CVE-2025-10096 (GCVE-0-2025-10096)
Vulnerability from cvelistv5 – Published: 2025-09-08 15:32 – Updated: 2025-09-08 15:54 X_Open Source
VLAI
EPSS
VEX
Title
SimStudioAI sim route.ts server-side request forgery
Summary
A vulnerability was determined in SimStudioAI sim up to 1.0.0. This affects an unknown function of the file apps/sim/app/api/files/parse/route.ts. Executing manipulation of the argument filePath can lead to server-side request forgery. The attack can be executed remotely. The exploit has been publicly disclosed and may be utilized. This patch is called 3424a338b763115f0269b209e777608e4cd31785. Applying a patch is advised to resolve this issue.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-09-08 15:53 UTC
CWE
- CWE-918 - Server-Side Request Forgery
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.323057 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.323057 | signaturepermissions-required |
| https://vuldb.com/?submit.644953 | third-party-advisory |
| https://github.com/simstudioai/sim/issues/960 | exploitissue-tracking |
| https://github.com/simstudioai/sim/pull/1149 | issue-tracking |
| https://github.com/simstudioai/sim/commit/3424a33… | patch |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| SimStudioAI | sim |
Affected:
1.0
|
guessed |
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.