Kashipara Online Furniture Shopping Ecommerce Website 1.0 login.php txtAddress cross site scripting
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 4.3 | $0-$5k | 0.00 |
Summary
A vulnerability, which was classified as problematic, has been found in Kashipara Online Furniture Shopping Ecommerce Website 1.0. Affected by this issue is some unknown functionality of the file login.php. Performing a manipulation of the argument txtAddress results in cross site scripting. This vulnerability is identified as CVE-2024-4075. The attack can be initiated remotely. Additionally, an exploit exists.
Details
A vulnerability classified as problematic has been found in Kashipara Online Furniture Shopping Ecommerce Website 1.0. This affects an unknown functionality of the file login.php. The manipulation of the argument txtAddress with an unknown input leads to a cross site scripting vulnerability. CWE is classifying the issue as CWE-79. The product does not neutralize or incorrectly neutralizes user-controllable input before it is placed in output that is used as a web page that is served to other users. This is going to have an impact on integrity.
The advisory is shared at github.com. This vulnerability is uniquely identified as CVE-2024-4075. The exploitability is told to be easy. It is possible to initiate the attack remotely. It demands that the victim is doing some kind of user interaction. Technical details and a public exploit are known. MITRE ATT&CK project uses the attack technique T1059.007 for this issue.
The exploit is shared for download at github.com. It is declared as proof-of-concept. By approaching the search of inurl:login.php it is possible to find vulnerable targets with Google Hacking.
There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Product
Type
Vendor
Name
Version
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔍VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 4.4VulDB Meta Temp Score: 4.3
VulDB Base Score: 3.5
VulDB Temp Score: 3.2
VulDB Vector: 🔍
VulDB Reliability: 🔍
NVD Base Score: 6.1
NVD Vector: 🔍
CNA Base Score: 3.5
CNA Vector (VulDB): 🔍
CVSSv2
| AV | AC | Au | C | I | A |
|---|---|---|---|---|---|
| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
|---|---|---|---|---|---|
| Unlock | Unlock | Unlock | Unlock | Unlock | Unlock |
| Unlock | Unlock | Unlock | Unlock | Unlock | Unlock |
| Unlock | Unlock | Unlock | Unlock | Unlock | Unlock |
VulDB Base Score: 🔍
VulDB Temp Score: 🔍
VulDB Reliability: 🔍
NVD Base Score: 🔍
Exploiting
Class: Cross site scriptingCWE: CWE-79 / CWE-94 / CWE-74
CAPEC: 🔍
ATT&CK: 🔍
Physical: No
Local: No
Remote: Yes
Availability: 🔍
Access: Public
Status: Proof-of-Concept
Download: 🔍
Google Hack: 🔍
EPSS Score: 🔍
EPSS Percentile: 🔍
Price Prediction: 🔍
Current Price Estimation: 🔍
| 0-Day | Unlock | Unlock | Unlock | Unlock |
|---|---|---|---|---|
| Today | Unlock | Unlock | Unlock | Unlock |
Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: no mitigation knownStatus: 🔍
0-Day Time: 🔍
Timeline
04/23/2024 🔍04/23/2024 🔍
04/23/2024 🔍
04/04/2025 🔍
Sources
Advisory: github.comStatus: Not defined
CVE: CVE-2024-4075 (🔍)
GCVE (CVE): GCVE-0-2024-4075
GCVE (VulDB): GCVE-100-261801
scip Labs: https://www.scip.ch/en/?labs.20161013
Entry
Created: 04/23/2024 15:46Updated: 04/04/2025 06:45
Changes: 04/23/2024 15:46 (56), 05/28/2024 21:23 (2), 05/28/2024 21:38 (18), 02/28/2025 04:58 (20), 04/04/2025 06:45 (3)
Complete: 🔍
Submitter: SSL_Seven_Security Lab_WangZhiQiang_XiaoZiLong
Cache ID: 216:C50:103
Submit
Accepted
- Submit #321451: kashipara Online Furniture Shopping Ecommerce Website Project ≤1.0 XSS injection (by SSL_Seven_Security Lab_WangZhiQiang_XiaoZiLong)
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
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