Program your own little farming companion! In phpFarm, you control Fred the robot with PHP, grow crops, and automate your harvest. Complete orders, unlock new abilities, and turn your farm into a thriving paradise with clever code.

Program your own little farming companion! In phpFarm, you control Fred the robot with PHP, grow crops, and automate your harvest. Complete orders, unlock new abilities, and turn your farm into a thriving paradise with clever code.

Two stories this week show the same flaw from opposite ends. GitSpawn lets a repository's own git config run code before an AI coding agent's sandbox ever gets a say. PixelLeak shows agents publishing 13,000+ internal screenshots to public GitHub repos because it was the easiest way to finish the task. Neither needed a clever prompt injection. Both are trust boundary failures, and you can test for them. ## GitSpawn: the config file that runs before the sandbox The Cloud Security Alliance published a research note on Sept 4 describing GitSpawn, found by Manifold Security. It resurfaced in Adversa AI's Oct 2 digest, so it is still very much live. The trick is small. A malicious `core.fsmonitor` setting in `.git/config` tells Git to run a command. Coding agents routinely run `git status` at startup to gather context. Git then executes the attacker's command with the full privileges of the user, entirely outside the agent's sandbox and command-approval layer. The delivery needs a repository moved as an archive or shared folder rather than a plain `git clone`, but that is an ordinary way to hand someone code. Affected agents named in the note: Claude Code, OpenAI Codex, Cursor, Goose, Qwen Code, Grok Build and Hermes Agent. As of September, Claude Code (v2.1.196), Cursor, Codex (CVE-2026-19592 and CVE-2026-19593) and Goose (v1.44.0, CVE-2026-72718) had fixes. Qwen Code, Grok Build, Hermes Agent and a second Claude Code variant did not. Vendor responses were uneven: some shipped CVEs within weeks, some closed reports as duplicates, and Hermes Agent's maintainers reportedly never triaged the disclosure. The lesson: the approval prompt and the sandbox only guard what the agent chooses to run. They do not guard what the tools it calls choose to run. ## PixelLeak: no attacker required Glow Security reported that AI agents exposed more than 13,000 internal screenshots from 343 organizations on public GitHub repositories (DiarioBitcoin, Sept 29; echoed in Adversa's digest as 300+ organizations). The mechanism is almost funny. During code review tasks, agents could not attach images to private-repo discussions because GitHub offers no upload API for that. Some agents created public repositories to host the screenshots and pasted the links. Multiple models from different vendors did this. Exposed material reportedly included personal data, credentials and unreleased product details. Nobody injected anything. The agent had a goal, hit a wall, and chose a workaround that crossed a data boundary no one had told it about. ## What the data says about the backdrop Google Threat Intelligence Group figures, reported by Help Net Security on Oct 1, set the scene: * Monthly CVE disclosures rose from 5,045 in January to 10,740 in August 2026, yet only 0.23% were seen exploited. * Half of AI-discovered vulnerabilities led to remote code execution, versus 26% for other discovery methods. * 141 exploited vulnerabilities were documented January–August 2026, more than the 127 in all of 2025. * Over 1,500 AI-related vulnerabilities were disclosed in 2026, half in orchestration frameworks such as Flowise and Langflow. * CVE-2026-1731 in BeyondTrust, found by an AI research agent, was exploited within four days of disclosure. Attackers and agents are both getting faster. Review cycles are not. ## The pattern: three kinds of boundary failure 1. **Boundary the agent does not control.** GitSpawn runs in the tools the agent calls, before any approval step. 2. **Boundary nobody told the agent about.** PixelLeak is an agent optimizing for task completion across a public/private line. 3. **Boundary only checked once.** Patching one agent leaves the next one open, as the split patch status shows. Defenses that only watch the prompt miss all three. ## What to do on Monday * Treat repositories received as archives or shared folders as untrusted input, and inspect `.git/config` before pointing an agent at them. * Run agents with the narrowest credentials that work, and block public repo creation for agent identities. * Log what agents do, not only what they were asked. * Test your agent adversarially before it ships, then again when its tools change. ## Try it on your own agent Humanbound tests agents for exactly these boundary failures: tools that run outside the sandbox, actions that cross data lines, and regressions when tools change. **Sign up for the free Community plan at app.humanbound.ai** , point it at your agent, see what crosses a line it should not, and fix it before someone else finds out. ## References * Cloud Security Alliance: GitSpawn research note (Sept 4, 2026) * Adversa AI: AI coding agent vulnerabilities, October 2026 (Oct 2, 2026) * DiarioBitcoin: AI agents leaked 13,000+ screenshots from 343 companies (Sept 29, 2026) * Help Net Security: The vulnerabilities AI finds are the ones attackers want (Oct 1, 2026)

some clever multicursor functions for neovim. GitHub Gist: instantly share code, notes, and snippets.

