CVE•Published 2026-05-26•Modified 2026-07-23•1 article on news•4 live references•NVD data
CVE-2026-8046
Vulnerability data via NVD (ingested)
CVSS v3.1
8.1
HIGH
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
EPSS percentile
27
Exploit Prediction Scoring System · top 73% of all CVEs
Weaknesses (CWE)
Description
The affected products insufficiently verify authorization when deleting user accounts. An authenticated, low-privileged remote user can exploit this vulnerability to delete other users, including those with higher privileges.
Timeline
Published 2026-05-26
Modified 2026-07-23
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).
More intel sources (5)
Shodan report
vuln:CVE-2026-8046Country / ASN / product breakdown for the vuln query.
Censys
vulnerabilities.cve_id: CVE-2026-8046Censys host search filtered to this CVE id.
grep.app
CVE-2026-8046Public source-code mentions — fast PoC discovery.
GitHub code
CVE-2026-8046GitHub code search for direct mentions.
Google dork
"CVE-2026-8046" exploit -site:nvd.nist.govWrite-ups and news, NVD excluded.
Known PoCs on GitHub (6)
CVE-2026-80466 repos
Threekiii/Awesome-POCJava
一个漏洞 PoC 知识库。A knowledge base for vulnerability PoCs(Proof of Concept), with 1k+ vulnerabilities.
Ostorlab/KEVunknown
Ostorlab KEV: One-command to detect most remotely known exploitable vulnerabilities. Sourced from CISA KEV, Google's Tsunami, Ostorlab's Asteroid and Bug Bounty programs.
GODofExploit/exploit-arsenalPython
420 standalone Python-3 (stdlib-only) CVE exploits, each live-validated end-to-end against real vulnerable software with a write-up and a real-run screenshot. 102 in the CISA KEV c…
ynsmroztas/CVE-2026-82329-JFrog-Artifactory-Auth-BypassPython
CVE-2026-82329 — JFrog Artifactory (self-hosted) Auth Bypass
mirror-stack/measure-mirrorPython
🪞 Catch false positives/negatives in AI eval claims — pre-registration, fair-baseline, small-sample CI. Zero training, zero deps.
dainius1234/kai-systemPython
kai system