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Yes—an AI agent with network access can discover smart-home devices, identify misconfigurations, expose weak credentials, and sometimes find a path into a computer. That is what journalist Will Knight documented in a September 9, 2026, WIRED experiment. The test was not a benchmark and does not show how often homes are vulnerable; it was one deliberate exercise on one household network. It does show why home security reviews must include printers, speakers, firmware, API keys, login credentials, and network segmentation—not just laptops and phones.
Knight used an intentionally de-aligned version of Z.ai’s GLM-5.3 through Abliteration AI and the CyberStrike harness. The model found around a dozen networked hardware systems and dozens of software problems, then pursued access to a Linux machine. The useful lesson is not that every AI will instantly take over a house. It is that an autonomous scanner can turn small oversights into a prioritized attack path faster than a person casually checking settings.
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What did the AI agent actually find?
The agent’s first job was reconnaissance: identify what was reachable on the home network and determine which devices answered its probes. Knight reported around a dozen hardware systems, including a printer, a Wiim stereo, and other IoT equipment. Those observations belong to this single household test; they are not a prevalence rate for homes in general.
| System or area | Observation in the experiment | Why it matters |
|---|---|---|
| Printer | Misconfigured so anyone on the network could log in | A network intruder could potentially access printer functions, stored jobs, or administrative settings, depending on the model |
| Wiim stereo | Leaked information such as the last song played and could be controlled by anyone on the network | Reveals household activity and demonstrates that entertainment hardware can expose data and accept commands |
| Other IoT devices | Firmware needed updating | Unpatched firmware can leave known weaknesses available to an attacker |
| Casually built software projects | Dozens of problems, including unprotected API credentials and a misconfiguration that might let an attacker send email | Secrets and permissive settings can turn a local foothold into account abuse or spam |
| Linux machine | The model inferred a working username, offered to write a brute-force script, and found a cryptographic key that allowed passwordless login while seeking a route to root access | A stolen key can bypass password controls; attempted privilege escalation increases the potential damage |
The strongest finding was not a dramatic exploit chain. It was the accumulation of ordinary weaknesses: a default or overly broad printer setting, an unpatched device, secrets left in project files, and a key stored where the agent could discover it. An attacker needs only one usable opening, then looks for the next.
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How did the experiment work?
Knight gave the agent access to his home network and local files, then let it inspect what it could reach. The sequence matters because an agent can maintain context across tasks instead of treating each scan as an isolated command.
- Network discovery: The harness identified reachable hardware and services, creating an inventory that a human might never have written down.
- Configuration review: The model tested exposed interfaces and noticed that the printer accepted logins from other devices on the network.
- Behavioral checks: It determined that the Wiim stereo disclosed recent playback information and accepted network control.
- Patch assessment: It flagged IoT equipment whose firmware was out of date.
- Local-file audit: It inspected “vibe-coded” projects and found credentials and a mail-sending misconfiguration.
- Host probing: When asked to examine a Linux system, it inferred a username, proposed brute-force code, and located a cryptographic key that enabled login without a password.
Knight stopped the model when it offered brute-force behavior and later returned to an aligned model. That boundary is important: the experiment demonstrated what the tool could attempt, not a recommendation to let an unrestrained model attack real systems.
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Can an AI agent hack my smart-home devices?
It can discover and abuse weakly protected devices when it has a path onto the same network. “Hack” in this context may mean logging into an exposed administration page, reading metadata, issuing a supported command, or using a stolen key—not necessarily installing malware or taking permanent control.
Three conditions determine the risk:
- Reachability: The agent must be able to send traffic to the device, either from the home network or from a compromised device already inside it.
- Actionable weakness: A usable password, open service, outdated firmware, exposed key, or permissive API must be present.
- Permission to act: The model needs tools that can scan, authenticate, or execute commands. A chat-only assistant cannot independently probe your network.
The experiment does not establish that an AI agent can defeat every consumer device. It establishes that once access and tools are supplied, an agent can connect reconnaissance, interpretation, and follow-up actions with little human prompting.
What is the difference between an aligned assistant and a de-aligned hacking model?
