When running AI models on your personal hardware using a platform like Jan, Ollama, or LM Studio, your messages, documents, and chat history do not leave your device and aren’t sent to someone else’s cloud servers. If you’re worried about an AI company selling your data without your consent, or if you don’t want to end up with your credentials leaked in the next data breach, going local is the smart move.
Just this January, SentinelOne and Censys found 175,000 publicly exposed Ollama hosts that could be used by any attacker with an internet connection to execute code and connect to third-party services from a user’s credentials and hardware. If you want to run AI locally, you have to be very careful with where you get your models from and what they have access to. Here are some tips to help you get it right.
Keep your model server on localhost
Sometimes, setup guides will suggest that you do this anyway, so that you can access your local AI model from other devices on your network, like a smartphone or laptop. It also comes up when people try to run AI models on VPS servers or Network-Attached Storage (NAS) devices. But this will put your data and workflows at risk, so if you did something to change the default server configuration of your model runner, make sure to change it back now:
For LM Studio, toggle off “Serve on Local Network."
You shouldn’t expose your AI model to your public IP address on the internet. But what if you still need to share model access to your other devices remotely? Normally, people enable port forwarding on their routers to configure access to their resources and data from a remote location. But you should never use this approach to configure remote access to AI models or runners on your local machine.
Update your AI runner as soon as patches land
In May 2026, Cyera uncovered a new Ollama vulnerability that let attackers steal chunks of your data and credentials using unauthenticated API calls. The flaw, called “Bleeding Llama,” had a CVSS rating of 9.3 out of 10. At the time, it put around 300,000 publicly exposed Ollama servers at risk until it was addressed in patch version 0.17.1.
AI models based on older deep learning models like PyTorch are often downloadable as pickle files, with extensions like .bin, .pt, or .pkl. But due to the nature of the Python pickle file format, these model files can be altered to execute malicious code as soon as you try to load them using your runner.
To avoid data breaches or unauthorized access, you should only download LLMs that come packaged in newer file formats like .safetensor or .gguf. These file formats store your data in numerical format, which makes malicious code execution impossible as a model loads. If a particular model is only available as a .pt or .pkl file, I’d just skip it. There are plenty of newer-version LLMs that use more secure file formats.
Download models from publishers you can verify
For better safety, download model files uploaded from official accounts managed by major model developers only. For example, Google, Mistral, Meta, and Qwen (Alibaba) all have separate organizational accounts with a verified badge on Hugging Face. Verification badges indicate that a company account is really owned and administered by that company, because the uploader would have had to use an official company email address to log in and upload the model files. You can see the Advanced Security section of Hugging Face’s documentation for more details on how verified badges work for enterprises.
But the attacks have grown more sophisticated since then and may even target obscure local AI platforms and Python packages. Attackers have gone as far as to breach official GitHub repositories and Python Package Index (PyPI) uploads. TrendAI reported one particularly disturbing instance where malicious code was inserted directly into the official PyPI package of LiteLLM, an open-source AI gateway that lets you call hundreds of LLMs from a single API. Positive Security also discovered malicious Python packages uploaded to PyPI as Deepseek lookalikes.
Double-check packages your model tells you to install
I already covered how Python packages are corrupted to install malware as soon as you run them on your system. But it’s not just the LLM files and AI tools that you need to watch out for. When you ask AI agents to write code or execute tasks, they also install and run any packages or dependencies needed to complete that job. And because AI models are prone to hallucination, agents will often just make up package names that don’t exist. A recent study that analyzed 16 models across 576,000 code samples found that open-weight LLMs do this 21.7% of the time, while frontier AI models have a lower hallucination rate of 5.2%.
The best way to avoid these attacks is to limit what your AI agent can install and run without your approval. You can either choose to manually approve each software package before the model installs or runs it, or you can whitelist certain trustworthy repositories that aren’t likely to contain malware. Either way, make sure to review your model’s log to see what pip install and npm install commands it runs to avoid unauthorized installations.
There are multiple ways to regulate how much access an AI agent has. The first is to run your AI workflows inside a Dockerized container that can’t make direct changes to your system files. Beyond that, you can also restrict permissions by changing the default configuration of your agentic framework, like OpenClaw or Hermes. OpenClaw lets you choose between three default permission profiles, including ask, deny, and allowlist, which can be further scoped to specific workflows and services. Hermes also lets you set up a similar allowlist (whitelist) or restrict tool usage per cron job.
Switch on local-only mode
When you keep your AI workflows local, your entire chat history, along with any credentials, secrets, or API tokens you may have shared with your model, exist in plain text on your local drives. If someone managed to access your device physically, they could take all of it. Apps like FileVault, BitKocker, or LUKS can encrypt your hard drive so that your chat history can’t be read in plain text without an encryption key to decode it. Use them to avoid the risk of exposure if your device is stolen or lost.
A few local AI platforms to get started with
Ollama: An open-source model runner for macOS, Windows, and Linux. Large model library and a simple desktop app that most other local AI tools can plug into.
Jan: An open-source, Apache 2.0-licensed ChatGPT alternative that runs fully offline on Windows, macOS, and Linux.
Open WebUI: A browser-based offline chat interface that can connect to Ollama and make the UI more accessible. Pair it with a mesh VPN, and your whole household can use one AI server safely.
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