet Agents Need Permission Ledgers
The ChatGPT for Google Sheets exfiltration report is not just a spreadsheet bug. It is a warning about agentic office tools: permissions need to be action-scoped, logged, revocable, and visible.
8 min read
The Agent Security Checklist I Use Before Connecting Tools
Before an AI agent gets tools, files, APIs, MCP servers, or deployment access, decide what it can read, write, call, log, and roll back.
8 min read
Permissions, Logs, and Rollback for AI Coding Agents
AI coding agents become safer when permissions, logs, and rollback are designed as one system. Here is the operating loop I would put around any agent that can edit code, run tools, or open pull requests.
9 min read
Prompt Injection in Agent Apps: The Practical Version
Prompt injection stops being an abstract LLM risk once an agent can call tools. The practical defense is data boundaries, structured handoffs, tool guardrails, and approval gates around side effects.
8 min read
AI Security Scanners Move the Bottleneck to Triage
Anthropic's Project Glasswing update is a useful signal for developer teams: AI can find vulnerability candidates faster than humans can verify, disclose, patch, and ship them.
8 min read
Open Source Has a Bot Problem: Prompt Injection in Contributing.md
AI coding agents now read repository docs, config, issues, and comments before opening pull requests. That turns CONTRIBUTING.md and AGENTS.md into part of the security boundary.
8 min read
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More on AI Security
- - Tools Directory - dive deeper across the Developers Digest knowledge base
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