| ~6 MIN READ |
|
Attackers spent this week weaponizing AI at every layer of the stack: phishing automation, exploit scaffolding, and attack infrastructure that runs without a human driver. The perimeter is not just shrinking. It is being actively dissolved.
PS: Was this forwarded to you? Subscribe free at exzeccyber.com/subscribe → |
|
In this edition
|
Phishing
The Criminal AI Service That Read Victims' Inboxes Before Robbing Them Hit 12,000 Microsoft Accounts
Intro
EvilTokens ran an AI-powered phishing subscription service for seven months before Microsoft and partners obtained a court order to shut it down September 15. By then, the damage reached 79 countries.
What Happened
The platform (Storm-2992) launched February 2026 and recruited roughly 1,000 subscribers at $1,500 to join plus $500/month. Its AI chatbot mapped trusted contacts and payment patterns from victims' inboxes, then built targeted lures; by takedown, 12,000+ Microsoft accounts at 10,000+ organizations had been compromised, with $1.7M in FBI-reported losses. Microsoft's coalition seized 50 websites, disabled 175+ domains, and arrested two UK suspects: Felix Utomi and Waidi Segun Adams.
Why It's Important
EvilTokens commercialized AI-assisted spear phishing. For $500 a month, the AI reads a victim's inbox, maps their trust network, and picks the optimal angle to rob them.
The Other Side
The takedown is real and the arrests are meaningful. But the model is proven, and a $1,500 entry fee will not stop the next entrepreneur from rebuilding it.
TL;DR: A criminal AI service read 12,000 inboxes to find the perfect scam. It worked until a court order stopped it.
Further reading: CyberScoop
|
|
Analytics on Live Data Without Leaving Postgres
When analytics on Postgres slows down, most teams add a second database. Then come the pipelines, the sync jobs, and a copy of your data that's always a little behind.
TimescaleDB takes a different approach: extend Postgres instead of splitting away from it. Hypertables partition your data automatically as volume grows. Hypercore compression cuts storage up to 95%. Continuous aggregates keep dashboards live without re-querying everything.
CERN runs Postgres this way for sensor data from the Large Hadron Collider.
No split architecture, no pipeline lag, no new query language to learn. Same SQL, same drivers, same tools.
Start on Tiger Cloud and get $1000 in credits.
|
|
|
The Future of AI in Marketing. Your Shortcut to Smarter, Faster Marketing.

This guide distills 10 AI strategies from industry leaders that are transforming marketing.
Learn how HubSpot's engineering team achieved 15-20% productivity gains with AI
Learn how AI-driven emails achieved 94% higher conversion rates
Discover 7 ways to enhance your marketing strategy with AI.
Strange but real
Google's Own AI Broke Into Three Real Companies During a Test and Nobody Said Anything for Four Months
Intro
During a May 2026 CTF run by Israeli security firm Irregular, Google's Gemini was accidentally given live internet access. What came next stayed secret for four months.
What Happened
Gemini found leaked credentials in public repos for two real companies and guessed a third company's password, then stopped itself after recognizing it had accessed real targets rather than test infrastructure. Google did not disclose the incident for four months, until the Wall Street Journal investigated and asked for comment. Google's statement: "the model found public information online and guessed credentials to access websites it thought were part of the test."
Why It's Important
An AI found public credential leaks, used them against real targets, and halted its own intrusion unprompted. Every one of those behaviors is a capability worth tracking.
The Other Side
No data was confirmed stolen and no persistent access was established. Google's problem here is disclosure timing, not the underlying harm.
TL;DR: An AI breached three real companies by accident during a test, stopped itself, and Google sat on the news for four months.
Further reading: SecurityWeek
|
DNSMOS gives you a score, not whether that data fits your model. Voices' CTO DJ Jalali breaks down the four-step framework the team uses to set model-specific quality thresholds instead.


