activeloopai deeplake up to 3.9.10 Kaggle ingest_kaggle command injection
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 7.9 | $0-$5k | 0.00 |
Summary
A vulnerability was found in activeloopai deeplake up to 3.9.10. It has been rated as critical. Affected by this vulnerability is the function ingest_kaggle of the component Kaggle Handler. The manipulation leads to command injection.
This vulnerability is uniquely identified as CVE-2024-6507. The attack is possible to be carried out remotely. No exploit exists.
To fix this issue, it is recommended to deploy a patch.
Details
A vulnerability was found in activeloopai deeplake up to 3.9.10 and classified as critical. This issue affects the function ingest_kaggle of the component Kaggle Handler. The manipulation with an unknown input leads to a command injection vulnerability. Using CWE to declare the problem leads to CWE-77. The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component. Impacted is confidentiality, integrity, and availability. The summary by CVE is:
Command injection when ingesting a remote Kaggle dataset due to a lack of input sanitization in the ingest_kaggle() API
It is possible to read the advisory at research.jfrog.com. The identification of this vulnerability is CVE-2024-6507 since 07/04/2024. The exploitation is known to be difficult. The attack may be initiated remotely. No form of authentication is needed for a successful exploitation. Technical details of the vulnerability are known, but there is no available exploit. The attack technique deployed by this issue is T1202 according to MITRE ATT&CK.
Applying a patch is able to eliminate this problem. The bugfix is ready for download at github.com.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Vendor
Name
Version
Website
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔍VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 8.1VulDB Meta Temp Score: 7.9
VulDB Base Score: 8.1
VulDB Temp Score: 7.7
VulDB Vector: 🔍
VulDB Reliability: 🔍
CNA Base Score: 8.1
CNA Vector (JFROG): 🔍
CVSSv2
| AV | AC | Au | C | I | A |
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| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔍
VulDB Temp Score: 🔍
VulDB Reliability: 🔍
Exploiting
Class: Command injectionCWE: CWE-77 / CWE-74 / CWE-707
CAPEC: 🔍
ATT&CK: 🔍
Physical: No
Local: No
Remote: Yes
Availability: 🔍
Status: Not defined
EPSS Score: 🔍
EPSS Percentile: 🔍
Price Prediction: 🔍
Current Price Estimation: 🔍
| 0-Day | Unlock | Unlock | Unlock | Unlock |
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: PatchStatus: 🔍
0-Day Time: 🔍
Patch: github.com
Timeline
07/04/2024 🔍07/04/2024 🔍
07/04/2024 🔍
07/06/2024 🔍
Sources
Product: github.comAdvisory: jfsa-2024-0010
Status: Confirmed
CVE: CVE-2024-6507 (🔍)
GCVE (CVE): GCVE-0-2024-6507
GCVE (VulDB): GCVE-100-270347
Entry
Created: 07/04/2024 15:40Updated: 07/06/2024 08:56
Changes: 07/04/2024 15:40 (66), 07/06/2024 08:56 (2)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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