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VulnerabilityPublished Updated

CVE-2026-73325: Fujitsu Research OneCompression · Deserialization of Untrusted Data

Affects Fujitsu Research OneCompressionSource CVE Database V5

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Not known to be exploited · CVSS 8.4 High.

Data as of 8 Nov 2026, 23:24 UTC

CVSS 4.0 base score
8.4High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.4% chance of exploitation in 30 days – higher than 34% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 12 Aug 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 9 Nov 2026.

What to do

Affected software

ProductAffectedFixed in
Fujitsu ResearchOneCompressionAffected
  • <1.2.1
Fixed in–
PyPIonecomppkg:pypi/onecompOther threatsAffected
  • =1.2.0
Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

Until a fix is available, avoid loading untrusted or unauthenticated model.pt checkpoint files with the vulnerable OneCompression 1.2.0 library. Restrict access to directories from which model files are loaded and validate or sandbox the loading process where possible.

Description

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.

References

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