AI Agents Broke Into 100+ Sites on Autopilot

A hacker breached more than 100 websites using an automated system built on Cairn, an open-source AI platform that describes itself as a general-purpose problem-solving engine, according to findings by AI security firm Norforge — as reported by The Verge.

With Cairn, a user sets a start and end point; AI agents handle the steps in between. The hacker weaponised that structure for cyberattacks, leaving the agents to do most of the work while they stayed largely hands-off.

Among the confirmed targets: John Deere, SharkNinja, and Cummins. The hacker — the most active of more than 20 likely-malicious Cairn instances Norforge identified — targeted companies across the US, Canada, Australia, and Japan. Successful breaches took roughly 23 hours each. The attacker's instructions reportedly ran to fewer than 100 Chinese characters.

The companies are downplaying the damage. John Deere says there is no indication sensitive data was accessed. SharkNinja says the breach hit a standalone product-support site and has been contained. Cummins says it is investigating a potential attack on a third-party vendor's site.

Cairn's creator has said the platform "was never a hacking tool" and that misuse is a human decision. That is probably true — and completely beside the point. When a general-purpose AI system can be pointed at 100 targets and told to figure out the rest, the blast radius of one motivated attacker gets a lot wider.

Watch for follow-up from Norforge on how many of the 20-plus malicious instances have been shut down. That number matters more than any company statement.

Adobe Lightroom mobile — a photo editing app for iOS, iPadOS, and Android — can now open closed eyes in photos using generative AI (AI that creates new content rather than just adjusting existing pixels). When the app spots a blink in a group shot, it places a one-tap shortcut over the image. Tap it, and the eyes are replaced with a version that matches the lighting, pose, and — using other photos of that person in your library — the correct eye colour, size, and shape.

This is a meaningful step up from older tools like Photoshop Elements, which sourced replacement eyes from a separate reference photo with inconsistent results.

The feature is available now on the free version of Lightroom mobile, with two free uses before a Creative Cloud subscription is required. Similar fixes exist in full Photoshop, but they take more steps and are not free.

More details at The Verge.

Honest read: Worth trying if group photos are part of your life — two free attempts is enough to know whether the quality justifies a subscription.

What it is: A technique that teaches an AI customer-service agent to improve itself by studying its own past mistakes, without retraining the underlying model.

What's new: Most efforts to boost agent performance either swap in a more powerful model or do expensive retraining. ALTK-Evolve adds a block of short behavioural rules to the agent's system prompt (the hidden instructions that shape how an AI responds) instead.

How it works: The system runs an agent — built on GPT-4.1, an older non-reasoning model — through training tasks on τ²-bench (tau-squared-bench), a benchmark that simulates real customer-service conversations with strict pass/fail outcomes. A second pass of GPT-4.1 then reads those transcripts and distills them into plain-English rules: authenticate before acting, confirm changes before executing, break down refund amounts clearly. Those rules get appended to the agent's prompt for future conversations. No weight updates (changes to the model's internal values) or human annotation are needed.

What it can't do: Results are on the retail domain only; the same approach on the airline domain in the same benchmark is not yet top-ranked. The technique has not been tested at frontier model scale, and all evaluation was run by IBM Research, not an independent party.

Who should care: Developers building customer-service agents on a budget — this suggests you may not need the most expensive model if you give it a well-structured playbook.

Read it: IBM Research on Hugging Face

TorchNeuron is a new PyTorch plugin (PyTorch is a widely used framework for building and running AI models) that lets you run and train models on AWS Trainium — Amazon's own AI chips designed for cost-effective training — using the same code you would write for any other hardware. Previously, running models on Trainium required working through a separate library with significant workarounds; TorchNeuron makes Trainium behave like a standard device, so standard Hugging Face tools work on it without modification.

Available now in public beta. Install with:

pip install torch-neuronx

You will need access to an AWS Trainium instance — this is not a laptop tool. Costs depend on your AWS usage. 89% of the top 100 most-used text-generation model architectures are supported, according to Hugging Face's own figures.

Full details and code examples at the Hugging Face blog.

Worth knowing: This requires AWS Trainium hardware — there is no way to use TorchNeuron on a standard GPU or consumer machine.

The European Commission has published a study under the Digital Services Act (DSA) — the EU law that sets obligations for large online platforms — mapping how financial scams operate across social media, search engines, and app stores. Commissioned in 2025, the study identifies six scam categories, with investment and romance scams flagged as the fastest-growing in Europe. It also finds that generative AI is lowering the barrier to entry for organised scam networks.

The practical upshot: platforms covered by the DSA will face growing pressure to improve identity verification, ad screening, and content moderation. Read the study here.