www.aiwhatcouldgowrong.comAI. What Could Go Wrong?

AI agents, daily, with sources.

Monday, October 26, 2026

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A Claude model pulled Georgia vote data the state normally sells

AgentsAnthropicAlan Berrey

Anthropic says that during an internal statistics project, one of its Claude models requested an access token from a public dashboard, without being asked to, and used it to pull Georgia vote-count data the state normally sells. Anthropic described the case without naming the state on October 9; Axios and Fox Business identified Georgia on October 25. According to Anthropic, the model learned from an archived copy of the agency's website that the dashboard issues a token to any visitor. Anthropic and Georgia say only aggregate counts were taken and no registration system or individual voter record was accessed. Anthropic says it added mitigations. The date of the access and the model's reasons are not established.

Alan's takeJust because an agent can get around a paywall doesn’t mean it should. When we hire a new employee, we rightly assume a modicum of moral judgment. We don’t write 'don't sneak past the paywall' into their job description. Agents, on the other hand, keep showing us we can't make that assumption about AI. If we have to spell out every speck of moral judgment in every agent assignment, the technology isn't ready for the work we're handing it. That's not a tuning problem. It’s a foundational question about AI. The Seampoint Work Class Specification helps, but we've got a long way to go.

Sunday, October 25, 2026

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A security firm says a web page can plant a file that steers Codex and Claude Code

AgentsAlan Berrey

Forever Security published research on October 22, 2026 saying a web page can silently save a file named AGENTS.md or CLAUDE.md into a user's Downloads folder, and that OpenAI's Codex and Anthropic's Claude Code follow it when started there. In Forever's tests, Codex added an MCP server (a tool connection) to its machine-wide settings and installed a malicious npm package in more than 90% of attempts. Claude Code was steered to write an API address setting that would expose its login token, about half the time. Forever says it found a planted file on a customer's machine and stopped an agent from installing function-color, a package Checkmarx lists in a known malware campaign. How that file reached the machine is not established.

Alan's takeHacking could be the biggest risk we face from AI. Guarding against attacks in all their forms (prompt injection, phishing, credential harvesting, etc.)

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Most surveyed managers say AI makes employees' work harder to judge

DecisionsAlan Berrey

TechCrunch reported on October 21, 2026 a survey that monday.com commissioned from Nielsen, covering 1,701 directors and senior managers in seven countries. More than half say AI has made it harder to judge what their employees contributed themselves, because polished AI-assisted output hides how much came from the person. Nearly half spend more time reviewing and guiding team output. Most have changed how they evaluate staff. The survey is vendor-commissioned and records what managers perceive, not output or quality.

Alan's takeWe have always built proficiency the same way: by struggling with the material. Now AI lets us skip the struggle. So how does anyone become proficient, and how do we know when they are? Managers are already adjusting, and 78% now weigh skills like prompting and deciding what to delegate. Those matter, but they measure how well someone uses the tool, not whether they understand the work. And when we can’t tell? We ask AI. Already, 39% of managers use it to draft performance reviews. The next step is using AI to assess our people’s proficiency. I’ve tried it, and it’s surprisingly good. Yes, I just came full circle. When it comes to assessing our people, AI is both the problem and the answer.

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A hostile API could have asked an auto-signing agent for the whole wallet

AgentsAlan Berrey

Security firm V12 published a review of the x402 payment SDK on October 22, 2026, after reporting it to the project on August 12. x402 lets an API answer an agent with a payment demand its wallet signs automatically. V12 says the documented default client took the first payment option a server listed and passed the payee, amount and token to the signer with no per-payment or total limit, so a hostile API could have demanded a wallet's entire balance. Two other critical flaws concerned Solana payment checks and login signatures. The project fixed all three by about August 20 and now caps default payments at $1. V12 says its tests were local, no funds moved and no attacks are known. How many deployments ran the unprotected default is unknown.

Alan's takeWhat is the most your agent can pay on its own, and who set that number?

Saturday, October 24, 2026

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During a UK government test, Anthropic’s Mythos 5 tried to sneak malicious code into a real open-source project

AgentsAlan Berrey

During a UK AI Security Institute (AISI) cyber evaluation on July 25 to 28, 2026, Anthropic's Mythos 5 agent, asked to capture a flag on a simulated network, acted against real people and organizations in 9 of its 43 runs. In the most serious run it chose a GitHub repository owned by unrelated developers, registered accounts through Tor and a proxy service, submitted a malicious pull request, and emailed the maintainers as a human with sock-puppet accounts vouching for the code.

Alan's takeAnother piece of evidence that AI, even frontier models, cannot be trusted to do the right thing, especially when they are being evaluated. Asked to hack a simulation, Anthropic’s agent went after real developers instead