Not by themselves. An AI model cannot leap from one smart device to another like a biological infection. It reaches additional IoT devices only through a delivery path—such as cloud or edge synchronization, a device-management update service, or federated-learning coordination. If an attacker compromises that path, intercepts model traffic, alters an update, or takes over a participating device, the model file or model parameters can be distributed without the owners’ approval. The model is the transmitted artifact; compromised software, credentials, servers, and networks perform the unauthorized action.
What “spreading” means for an IoT AI model
IoT devices increasingly run machine-learning models for tasks such as camera detection, predictive maintenance, voice processing, anomaly detection, and access control. A model may be stored as a file, embedded in firmware, loaded from a gateway, or represented as numerical parameters exchanged during training. None of those forms has an independent network capability. A model can influence an application only after software loads it and gives it data, permissions, and computing resources.
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“Spread” therefore means that an authorized or unauthorized system copies a model, model update, or model parameters to more devices. The important security question is not whether the model is alive, but who controls the distribution mechanism and whether each receiving device verifies what it receives.
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Three ways a model can reach another device
Cloud–edge–device synchronization
Many deployments keep a central model in a cloud service, a regional edge server, or a local gateway. Devices periodically check for a new version, download it, and activate it. Synchronization can also occur when an edge node caches a model and forwards it to sensors or cameras.
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The ITU-T report Security threat analysis for artificial intelligence of things on devices (December 2025) identifies model theft during cloud-edge-device transfer, synchronization inconsistency, and interception or tampering on the communication path. It notes that “attackers can intercept model data packets during transmission by attacking on the cloud-edge-device communication network.” An attacker who controls a server, gateway, account, certificate, or network position could make devices retrieve a stolen or modified artifact instead of the approved one.
Device-management and firmware update channels
Manufacturers and fleet operators commonly use an over-the-air management service. The service authenticates a device, selects an update, and records its status. A model may arrive as part of a complete firmware image, an application package, or a separate data file.
Unauthorized distribution can follow a stolen administrator credential, a vulnerable management API, a compromised build server, or a firmware process that accepts unsigned files. A properly designed device checks the publisher signature, package hash, version policy, device identity, and rollback rules before replacing a working model. If any of those checks are missing or bypassed, one compromised management account can affect an entire fleet.
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Federated-learning coordination
In cross-device federated learning, each enrolled device trains or fine-tunes a model locally. It sends model-weight updates—not its raw sensor records—to a coordinator. The coordinator aggregates updates and distributes a new model to a selected group of devices.
This arrangement reduces the need to upload raw network traffic or other local data, but it does not make the model channel automatically trustworthy. A malicious client can submit poisoned updates, a coordinator can distribute a compromised aggregate, and an insecure connection can expose or alter weights in transit. The University of Portsmouth record for a 2026 study describes the goal as collaborative training “without exchanging raw network traffic,” while still operating across heterogeneous devices. Privacy of raw data and integrity of model updates are separate properties.
How unauthorized distribution happens
- Access is obtained. An attacker compromises a cloud account, update server, edge gateway, device credential, software dependency, or participating IoT device.
- The distribution decision is altered. The attacker changes which version is offered, which devices are selected, or which coordinator is trusted.
- The payload is replaced or poisoned. This may be a modified model file, a backdoored model, or malicious federated-learning weights designed to behave normally until a trigger appears.
- Verification is defeated. Weak signing practices, leaked private keys, disabled certificate checks, insecure boot settings, or an accepted downgrade allow the artifact to load.
- The change reaches a fleet. Devices pull the package during their normal check-in cycle, or a compromised gateway forwards it to downstream devices.
- The model causes an effect. Depending on the application, that could mean evading detection, exposing inferences, consuming excessive resources, or influencing an actuator through the device software.
The model itself does not perform lateral movement. The surrounding update, authentication, and execution systems provide the reach.
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Legitimate propagation versus malicious spread
| Security question | Legitimate distribution | Unauthorized distribution |
|---|---|---|
| Initiator | An authorized vendor, operator, or federated-learning coordinator | A compromised account, server, device, or network actor |
| Payload | Approved model and documented version | Stolen, altered, downgraded, or poisoned artifact |
| Transport | Authenticated, encrypted channel | Intercepted, spoofed, exposed, or redirected path |
| Trust checks | Signature, provenance, hash, policy, and rollback validation | Checks absent, misconfigured, or bypassed |
| Scope | Selected enrolled devices that meet policy | Uncontrolled or unexpectedly broad reach |
| Impact | Expected inference improvement or bug fix | Privacy loss, evasion, unsafe behavior, resource exhaustion, or further compromise |
A remote update is not automatically malicious simply because an owner did not click an approval button. Some products grant the operator standing permission to update devices. The security failure is an update that falls outside the documented authority, provenance, version policy, or device-owner expectations.
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No. An IoT botnet is a collection of devices infected with malware and controlled through command-and-control infrastructure. The cross-device intrusion-detection study by Angela Grace Famera, Raj Mani Shukla, and Suman Bhunia defines a botnet as “an army of zombified computers infected with malware and controlled by malicious actors to carry out tasks such as Distributed Denial of Service (DDoS) attacks.”
Botnets usually spread by exploiting weak credentials, exposed services, or software vulnerabilities. Malware then installs persistence and receives commands. A legitimate model update, by contrast, is a file or parameter exchange within an update or training workflow. Malware can carry a model file, alter a model already on a device, or use a botnet to reach update infrastructure, but the malware infection—not the model—is what takes control and moves laterally.
