Fujitsu OneCompression < 1.2.1 Arbitrary Code Execution via torch.load Deserialization
Published Aug 12, 2026 · Updated Nov 3, 2026
Original summary
Normalized public record · Updated Nov 3, 2026
Fujitsu OneCompression < 1.2.1 Arbitrary Code Execution via torch.load Deserialization
Publisher and source text is retained separately from QFinch interpretation.
QFinch assessment
Fujitsu Research's OneCompression library before 1.2.1 contains an unsafe deserialization vulnerability that allows attackers to execute arbitrary code by supplying a crafted model.pt checkpoint file, as QuantizedModelLoader.load_quantized_model_pt() unconditionally calls torch.load with weights_only=False, invoking Python's pickle machinery during deserialization. Attackers can embed malicious __reduce__ methods in a crafted model checkpoint to execute arbitrary Python code, including system commands, when the library loads the file from a caller-selected model directory.
Evidence: public source recordsAffected products
| Affected package, product or range | Fix status |
|---|---|
| Consult the referenced vendor advisory for affected versions. | Consult source advisory |
Matching a version is only one part of assessing exposure. QFinch has not assessed your environment on this public page.
Remediation
Apply the vendor-supported fixed release or mitigation, then verify the affected component is no longer reachable.
- Confirm the affected product and reachable code path exist in your environment.
- Apply the vendor-supported patch or mitigation from the primary advisory.
- Rescan the exact deployed revision and retain verification evidence.
Evidence coverage
- SSVC priority
- MEDIUM
- Confidence
- Not available
- Aliases
- None reported
Scores appear only when corresponding evidence exists. Missing data is never interpreted as zero risk.
Sources and record history
Record history
Published Aug 12, 2026 · Last refreshed Nov 3, 2026