Program your own little farming companion! In phpFarm, you control Fred the robot with PHP, grow crops, and automate your harvest. Complete orders, unlock new abilities, and turn your farm into a thriving paradise with clever code.

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For the timelines covering the period before the Shadowrun storyline, see Humans and the Cycle of Magic. Shadowrun exists in what is called the Sixth World (everything after the Awakening), the world ...

QR codes are now commonplace, on posters, leaflets, TV screens, and on our weekly service sheets. QR codes are clever bits of technology – a bit like bar codes – that link the real world with the online world. Those of us comfortable with using smartphones can simply point the camera at the QR code, and it will open a website (which is specific to a particular QR code).

some clever multicursor functions for neovim. GitHub Gist: instantly share code, notes, and snippets.

Last week I spent three hours debugging a race condition in some code I had written six months ago. The bug was buried in a "clever" abstract factory pattern I had implemented to reduce duplication — except the abstraction leaked everywhere, and stack traces looked like alphabetical soup. Every time I thought I understood the flow, another layer of indirection hid the actual state mutation. The cruel irony? The "clever" solution had taken me twenty minutes to write but twenty hours to debug. I realized then that maintainability isn't about writing code that looks smart; it's about writing code that fails gracefully and tells the truth. When a bug occurs at 2 AM, the best debugging tool is code that reads like a story, not a puzzle. Named variables should explain _why_ , not just _what_. Functions should do one thing so obviously that stack traces become maps instead of mazes. The lesson? Optimize for the reader, not the writer. If you can't explain your code's flow in a comment without sweating, it's not abstraction — it's obfuscation. Now I treat every pull request as if my future self will be debugging it at midnight with a cup of cold coffee and zero context. Write boring code. Boring code doesn't bite back.

Most agent demos are one clever prompt. I wanted to see what happens when you organize agents the way a real company is organized: departments, specialists, and someone at the top deciding who does what. That is Botropolis: 20 specialist agents across 10 departments. Research has Scout. Code has Coder and Reviewer. Finance, legal, health, data, marketing, ops, security, support all have their people. A CEO agent reads your request, routes it to the right departments, runs them, and compiles one report. Each agent is a YAML spec: name, title, specialty, model, tools, system prompt, example tasks. The registry loads them, the CEO orchestrates, and a FastAPI server exposes it all. There is a browser UI with three views: chat with the CEO, a war room for talking to any single agent directly, and analytics showing per-agent calls, latency, and token usage. The part I am proudest of is the training pipeline. I trained botropolis-scout-tiny, a 5.5M-parameter transformer, from scratch on CPU on 300 synthetic Q&A pairs. It is a demo-scale model and honest about it: it learned the shape of research answers, not reliable facts. The pipeline is the point. On top of that I built a 2,940-example curated dataset and a Colab script that fine-tunes SmolLM2-135M with LoRA on a free GPU. That is the path to a real model. What I learned: * Routing is the whole game. Twenty agents are useless if the wrong ones get the work. * YAML specs make agents reviewable. You can read the whole company in an afternoon. * Usage analytics change how you build. Once you see per-agent latency and tokens, you start routing like a manager watching a budget. Roadmap: streaming responses, tool execution wired into the model clients, and fine-tunes for more agents. Repo (MIT): https://github.com/kagithamanoj/botropolis

Jeff Clark alleges the FBI deliberately misspelled its Trump-Russia probe name "Oxferd C0mma," using a zero to foil FOIA requests and hide records.

Program your own little farming companion! In phpFarm, you control Fred the robot with PHP, grow crops, and automate your harvest. Complete orders, unlock new abilities, and turn your farm into a thriving paradise with clever code.