CVE•Published 2026-08-04•Modified 2026-09-23•1 article on news•5 live references•NVD data
CVE-2026-0163Google · Android
Vulnerability data via NVD (ingested)
CVSS v3.1
9.8
CRITICAL
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
EPSS percentile
43
Exploit Prediction Scoring System · top 57% of all CVEs
Weaknesses (CWE)
Description
In multiple functions of vpu_ioctl.c, there is a possible use after free due to a use after free. This could lead to remote escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.
Timeline
Published 2026-08-04
Modified 2026-09-23
External references
Search for exposed instances
Shodan + Censys queries derived from NVD's CPE data. The vuln tag catches assets Shodan has explicitly linked to this CVE; the product / banner fingerprints find exposed instances even when the vuln tag was never applied (which is common).
Shodan · vuln tag0 hosts
vuln:CVE-2026-0163Hosts Shodan has explicitly fingerprinted as vulnerable.
Shodan · OS
os:"Android"Hosts Shodan identified as running Android.
More intel sources (5)
Shodan report
vuln:CVE-2026-0163Country / ASN / product breakdown for the vuln query.
Censys
vulnerabilities.cve_id: CVE-2026-0163Censys host search filtered to this CVE id.
grep.app
CVE-2026-0163Public source-code mentions — fast PoC discovery.
GitHub code
CVE-2026-0163GitHub code search for direct mentions.
Google dork
"CVE-2026-0163" exploit -site:nvd.nist.govWrite-ups and news, NVD excluded.
Known PoCs on GitHub (5)
CVE-2026-01635 repos
nomi-sec/PoC-in-GitHubunknown
📡 PoC auto collect from GitHub. ⚠️ Be careful Malware.
tianchong-zerotemp/dianxingunknown
DianXing - AI-Driven End-to-End Code Security Auditing
sentinel-aidefense/CVE-2026-0163-EXPunknown
CVE-2026-0163 Exploit
micferna/app-phone-spamRust
App anti-spam téléphonique communautaire : backend de signalement partagé + listes publiques (ARCEP, spamtel) auto-actualisées
unknown404-practice/linear-models-from-scratchJupyter Notebook
From-scratch, vectorized NumPy implementations of linear models (OLS, regularized, robust, Bayesian, GLMs, streaming, conformal) with scikit-learn-compatible pipelines, full docs, …