118 lines
4.9 KiB
JSON
118 lines
4.9 KiB
JSON
{
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"dataType": "CVE_RECORD",
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"dataVersion": "5.1",
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"cveMetadata": {
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"cveId": "CVE-2025-58756",
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"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
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"state": "PUBLISHED",
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"assignerShortName": "GitHub_M",
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"dateReserved": "2025-09-04T19:18:09.499Z",
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"datePublished": "2025-09-08T23:39:55.508Z",
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"dateUpdated": "2025-09-09T13:28:57.518Z"
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},
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"containers": {
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"cna": {
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"title": "MONAI's unsafe torch usage may lead to arbitrary code execution",
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"problemTypes": [
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{
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"descriptions": [
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{
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"cweId": "CWE-502",
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"lang": "en",
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"description": "CWE-502: Deserialization of Untrusted Data",
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"type": "CWE"
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}
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]
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}
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],
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"metrics": [
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{
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"cvssV3_1": {
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"attackComplexity": "LOW",
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"attackVector": "NETWORK",
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"availabilityImpact": "HIGH",
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"baseScore": 8.8,
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"baseSeverity": "HIGH",
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"confidentialityImpact": "HIGH",
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"integrityImpact": "HIGH",
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"privilegesRequired": "LOW",
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"scope": "UNCHANGED",
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"userInteraction": "NONE",
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"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
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"version": "3.1"
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}
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}
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],
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"references": [
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{
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"name": "https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6vm5-6jv9-rjpj",
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"tags": [
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"x_refsource_CONFIRM"
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],
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"url": "https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6vm5-6jv9-rjpj"
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}
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],
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"affected": [
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{
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"vendor": "Project-MONAI",
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"product": "MONAI",
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"versions": [
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{
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"version": "<= 1.5.0",
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"status": "affected"
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}
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]
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}
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],
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"providerMetadata": {
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"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
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"shortName": "GitHub_M",
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"dateUpdated": "2025-09-08T23:39:55.508Z"
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},
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"descriptions": [
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{
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"lang": "en",
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"value": "MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.0, in `model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True)` in monai/bundle/scripts.py , `weights_only=True` is loaded securely. However, insecure loading methods still exist elsewhere in the project, such as when loading checkpoints. This is a common practice when users want to reduce training time and costs by loading pre-trained models downloaded from other platforms. Loading a checkpoint containing malicious content can trigger a deserialization vulnerability, leading to code execution. As of time of publication, no known fixed versions are available."
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}
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],
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"source": {
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"advisory": "GHSA-6vm5-6jv9-rjpj",
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"discovery": "UNKNOWN"
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}
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},
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"adp": [
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{
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"metrics": [
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{
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"other": {
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"type": "ssvc",
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"content": {
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"timestamp": "2025-09-09T13:13:04.127686Z",
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"id": "CVE-2025-58756",
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"options": [
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{
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"Exploitation": "none"
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},
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{
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"Automatable": "no"
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},
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{
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"Technical Impact": "total"
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}
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],
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"role": "CISA Coordinator",
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"version": "2.0.3"
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}
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}
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}
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],
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"title": "CISA ADP Vulnrichment",
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"providerMetadata": {
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"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
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"shortName": "CISA-ADP",
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"dateUpdated": "2025-09-09T13:28:57.518Z"
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}
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}
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]
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}
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} |