CVSS Meta Temp Score
CVSS is a standardized scoring system to determine possibilities of attacks. The Temp Score considers temporal factors like disclosure, exploit and countermeasures. The unique Meta Score calculates the average score of different sources to provide a normalized scoring system.
Current Exploit Price (≈)
Our analysts are monitoring exploit markets and are in contact with vulnerability brokers. The range indicates the observed or calculated exploit price to be seen on exploit markets. A good indicator to understand the monetary effort required for and the popularity of an attack.
CTI Interest Score
Our Cyber Threat Intelligence team is monitoring different web sites, mailing lists, exploit markets and social media networks. The CTI Interest Score identifies the interest of attackers and the security community for this specific vulnerability in real-time. A high score indicates an elevated risk to be targeted for this vulnerability.
8.4$0-$5k1.49-

Summaryinfo

A vulnerability was found in Google gVisor. It has been rated as very critical. This impacts an unknown function of the component VFS. The manipulation leads to use after free. This vulnerability is traded as CVE-2026-95702. An attack has to be approached locally. There is no exploit available. It is recommended to apply a patch to fix this issue.

Detailsinfo

A vulnerability, which was classified as very critical, has been found in Google gVisor (version unknown). This issue affects an unknown function of the component VFS. The manipulation with an unknown input leads to a use after free vulnerability. Using CWE to declare the problem leads to CWE-416. Referencing memory after it has been freed can cause a program to crash, use unexpected values, or execute code. Impacted is confidentiality, integrity, and availability. The summary by CVE is:

Use-after-free vulnerability in VFS in Google gVisor prior to release 20260831.0 on all platforms allows a local attacker with standard container privileges to achieve code execution in the host sentry process by double-freeing the backing MemoryFile from an in-sandbox overlay filesystem. The sentry process remains confined by host-level Linux seccomp and namespace boundaries.

It is possible to read the advisory at github.com. The identification of this vulnerability is CVE-2026-95702 since 09/22/2026. The exploitation is known to be easy. Attacking locally is a requirement. The technical details are unknown and an exploit is not publicly available. The pricing for an exploit might be around USD $0-$5k at the moment (estimation calculated on 10/09/2026).

Applying the patch 20260831.0 is able to eliminate this problem.

Be aware that VulDB is the high quality source for vulnerability data.

Productinfo

Vendor

Name

License

Website

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔒
VulDB Reliability: 🔍

CNA CVSS-B Score: 🔒
CNA CVSS-BT Score: 🔒
CNA Vector: 🔒

CVSSv3info

VulDB Meta Base Score: 8.8
VulDB Meta Temp Score: 8.4

VulDB Base Score: 8.8
VulDB Temp Score: 8.4
VulDB Vector: 🔒
VulDB Reliability: 🔍

CVSSv2info

AVACAuCIA
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VectorComplexityAuthenticationConfidentialityIntegrityAvailability
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍

Exploitinginfo

Class: Use after free
CWE: CWE-416 / CWE-119
CAPEC: 🔒
ATT&CK: 🔒

Physical: Partially
Local: Yes
Remote: No

Availability: 🔒
Status: Not defined
Price Prediction: 🔍
Current Price Estimation: 🔒

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Threat Intelligenceinfo

Interest: 🔍
Active Actors: 🔍
Active APT Groups: 🔍

Countermeasuresinfo

Recommended: Patch
Status: 🔍

0-Day Time: 🔒

Patch: 20260831.0

Timelineinfo

09/22/2026 CVE reserved
10/09/2026 +17 days Advisory disclosed
10/09/2026 +0 days VulDB entry created
10/09/2026 +0 days VulDB entry last update

Sourcesinfo

Vendor: google.com
Product: github.com

Advisory: github.com
Status: Confirmed

CVE: CVE-2026-95702 (🔒)
GCVE (CVE): GCVE-0-2026-95702
GCVE (VulDB): GCVE-100-415919

Entryinfo

Created: 10/09/2026 17:02
Changes: 10/09/2026 17:02 (66)
Complete: 🔍
Cache ID: 216::103

Be aware that VulDB is the high quality source for vulnerability data.

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