AI Assistants Nearly Exposed My Entire Home Network to the Internet
A seemingly simple idea — to streamline the management of a home network and enhance its security — unexpectedly turned into a sequence of near-catastrophic mistakes, all triggered by the advice of popular AI assistants. Instead of saving time and reducing risks, a journalist from Cybernews who relied on chatbot guidance received recommendations that could have exposed his local services to the entire internet.
The experiment began with a reasonable goal: to replace IP addresses with human-readable domain names and secure unencrypted HTTP connections with TLS. The setup itself was typical — pfSense acting as a firewall, TrueNAS serving as storage, and a Proxmox hypervisor hosting virtual machines and containers. Rather than configuring everything manually, the owner chose to delegate the task to AI — and therein lay the core problem.
Almost all major language models — including ChatGPT, Claude, and Gemini — unanimously advised publishing DNS records openly, linking subdomains directly to the home IP address. In doing so, they effectively recommended exposing internal components — from pfSense to TrueNAS — under their real hostnames, while also suggesting the unnecessary opening of ports 80 and 443. From a technical standpoint, this approach encouraged users to make their critical services publicly accessible, rendering them easy prey for automated scanners and bots.
Later, when confronted with the potential dangers of such advice, the assistants “reconsidered” and conceded that TLS within a local network could be configured more safely. Yet none initially proposed the proper and widely adopted solution — issuing wildcard certificates via a DNS-01 challenge, which allows full encryption without opening any ports at all.
When the journalist moved on to configuring NGINX Proxy Manager — a tool for routing traffic and automating TLS certificate issuance — the AI once again provided flawed guidance. While warning against executing unverified scripts from the internet, Gemini generated its own script riddled with two critical vulnerabilities. First, it launched the container under the root user, creating a serious risk of sandbox escape. Second, it connected to a MariaDB instance using default credentials — a reckless setup that, if reused blindly, could lead to full system compromise.
In many instances, the assistants merely agreed with the user’s statements without clarifying crucial details or inquiring about the architecture of the home lab. For example, when faced with issues involving Debian containers on Proxmox, one assistant simply suggested switching to a full virtual machine — a more resource-intensive and inefficient option — rather than diagnosing the actual cause. None recommended employing ACME clients directly within the services themselves, despite this being the standard method for certificate management.
Moreover, not a single model pointed out that even when using an internal proxy, traffic could remain unencrypted unless additional safeguards were applied. As a result, the user — trusting AI guidance — nearly exposed his entire internal network while simultaneously deploying vulnerable components with minimal protection.
As the author observed, tutorials and official documentation would have provided faster and far safer answers than hours of dialogue with language models. Yet major tech companies continue to boast about the growing proportion of AI-generated code, failing to distinguish between theoretical efficiency and real-world security risks. As such, the accumulation of flawed recommendations may leave inexperienced users on the brink of complete system compromise.
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