| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 7.0 | $0-$5k | 0.08 |
Overview
A vulnerability was found in GNOME gvdb. It has been classified as critical. This affects the function gvdb_table_write_contents_async of the file gvdb-builder.c. The manipulation leads to use after free. The CWE definition for the vulnerability is CWE-416. The weakness was shared 12/26/2022 as d83587b2a364eb9a9a53be7e6a708074e252de14. The advisory is shared at github.com.
This vulnerability is uniquely identified as CVE-2019-25085. It is possible to initiate the attack remotely. Technical details are available. There is no exploit available. The price for an exploit might be around USD $0-$5k at the moment.
It is declared as not defined. We expect the 0-day to have been worth approximately $0-$5k.
The identifier of the patch is d83587b2a364eb9a9a53be7e6a708074e252de14. The bugfix is ready for download at github.com. It is recommended to apply a patch to fix this issue. A possible mitigation has been published even before and not after the disclosure of the vulnerability. [Details]
IOB - Indicator of Behavior (395)
Timeline
The data in this chart does not reflect real data. It is dummy data, distorted and not usable in any way. You need an additional purchase to unlock this view to get access to more details of real data.
Activities
IOC - Indicator of Compromise (7)
These indicators of compromise highlight associated network ranges which are known to be part of research and attack activities.
| ID | IP range | Actor | Type | Confidence |
|---|---|---|---|---|
| 1 | 3.70.168.0/24 | Sliver | predictive | High |
| 2 | X.XXX.X.X/XX | Xxxxxx | predictive | High |
| 3 | XX.XX.XX.X/XX | Xxxxxxxx | predictive | High |
| 4 | XX.XXX.XXX.X/XX | Xxxxx | predictive | High |
| 5 | XX.XXX.XXX.X/XX | Xxxxxxxxx | predictive | High |
| 6 | XXX.XX.XXX.X/XX | Xxxxxxxx | predictive | High |
| 7 | XXX.XX.XXX.X/XX | Xxxxxxx | predictive | High |
IOA - Indicator of Attack (1)
These indicators of attack list the potential fragments used for technical activities like reconnaissance, exploitation, privilege escalation, and exfiltration. This data is unique as it uses our predictive model for actor profiling.
| ID | Class | Indicator | Type | Confidence |
|---|---|---|---|---|
| 1 | File | xxxx-xxxxxxx.x | verified | High |