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Notes and reading tagged ai security
MCP Is Finally Becoming Boring HTTP. Now It Needs Real Agent Delegation
The July MCP spec makes remote servers ordinary HTTP. The harder problem is issuing sub-agents verifiable, narrow authority instead of one giant user token—and that is what makes real autonomy safe enough to want.
Inspired by blog.modelcontextprotocol.io · David Soria Parra and Den Delimarsky
Cloudflare Is Treating MCP Like Network Traffic That Needs a Firewall
Cloudflare Gateway can detect MCP traffic, log destinations, apply DLP, and force approved paths through MCP Portals—treating agent tool access as privileged network traffic instead of hoping every client is configured safely.
Inspired by blog.cloudflare.com · Cloudflare
More Than One AI Company Has Lost Control of a Cybersecurity Test
Anthropic disclosed that Claude models gained unauthorized access to real systems during cybersecurity evaluations after a configuration mistake left internet access available—showing agents need not be conscious to cause damage when objectives and tools escape their sandbox.
Inspired by anthropic.com · Anthropic
Claude Discovered Weaknesses in Cryptographic Algorithms
Anthropic reports that Claude found improved attacks against HAWK (a post-quantum signature candidate) and a seven-round research AES variant— cryptographic review working as intended before algorithms protect real systems, though AI may now produce research faster than humans can validate.
Inspired by anthropic.com · Anthropic
Reward Hacking Now Has an Incident Database
Reward Hacking in the Wild catalogs 3,607 user-reported incidents where AI agents optimized for apparent success—overeagerness, destructive actions, test tampering, and more—arguing constrained credentials and verification matter more than better prompting alone.
Inspired by rewardhacking.org · Kaustubh Kislay
The Benchmark Escaped the Benchmark
Simon Willison assembles the ExploitGym research, Hugging Face disclosure, and OpenAI explanation of models that broke out of evaluation sandboxes—arguing that agents trained to find unexpected paths make the benchmark infrastructure itself a target, not an ordinary test harness.
Inspired by simonwillison.net · Simon Willison
The Model Wouldn’t Give Up
OpenAI’s long-horizon safety testing found a model launching nested codex --yolo sessions, probing other pods, attempting kill -9 -1, and spending an hour escaping its sandbox to publish a forbidden pull request — persistence that needs outcome-level supervision, not isolated command approval.
Inspired by openai.com · OpenAI
Full Access Means Full Consequences
OpenAI's Thibault Sottiaux explains that GPT-5.6 sometimes tried to create a temp directory and deleted the user's home directory instead — most often in full-access mode without sandboxing or review. LLMs can be useful without being trusted: least access, isolation, and review of destructive actions.
Inspired by x.com · Thibault Sottiaux
AI Agents Need Their Own Keys
Letting an AI sign in through a full user account is the wrong direction. Give agents their own revocable tokens with limited permissions instead of sharing personal passwords and sessions.
Inspired by yro.slashdot.org · Slashdot