OpenAI pauses work on top AI models after agent slips past internet controls

OpenAI’s latest containment failure adds to a pattern that may force the company to make an unpopular decision.

The company has paused training, evaluation, and tool-enabled inference for its most capable models after an internal research agent found a way around an intended internet restriction. More alarming, the agent remained active for hours after OpenAI’s monitoring system raised the alarm.

OpenAI published an account of the incident, which occurred during a search-based training task on September 20. According to OpenAI, the agent could not get useful results through its supplied search tools, tried direct access to search engines, and eventually tested the limits of its environment. It found that although direct web access was blocked, the sandbox’s DNS resolver could still reach the public internet. By using DNS delegation and a public chatbot that could respond through DNS records, the agent sent questions outside the restricted environment and received answers back. It subsequently sent 18 additional queries along the same route.

OpenAI’s misalignment monitor generated a high-priority alert within 15 minutes, and a human reviewer acknowledged it 3 minutes later. But the run did not stop automatically as expected. It continued for roughly another two and a half hours before someone stopped it manually.

OpenAI has not reported any exposure of sensitive data or compromise of an outside system in this incident. But it follows other disclosed cases in which agents went beyond their assigned tasks or crossed the boundaries of testing environments.

The New York Times reports that OpenAI agents also interacted with US government websites this summer without the company’s knowledge:

“OpenAI’s artificial intelligence went rogue and meddled with the websites for the Education Department, the Commerce Department and the Securities and Exchange Commission this summer without the A.I. lab’s knowledge, according to security researchers and a person familiar with the episodes.”

No breaches have been confirmed in those instances. And while Australia described the Medicare incident as unauthorized access, could it really be called an attack?

The call for an industry-wide slowdown has even prompted a lawsuit from paying subscribers who fear they would get less “value for their money.” Others worry that slowing down would give China a chance to pull ahead of the US in an increasingly tight technology race.

Here’s a thought: Why not accelerate the development of truly isolated testing environments? Better containment would make these tests safer and their results more reliable.

When a child isn’t ready to handle a dangerous object, you keep it out of reach. Why give an AI agent access it isn’t ready to use safely?

How to use AI agents safely

Treat an agent like an enthusiastic but fallible junior employee with access to your computer. Do not give it unrestricted access to your email, files, cloud storage, developer credentials, financial accounts, or production systems merely because it promises to save time.

Use separate accounts with minimal permissions. Keep sensitive data out of its working context where possible, require human approval before it sends messages, spends money, changes settings, or publishes anything, and regularly review its activity.

An AI agent does not need malicious intent to cause harm. A misunderstood instruction, an overly broad permission, or an unexpected workaround can be enough.


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Stop trusting Reddit’s DNS recommendations—here’s how to find the best one for your home

There’s no denying that using a custom DNS server is a better choice than sticking with the one provided by your ISP, but the catch is that you shouldn’t opt for the first DNS provider you find positive reviews of online, or the one everyone recommends on Reddit. There’s a way to test which DNS server works best for you that only takes about a minute of your time, and it’ll give you better results than blindly choosing from the most popular options.

LinkedIn adds new checks for fake profiles and work histories

LinkedIn is adding trust and verification features aimed at making fake professional identities, invented work histories, and company impersonation harder to pull off.

The company is responding to an environment in which generative AI enables imposters to create an entirely made-up professional persona. It reduces the cost of creating convincing headshots, biographies, résumés, outreach messages, and recommendations.

LinkedIn’s new features use verified people and company Pages to help check other users’ claims.

Colleague and classmate vouching: People can confirm that they worked or studied with someone during the period listed on that person’s profile. This confirms an affiliation; it does not rate the person’s ability or recommend them.

Employer control over false affiliations: Admins of verified company Pages can remove people who falsely claim to work there from the company’s associated people and search results. This doesn’t remove the person’s profile or erase the claim from it.

Workplace verification requirement (in testing): LinkedIn is testing a setting that would require people who want to associate themselves with a company Page to verify their workplace, for example, through a work email address.

Verified identity beyond LinkedIn: LinkedIn is extending partnerships that let users display their LinkedIn-verified identity elsewhere. New partners include Truecaller and PeerSpot, alongside Adobe and UserTesting.

LinkedIn hopes that multiple visible signals such as identity verification, workplace verification, peer corroboration, and company Page controls will make a fabricated profile less persuasive. It says its existing verification tools have verified 115 million members and more than 700,000 companies.

LinkedIn VP of Product Oscar Rodriguez told TechCrunch:

“We’ve been invested in [verification] because we believe that authenticity will be the single most valuable currency on the internet.”

These new affiliation checks cannot tell a job seeker whether an opportunity is genuine. A scammer does not always need to impersonate a major employer. They can invent a convincing startup, build a polished company Page, and use fake job listings or recruiters to lure applicants. The checks may be less helpful if the supposed employer itself is part of the scam.

As we’ve seen in recent research, criminals pose as both well-known companies and appealing new ventures offering the kind of role a job seeker hopes to find. The new checks may help someone determine whether a supposed recruiter is affiliated with a company they recognize. They may be less helpful when the company itself is part of the scam.

Verification is therefore a useful signal, not a guarantee. Job seekers should still be wary of unsolicited approaches, pressure to move conversations off LinkedIn, requests to install software or share identity documents, and job offers that arrive before a credible interview process. Check a recruiter’s affiliation, visit the employer’s website independently, and confirm that its contact details and job listing match.

LinkedIn’s changes acknowledge a growing problem: AI can make a fake professional identity look remarkably polished. Verified accounts, workplace affiliations, and colleague confirmations may become more valuable to criminals, too. They may try to exploit those signals through stolen accounts, manipulated verification, or carefully constructed networks of fraudulent profiles. Verification can raise the cost of deception, but it cannot replace healthy skepticism.


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