CVE-2026-112304
HIGH
7,7
Source: [email protected]
Attack Vector: network
Attack Complexity: low
Privileges Required: none
User Interaction: passive
Confidentiality: N/A
Integrity: N/A
Availability: N/A
HIGH
7,5
Source: [email protected]
Attack Vector: network
Attack Complexity: high
Privileges Required: none
User Interaction: required
Scope: unchanged
Confidentiality: high
Integrity: high
Availability: high
Description
AI Translation Available
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.
EPSS (Exploit Prediction Scoring System)
Trend Analysis
EPSS (Exploit Prediction Scoring System)
Prevede la probabilità di sfruttamento basata su intelligence sulle minacce e sulle caratteristiche della vulnerabilità.
EPSS Score
0,0029
Percentile
0,2th
Updated
Single Data Point
Only one EPSS measurement is available for this CVE. Trend analysis requires multiple data points over time.
502
Deserialization of Untrusted Data
DraftCommon Consequences
Security Scopes Affected:
Integrity
Availability
Other
Potential Impacts:
Modify Application Data
Unexpected State
Dos: Resource Consumption (Cpu)
Varies By Context
Applicable Platforms
Languages:
Java, Ruby, PHP, Python, JavaScript
Technologies:
Not Technology-Specific, ICS/OT, AI/ML
https://github.com/mindee/doctr/blob/v1.1.0/doctr/models/utils/pytorch.py
https://github.com/mindee/doctr/commit/687503dd15592483c4699ee51280b41938579a9e
https://github.com/mindee/doctr/issues/2175
https://www.vulncheck.com/advisories/doctr-through-1.1.0-unsafe-deserialization…