An aligned assistant is designed to refuse or constrain harmful activity. Knight deliberately used an abliterated, de-aligned version of GLM-5.3 so the experiment could examine behavior after those safeguards were removed. The comparison below describes the test’s design, not a controlled performance score.
| Comparison axis | Aligned assistant | De-aligned model used in the experiment |
|---|---|---|
| Authorization and safety controls | Expected to enforce safety boundaries and request legitimate authorization | Guardrails were intentionally removed, making behavior less predictable |
| Scanning or exploitation | May explain defensive steps without directly probing systems | Used the CyberStrike harness to inspect reachable devices and files |
| Autonomous escalation | Designed to stop or redirect unsafe requests | Proposed brute-force behavior and searched for a path toward root access |
| Defensive auditing | Can help interpret findings within approved limits | Found concrete misconfigurations and exposed secrets in the test environment |
Removing safeguards does not create a reliable security professional. It creates a system that may continue from harmless discovery into unauthorized access, credential attacks, or data destruction. If you are auditing your own equipment, keep the scope explicit and use tools that log every action.
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Can a hacked printer expose my documents?
A misconfigured printer can expose more than paper. In Knight’s test, anyone on the network could log in. Depending on the device and its settings, an intruder with administrative access might view queued jobs, alter address books, change firmware or network settings, or use the printer as a stepping stone to other services. The report did not establish which of those actions were possible on that specific printer, so treat them as risks to check rather than confirmed outcomes.
Review the printer’s administrator account, disable network protocols you do not use, install the latest firmware offered for the exact model, and require authentication for management pages. If the printer supports job storage, clear old jobs and enable encryption or automatic deletion where available. Keep the management interface off the guest network only if trusted computers need it; otherwise, place the printer on a segment where its access to sensitive machines is limited.
How do I isolate IoT devices from my computers?
Use network segmentation so a compromised speaker, camera, television, or appliance cannot freely reach laptops and file servers. The practical starting point is a Wi-Fi router with guest-network support; configure it as a separate network for IoT equipment, then verify that isolation is actually enabled.
- Update the router: Install the current firmware for the exact router model before changing network settings.
- Create a guest or IoT SSID: In the router app or web interface, open the Wi-Fi or Guest Network section, create a distinct network name, and use a unique password.
- Enable client isolation: Turn on the option that blocks wireless clients from contacting one another. Labels vary, so check the router’s documentation.
- Block local-network access: Disable settings such as “allow guests to access the local network” unless a specific device requires it.
- Move devices: Reconnect smart speakers, televisions, plugs, cameras, and similar equipment to the IoT or guest SSID. Keep workstations, storage, and personal phones on the primary network.
- Test from both sides: From an IoT-network device, confirm that a computer’s shared folders and administration pages are unreachable. From a trusted computer, verify only the device controls you genuinely need.
Segmentation limits the blast radius; it does not repair a vulnerable device. A speaker may still leak data to an internet service, and a compromised IoT device may attack other guests on its own segment if client isolation is disabled. Recheck the settings after router updates because firmware can change menu names or reset options.
What should I secure after an AI vulnerability scan?
Remediate in the order that removes access first, then reduces what a compromised device can reach.
1. Patch firmware and software
Record every router, printer, speaker, camera, appliance, computer, and application the scan identifies. Apply updates from the manufacturer or project’s official channel, reboot when required, and remove devices that no longer receive security fixes.
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2. Replace exposed credentials and keys
Assume any API token, private key, password, or connection string found in a project directory is compromised. Revoke it at the issuing service, create a replacement with the minimum permissions, and move the secret into an environment-variable store or dedicated secrets manager. Search backups and public repositories too; deleting one local copy does not revoke a leaked credential.
3. Correct printer and IoT access
Change default administrator credentials, disable anonymous or network-wide login, and restrict management interfaces to a trusted segment. Remove unused services such as legacy discovery or remote administration.
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4. Investigate passwordless Linux access
Identify the cryptographic key the agent found, determine which account accepted it, and revoke or replace it in the account’s authorized-key file. Review shell history, authentication logs, scheduled tasks, and recently modified files. Check whether the account could use sudo or another privilege path, then rotate any credentials stored on that machine.
