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VulnerabilityPublished Updated

CVE-2026-48240: Open ISES Tickets · SQL Injection

Affects Open ISES TicketsSource CVE Database V5

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Not known to be exploited · CVSS 7.1 High : upgrade to 3.44.2 soon.

Data as of 30 Oct 2026, 11:52 UTC

CVSS 4.0 base score
7.1High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.2% chance of exploitation in 30 days – higher than 12% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 3.44.2
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:L/VA:N/SC:N/SI:N/SA:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 21 May 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 30 Oct 2026.

What to do

Upgrade Tickets and openises/tickets to 3.44.2.

Affected software

ProductAffectedFixed in
Open ISESTicketsAffected
  • <3.44.2
Fixed in
  • 3.44.2
GitHubopenises/ticketspkg:github/openises/ticketsOther threatsAffected
  • <3.44.2
Fixed in
  • 3.44.2

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

If you cannot patch yet, restrict access to the affected ajax/statistics.php endpoint to trusted users only and monitor for suspicious activity related to POST parameters tick_id and f_tick_id. Avoid exposing this functionality to untrusted users.

Description

Open ISES Tickets before 3.44.2 contains a SQL injection vulnerability in ajax/statistics.php where the tick_id and f_tick_id POST parameters are concatenated into WHERE clauses of SELECT statements in the statistics rollup queries without sanitization. Authenticated attackers can craft requests that alter query semantics to read, modify, or destroy database contents.

References

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