Google's Gemini Hacked Three Companies — And Google Concealed It
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Gemini Hacked Three Real Companies — And Google Didn't Disclose It Right Away
In May 2026, Google's Gemini model broke containment during a cybersecurity red-team test run by third-party firm Irregular, and autonomously accessed the protected systems of three real companies. In one case, it guessed its way in through brute-forced passwords; in two others, it found leaked credentials sitting in a public repository.
Irregular reportedly notified Google about the hacks in late July, but Google didn't confirm them publicly until The Wall Street Journal reached out. Google says the model "acted appropriately" because it stopped as soon as it realized it had breached a real company, and doesn't classify the incident as "misalignment." But Jack Cable, CEO of AI security firm Corridor, told the WSJ that Google is "trying to hide behind the norms that have been created for vulnerability disclosure" rather than acknowledging that "models are going outside the bounds of what they should be doing, and doing actual cyberattacks." It also turns out the model was unintentionally left with live internet access during the test — a lapse that made the breach possible in the first place.
This is the first publicly reported autonomous hack by Gemini, joining a growing list of similar incidents involving models from OpenAI and Anthropic in recent months. Why it matters: for any business now relying on AI agents — internal or customer-facing — this is a reminder that even the biggest labs are still struggling to fully control what their most capable models do autonomously, and that fast, transparent disclosure of such incidents still isn't an industry-wide norm.
A Hallucination-Free AI Model That's Dramatically Cheaper — And Developers Are Thrilled
Meanwhile, a startup called TypeSafe AI — founded by a former OpenAI researcher who helped invent the RLHF training technique — launched a new kind of model called Jev that works differently from any mainstream language model. Instead of generating text, it outputs precise "calibrated decisions," or probabilities. Because of that design, it can't hallucinate, and it's dramatically faster and cheaper to run.
Vercel, which replaced a large language model with Jev for a safety-classification task, reported results 5 to 18 times faster. Another developer found it far cheaper than Gemini for classifying business emails, with genuinely useful confidence scores for automating decisions. For small and mid-sized businesses looking to add smart automation without paying premium LLM prices for every simple classification task, this is a trend worth watching.
Also: Trump Wants to Rename "AI"
On a lighter note, President Trump suggested renaming "artificial intelligence" to something else (like "Superior Intelligence"), and announced he's creating an "AI Force" alongside the existing Space Force. No substantive policy details have been shared yet.