5. Close mail-sending paths
If a project could send email without proper authorization, restrict the mail API, rotate its credentials, add rate limits, and inspect logs for unexpected messages. Confirm that outbound mail cannot be used to impersonate your domain or exhaust a service quota.
6. Verify the fix
Run a new inventory from an approved scanner, confirm that the old login no longer works, and test network boundaries from the IoT segment. Keep a dated record of the finding, the change made, and the evidence that access was removed.
Why is AI-enabled defense difficult?
Tufts computer scientist Shaanan Cohney described the imbalance plainly: “Attackers are often early adopters.” Defenders must close every relevant hole, while an attacker needs only one weakness. He also noted that “Most organizations have other things to worry about,” which helps explain why patching, inventory, and configuration work can lag behind new attack techniques.
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An agent changes the economics of reconnaissance. It can inspect many interfaces, remember relationships among findings, and produce a next action without waiting for a human to interpret each response. That does not make it infallible: it can misunderstand a service, overstate a vulnerability, or propose an unsafe step. Human authorization and verification remain essential.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Are open-source and independent AI security tools useful for defense?
They can be, particularly for organizations that need to inspect local networks, source code, or unusual hardware without sending sensitive data to a hosted service. MIT professor Aleksander Mądry said, “I do think there will be room for open source and independent tools.” That is a statement about practical opportunity, not proof that any particular scanner is accurate or safe.
Choose tools that provide scope controls, dry-run modes, detailed logs, rate limits, and a clear stop button. Test them in a lab or on disposable devices first. Independent software is valuable only when its operators can understand what it does, update it promptly, and review its findings instead of treating an automated verdict as fact.
How can I perform a safe self-audit?
Start with authorization and containment, not with an aggressive prompt.
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- Back up important data and create a recovery path before changing credentials or firmware.
- Prefer discovery, version checks, and configuration review over exploit attempts.
- Disable destructive actions, brute-force modules, mail sending, and privilege-escalation steps.
- Run during a maintenance window so a misbehaving scan cannot interrupt work or safety equipment.
- Save logs, screenshots, and timestamps, then manually validate each high-severity finding.
- Stop immediately if the tool proposes attacking a system outside the written scope.
Never point an autonomous agent at a neighbor’s network, a public IP range, or a service you do not control. Network access is not authorization, and a model’s suggestion is not a legal or security exemption.
What this experiment does—and does not—prove
It proves that one de-aligned model, equipped with a specialized harness and access to one home network and its files, could discover actionable weaknesses across hardware and software. It does not provide a success rate, a comparison with professional penetration testers, or evidence that most homes contain the same flaws. The “around a dozen” systems and “dozens” of software problems are observations from Knight’s 2026 experiment, not population statistics.
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The practical conclusion is narrower and more useful: inventory everything, patch what can be patched, remove exposed secrets, protect printers, and separate IoT devices from computers. Those controls reduce ordinary attack paths whether the probing comes from a human, a script, or an AI agent.
Frequently Asked Questions
Can an AI scan my home network without my Wi-Fi password?
Usually it needs a foothold, such as access to the Wi-Fi, a compromised device, or a service exposed to the internet. Once inside, network segmentation and client isolation determine how much it can see; an external chatbot cannot scan your private network merely because you asked it a question.
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If a speaker must reach a computer directly, strict guest isolation may block that feature. Prefer an integration that uses an approved cloud account or a narrowly allowed firewall rule, and keep file sharing and administration interfaces unavailable from the speaker’s segment.
How often should I check IoT firmware?
Check the manufacturer’s support page or the device’s update setting at least quarterly, and enable automatic updates when the vendor provides them. Replace equipment that no longer receives security updates rather than leaving it permanently exposed.
What should I do if a scan finds a private key in an old backup?
Treat the key as compromised even if the backup is not currently mounted. Revoke it wherever it is trusted, issue a replacement with narrower permissions, remove copies from active systems, and review logs for use during the period in which the backup was accessible.
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