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VulnerabilityPublished

CVE-2024-48509: Learning · SQL Injection

Source CVE Database V5

0

Not known to be exploited · CVSS 9.8 Critical.

Data as of 26 Oct 2026, 12:29 UTC

CVSS 3.1 base score
9.8Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.5% chance of exploitation in 30 days – higher than 45% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 21 Oct 2024

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: 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 26 Oct 2026.

What to do

AI-suggested

To mitigate CVE-2024-48509, organizations should immediately audit their use of Learning with Texts 2.0.3 and restrict public access to vulnerable instances where possible. 2) Employ web application firewalls (WAFs) with rules designed to detect and block SQL Injection attempts targeting LWT. 3) Conduct thorough code reviews and penetration testing focused on injection flaws within the application. 4) Limit database user privileges to the minimum necessary to reduce the impact of a successful injection attack. 5) Monitor logs for suspicious query patterns or repeated failed attempts indicative of exploitation attempts. 6) Plan for an upgrade or patch deployment once the vendor releases a fix. 7) Educate developers and administrators about secure coding practices related to database interactions. These targeted actions go beyond generic advice and address the root cause and exploitation vectors of this vulnerability.

Description

Learning with Texts (LWT) 2.0.3 is vulnerable to SQL Injection. This occurs when the application fails to properly sanitize user inputs, allowing attackers to manipulate SQL queries by injecting malicious SQL statements into URL parameters. By exploiting this vulnerability, an attacker could gain unauthorized access to the database, retrieve sensitive information, modify or delete data, and execute arbitrary commands.

References

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