CVE-2019-25655 (GCVE-0-2019-25655)
Vulnerability from cvelistv5 – Published: 2026-03-30 11:02 – Updated: 2026-07-28 01:46
VLAI
EPSS
VEX
Title
Device Monitoring Studio 8.10.00.8925 Denial of Service
Summary
Device Monitoring Studio 8.10.00.8925 contains a denial of service vulnerability that allows local attackers to crash the application by supplying an excessively long string to the server connection dialog. Attackers can trigger the crash by entering a malformed server name or address containing repeated characters through the Tools menu Connect to New Server interface.
Severity
6.2 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-03-30 15:59 UTC
CWE
- CWE-1316 - Fabric-Address Map Allows Programming of Unwarranted Overlaps of Protected and Unprotected Ranges
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://www.exploit-db.com/exploits/46321 | exploit |
| https://www.vulncheck.com/advisories/device-monit… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| hdd | Device Monitoring Studio |
Affected:
8.10.00.8925
cpe:2.3:a:ctfer:monitoring:8.10.00.8925:*:*:*:*:go:*:* |
Date Public
2019-02-04 00:00
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
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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.
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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.
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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.