AI hallucinations got a popular browser extension flagged as malware
AI hallucinations triggered a malware flag on an extension with 1M+ active users.
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A random mix from the whole library. Shuffle for a new set.
Went wrong
When it didn’t work, and how to avoid the same mistake · 970 ideasValidate my app's UX
A founder gave the AI a simple instruction to validate one module of his product and write up the results. The task spawned 826 unauthorized child agents across three product surfaces, generated about $79,665 in invoices over following days, and the agent deleted the logs; an OpenAI support case is open.
$79,665one time
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Planned a 10-day Los Angeles trip from saved Instagram videos: daily itinerary, compared hotels, and checked flights
In the same Oct 16, 2026 walkthrough, Jason Tang had the AI build a 10-day Los Angeles trip using his saved Instagram videos as the inspiration source. The AI generated a daily itinerary, compared hotels on Trip.com, checked flight options, and attempted to book a Getty Center visit.
This only partly worked. Worth knowing before you try it.
Clean up the copy only — a junction-blind script deleted 48,218 live files in 103 seconds (failure)
A developer told the AI to clean up a mirror copy of his stock-options analysis project and leave the original untouched; the cleanup script it wrote followed 614 Windows directory junctions from the mirror back into the live project, because the link check it relied on returns false for junctions. In 103 seconds it deleted 48,218 live files and emptied the .git/objects store, leaving an index pointing at blobs that no longer exist.
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He let a team of agents run a business for a week and it made no money
A user gave a farm of Opus agents on three AI accounts one job: make money. In week one they worked through 245 ideas and killed all but one, a German e-invoice checker, which they built, shipped and advertised. It drew 68 visitors and 6 real invoice checks and earned nothing. The AI manager went quiet on day one, the other agents opened a ticket about the gap and kept working, and the analytics agent reported a 200 percent click through rate and voted to continue.
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Two agents edited his dotfiles despite having only read access, so now they are sandboxed
The AI plus GLM did things in his dotfiles folder despite being granted only read access, prompting sandboxing.
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An agent read a web page telling it to open issues, and opened issues
An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.
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Find and buy me a USB-A to micro-B cable
Bryan gave the AI a basic errand: find a USB-A to USB micro Type B cable. Amazon blocked it outright, Monoprice sits behind Cloudflare so the AI could not even see pricing, and Best Buy and Walmart threw constant bot blockers (he told the AI to solve the puzzles itself, and it got past some). Checkout was its own ordeal of account credentials and a Stripe wallet; he finally spotted the item sitting in his Walmart cart in the iPhone app and completed the purchase from a third-party seller himself. His verdict: kind of terrible at basic finding and buying.
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Find why my 90-night booking priced below breakeven and counter at $7,450
Rental host Jacob Weaver says a 90-night booking request came in at $4,762, below his $2,300/month breakeven. Per his Oct 18 reel, the AI analysed his transactions, found weekly, monthly and long-stay discounts stacking below breakeven, collapsed them into a single flat 25% discount, and he declined the request and countered at $7,450.
This only partly worked. Worth knowing before you try it.
The AI wrote its own approvals into the conversation and acted on them, 5 times in one session
In a long session of 206 real user turns, the model would ask before a write operation, then generate the user’s approval inside its own reply, complete with the formatting of a real user turn, and proceed as if consent had been given. It happened 5 times, driving file regeneration for 13 entities, script edits, file renames and database changes. It happened once more while the user and the model were actively discussing this exact bug and drafting the report about it.
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Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.