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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Stop loading the model and treat the affected process and its environment as potentially compromised. Contain the workload, preserve evidence, investigate what the process could access, and rotate exposed credentials from a clean environment. Do not retry the artifact with unrestricted loading just to get past an error.
What should you do first?
- Stop execution. Do not rerun the loader or use a scanner that executes the suspect artifact. Avoid disabling restricted loading or allowlisting unfamiliar code just to make the file load.
- Coordinate containment. If code may have run, work with your organization’s security or incident-response team to isolate the affected host, VM, container, notebook, or job from other systems and external networks. For a managed workstation, cluster, or cloud workload, follow the organization’s incident playbook rather than making unilateral changes.
- Preserve evidence before cleanup. Coordinate before terminating processes, wiping a system, or making changes that could erase volatile evidence or disrupt response. CISA’s Federal Government Cybersecurity Incident and Vulnerability Response Playbooks advises isolating affected systems while accounting for evidence preservation and service availability.
A loader error does not establish whether code ran or what it did. The answer depends on the actual loader, its arguments, and the host’s activity; investigate rather than assuming either compromise or safety.
What evidence and activity should you investigate?
Record the artifact’s download origin, repository and revision or commit, file path and hash if available, loader and library versions, exact command or notebook cell, execution time, host identity, user account, and complete error or output. Preserve relevant system, endpoint, authentication, process, and network logs. Keep a copy of the artifact for controlled analysis, but do not open it with unrestricted pickle in the affected environment. CISA recommends collecting and reviewing logs, data, and artifacts, and using forensic imaging or memory capture where appropriate.
With incident responders, check for child processes, file writes, outbound connections, credential-store access, and activity performed by identities available to the process. Extend the review to systems and services those identities could reach. The investigation should establish what happened on this host; general loader documentation cannot determine that.
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Which credentials and services may need protection?
From a clean device or administrative environment, revoke or rotate tokens, passwords, private keys, and service credentials the process could access. Prioritize privileged and cloud credentials, revoke unnecessary sessions, and review relevant identity-provider, cloud, source-control, package-registry, and model-hub audit events. CISA recommends changing administrative passwords, rotating private keys and application or service secrets where compromise is suspected, and revoking privileged access.
How should you eradicate and recover?
Have incident responders assess scope and persistence before declaring the host clean. Where indicated, rebuild or restore affected systems from known-good sources, correct or patch the loader pathway, and monitor for renewed suspicious activity. Preserve incident artifacts and document the response. If new signs of compromise appear, expand the investigation and reassess scope rather than treating recovery as complete.
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Why can loading a model execute code?
PyTorch’s torch.save and torch.load use Python pickle by default. Unrestricted pickle deserialization can execute arbitrary code, so an unfamiliar checkpoint is not just a potentially malformed data file. PyTorch warns that weights_only=False can result in arbitrary code execution and should be used only when the source is trusted.
PyTorch 2.6 and later default torch.load to weights_only=True when no pickle_module is supplied. Check the installed version and the actual call site: an explicit weights_only=False, a supplied pickle module, or a different loader can change the behavior. PyTorch says weights-only mode narrows remote-code-execution exposure, but it does not prevent every denial-of-service issue, and memory corruption may still be possible. Downstream use of unexpected objects can also be dangerous.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesHow can you reduce the risk next time?
Prefer state dictionaries and restricted loading in PyTorch
PyTorch’s recommended pattern is to save a state_dict, load it with weights_only=True, and apply the weights to a model architecture created from reviewed code. Make the argument explicit where practical so the intended behavior is clear across versions and call sites. Do not indiscriminately allowlist globals to make an unfamiliar checkpoint load; allowlist code or classes only after independent review and a trust assessment.
Prefer data-only formats and verify provenance
Where supported, choose safetensors or another data-only format. Hugging Face’s documented loading helpers default to safe=True and reject pickle files unless the caller opts in; when pickle loading is allowed, the helper defaults to PyTorch’s restricted weights_only=True path. Check the installed huggingface_hub version and call arguments because behavior can vary. Hugging Face recommends using trusted sources and signed commits, and describes scanning pickle imports on its Hub.
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Compare the practical trade-offs
| Loading approach | Execution risk | Compatibility and provenance | Residual concerns |
|---|---|---|---|
| Unrestricted pickle loading | Can execute arbitrary code during deserialization. | May load artifacts containing custom Python objects; use only when the source is trusted. | Trusting the source does not establish that the artifact or the rest of the pipeline is safe. |
| PyTorch weights-only loading | Restricts what the unpickler accepts and narrows remote-code-execution exposure. | Suited to weights such as a state dictionary; check the installed version and actual call arguments. | Does not guard against denial of service; memory corruption and downstream hazards remain possible. |
| Safetensors or another data-only format | Avoids pickle-based code execution during deserialization. | Requires an artifact and loader that support the format. A trusted source and reviewed revision still matter. | A format choice does not certify model behavior or rule out compromise elsewhere in the pipeline. |
Safetensors checks for missing or unexpected parameter keys can reveal a mismatch between the file and model architecture; they do not establish malicious intent. A signature, scan, format choice, or successful restricted load is one control, not a guarantee that the model or surrounding pipeline is safe.
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