CVE-2025-9151 (GCVE-0-2025-9151)
Vulnerability from cvelistv5 – Published: 2025-08-19 18:02 – Updated: 2025-08-19 18:38
VLAI
EPSS
VEX
Title
LiuYuYang01 ThriveX-Blog web updateJsonValueByName improper authorization
Summary
A security flaw has been discovered in LiuYuYang01 ThriveX-Blog up to 3.1.7. Affected by this vulnerability is the function updateJsonValueByName of the file /web_config/json/name/web. Performing manipulation results in improper authorization. It is possible to initiate the attack remotely. The exploit has been released to the public and may be exploited. The vendor was contacted early about this disclosure but did not respond in any way.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-08-19 18:38 UTC
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.320530 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.320530 | signaturepermissions-required |
| https://vuldb.com/?submit.629873 | third-party-advisory |
| https://github.com/echo0d/vulnerability/blob/main… | related |
| https://github.com/echo0d/vulnerability/blob/main… | exploit |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| LiuYuYang01 | ThriveX-Blog |
Affected:
3.1.0
Affected: 3.1.1 Affected: 3.1.2 Affected: 3.1.3 Affected: 3.1.4 Affected: 3.1.5 Affected: 3.1.6 Affected: 3.1.7 |
guessed |
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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- Confirmed: The vulnerability has been validated from an analyst's perspective.
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- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
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- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
How durations are calculated
Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.
Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
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.