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Clinejection: How a GitHub Issue Title Compromised an AI Coding Assistant Used by 5M Developers

TL;DR In December 2025, Cline — an AI coding assistant with over 5 million users — gave an AI agent (Claude) write access to triage GitHub issues, including permission to run shell commands. A misconfigured trigger condition let any GitHub user invoke the workflow. What followed was a four-hop supply chain compromise that ended with a malicious npm package silently installing a second AI agent on user machines. I broke down the full chain in video form: Watch the episode Below is the chain, hop by hop. Hop 0: The setup The triage automation was configured with broad tool permissions and a trigger condition open to any GitHub user — not just contributors. That second part is the root cause: it opened the trigger to unauthenticated input. (Exact config values are in the Confirmed Artifacts section below.) Hop 1: Prompt injection via issue title The issue title itself was never sanitized before reaching the model. No first-party source has published the exact injected payload verbatim — GHSA doesn't disclose it — so any reconstruction here is illustrative, not confirmed. What's confirmed is the mechanism: an untrusted string reached the model with tool access already granted. Hop 2: Cache poisoning The injected instruction deployed a tool (multiple independent postmortems — Snyk, Cloud Security Alliance — name it "Cacheract") that flooded the CI cache with over 10GB of junk data, evicting legitimate entries through standard LRU eviction. Hop 3: Nightly workflow inherits the poisoned cache The nightly release workflow restored that poisoned cache around 2 AM UTC and ran inside it — handing over three publish tokens. (Names confirmed across GHSA and multiple independent sources — see below.) Hop 4: Publication cline@2.3.0 went live on npm with a postinstall script that silently installed a second package globally — an AI agent, installed by an AI agent, with no user consent. This line is a direct quote from Cline's own security advisory, not a reconstruction. It's the st

2026-07-19 原文 →
AI 资讯

Apple Music is getting a price hike

Apple Music is more expensive now. In the US, an individual plan now costs $11.99 per month, a $1 bump up from the previous $10.99 price. A family plan now costs $19.99 per month, up from $16.99, and a student plan costs $6.99 per month, up from $5.99. Apple, in a statement to Music Business […]

2026-07-18 原文 →
AI 资讯

WhatsApp Automation for Small Businesses in 2026: AI Replies, Lead Capture & Tiered Commissions

Your customers would rather message you on WhatsApp than fill in a contact form. That's fine at ten conversations a day. At a hundred, messages get missed, nobody knows which rep is on which deal, and at month-end somebody rebuilds the commission sheet by hand and gets it wrong. The usual answer is a $49–$499/month WhatsApp SaaS platform, priced per seat, with your customer data living in someone else's database. This post is the other answer: the same workflow on Google Sheets + Apps Script — and the one piece I see teams get wrong every single time, with the code to fix it. Where DIY WhatsApp automation actually breaks It isn't the messaging. Wiring a WhatsApp webhook into a sheet is a couple of hours of work, and I've written that build up separately — the webhook, the AI reply, and the lock that stops two reps chasing the same lead are all in Build a WhatsApp Sales Inbox in Google Sheets . I won't repeat it here. The part that breaks is the commission math . Someone writes =IF(revenue>10000, revenue*0.08, revenue*0.05) into a column, and three things kill it: A single sale spans two tiers — the formula charges the whole amount at one rate. The tiers change in July , and now every historical row recalculates at the new rate. A customer refunds in August on a sale from June, and nobody can unwind it without breaking the audit trail. So that's what this post builds: a tiered commission engine that survives rule changes and refunds. 1. Put the tiers in a table, never in a formula This is the whole trick. Make a Commission Rules tab, one row per rule: rule_id | rep_id | effective_from | effective_to | tier_1_cap | tier_1_pct | | | | | tier_2_cap | tier_2_pct | tier_3_pct --------+----------+----------------+--------------+------------+------------+----------- R1 | ALL | 2026-01-01 | | 10000 | 0.05 | | | | | 50000 | 0.08 | 0.10 R2 | rep_ayse | 2026-06-01 | | 10000 | 0.06 | | | | | 50000 | 0.09 | 0.12 rep_id is either a specific rep or ALL (the house default). Percenta

2026-07-17 原文 →
AI 资讯

Google is renaming NotebookLM to Gemini Notebook

Google is giving its AI note-taking app a new name. The company announced on Thursday that NotebookLM is becoming Gemini Notebook, but will remain a standalone app even as it integrates more deeply across Gemini and Google Search. Google first revealed Gemini Notebook - then called Project Tailwind - in May 2023 before widely releasing […]

2026-07-17 原文 →