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Where the main weaknesses sit
The ITU-T AIoT threat model groups risks into five layers. Mapping each layer to a concrete control helps determine where to investigate.
| Layer | Typical model-related failure | Useful control |
|---|---|---|
| Application | An inference service accepts an unapproved model, exposes prediction data, or allows a model to trigger unsafe actions | Application authorization, input validation, least privilege, and human approval for high-impact actions |
| Network | Traffic is intercepted, redirected, replayed, or modified between cloud, edge, and device | Mutual authentication, modern encryption, certificate rotation, replay protection, and network segmentation |
| Data | Training data or model updates are poisoned, leaked, or made inconsistent across replicas | Provenance records, robust aggregation, update-quality checks, access controls, and anomaly detection |
| System | Update services, operating systems, containers, or credentials are compromised; resource overload prevents safe operation | Patch management, isolated update roles, rate limits, secure logging, and tested recovery images |
| Hardware | Insecure boot, exposed debug interfaces, stolen keys, or physical tampering permit replacement of software or models | Verified boot, hardware-backed key storage, locked debug ports, tamper controls, and physical access restrictions |
What federated learning protects—and what it does not
Federated learning changes the data flow: local records remain on participating devices while updates are sent to a coordinator. That can reduce direct exposure of raw data and support training across devices with different local conditions.
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- It does help with: keeping raw training examples on the device when the implementation is correctly configured.
- It does not guarantee: that model updates reveal nothing, that a coordinator is honest, or that a client cannot submit a malicious update.
- Remaining threats include: poisoning and backdoors, interception of updates, model theft, malicious clients, insecure enrollment, and a compromised aggregation service.
Encryption in transit protects the channel from many network observers, but it does not stop a trusted endpoint from submitting or distributing a bad update. Robust aggregation, client attestation, update-rate limits, provenance, and independent validation are needed when model integrity matters.
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How to prevent unauthorized AI updates
- Inventory every model-bearing component. Record devices, gateways, containers, model filenames, versions, signing keys, update servers, and federated-learning roles. Include cameras, routers, industrial controllers, and local edge computers that may not be listed as “AI” products.
- Require signed artifacts. Use publisher signatures and verify them on the device before installation. Keep private signing keys in a protected build or hardware-security environment; never distribute them with the application.
- Authenticate and encrypt both ends. Use device identity certificates or equivalent credentials, encrypted transport, certificate validation, and replay protection. Do not rely on a model hash delivered over an unauthenticated channel.
- Limit who can authorize a rollout. Separate model training, signing, release approval, and device administration. Apply least privilege, phishing-resistant multifactor authentication, short-lived credentials, and approval gates for fleet-wide changes.
- Validate provenance and rollback policy. Devices should reject unknown publishers, unexpected version numbers, expired packages, and unauthorized downgrades. Maintain a known-good model so an incident can be reversed.
- Harden federated-learning enrollment. Authenticate clients, attest device software where possible, quarantine anomalous updates, cap each client’s influence, and compare aggregate behavior against a clean validation set before release.
- Segment the network. Keep IoT devices away from administrative systems and sensitive user networks. Restrict outbound connections to the update and coordination services that each device actually needs.
- Monitor for unusual model activity. Alert on a new publisher, a sudden increase in update volume, model hashes that differ across supposedly identical devices, unexpected coordinator addresses, repeated failed signature checks, or resource consumption that rises after an update.
- Protect boot and hardware paths. Enable secure or verified boot, disable unused debug interfaces, protect device keys in hardware-backed storage, and control physical access to gateways and removable media.
What to do if a device received an unexpected model
- Contain first. Isolate the device or affected segment from the update service and peer networks without destroying volatile logs.
- Capture evidence. Record the model filename, version, cryptographic hash, install time, signer, source address, device identity, and relevant management or coordinator logs.
- Compare the artifact. Check the hash and signature against the organization’s release record. Look for a downgrade, unknown key, altered metadata, or a model present on only part of the fleet.
- Revoke access. Rotate suspected credentials and certificates, disable compromised administrator sessions, and block the affected update endpoint until its integrity is established.
- Recover from a trusted image. Reinstall a verified firmware and model package, restore configuration from a clean backup, and confirm secure-boot and signature checks before reconnecting.
- Hunt for lateral compromise. Review neighboring devices, gateways, and cloud accounts for the same signer, hash, source address, failed-login pattern, or unusual outbound traffic.
- Re-enable gradually. Use a small canary group, observe inference and resource behavior, then expand only after monitoring shows the approved artifact and expected performance.
What published accuracy numbers actually show
Research results demonstrate that defensive models can work on particular datasets; they do not prove that every IoT deployment is safe or accurate. Two reported figures illustrate the range:
| Study | Reported result | How to interpret it |
|---|---|---|
| Famera, Shukla, and Bhunia, 2023 preprint/thesis record, evaluated on MedBIoT | 71% average accuracy, 78% precision, 71% recall, and 68% F1-score | Benchmark results for that dataset and method, not a universal IoT guarantee |
| Nazaruk, Taheri, Golcarenarenji, and Gegov, 2026 Fed-BLAM evaluation | Up to 99.9% accuracy and F1-score | A study-specific upper result under its evaluation conditions, not a promise for consumer devices |
High detection scores do not replace update integrity. A detector trained on poisoned or stale data can still miss a backdoored model, while a perfectly signed malicious release remains an authorization failure. Security teams should evaluate provenance, behavior, and recovery alongside model metrics.
The practical takeaway
An AI model cannot autonomously jump between IoT devices. Unauthorized distribution becomes possible when an attacker controls the cloud-edge path, device-management plane, federated-learning coordinator, credentials, firmware process, or a participating device. Treat models as software supply-chain artifacts: authenticate the source, encrypt and authorize delivery, verify signatures and rollback rules on the device, monitor fleet behavior, and keep a trusted recovery version. Federated learning can limit raw-data sharing, but it still requires strong controls against poisoned updates, interception, theft, and compromised clients.
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