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VulnerabilityPublished Updated

CVE-2024-53030: Qualcomm Snapdragon · Improper Input Validation

Affects Qualcomm SnapdragonSource CVE Database V5

0

Not known to be exploited · CVSS 7.8 High.

Data as of 26 Oct 2026, 10:12 UTC

CVSS 3.1 base score
7.8High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.1% chance of exploitation in 30 days – higher than 2% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 3 Mar 2025

  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

  1. Monitor Qualcomm advisories closely for official patches or firmware updates addressing CVE-2024-53030 and apply them promptly across all affected devices.
  2. Implement strict application whitelisting and privilege restrictions to minimize the risk of local attackers gaining the necessary access to exploit the vulnerability.
  3. Employ runtime protection mechanisms such as memory corruption mitigations (e.g., ASLR, DEP) where supported by the device to reduce exploitation success.
  4. For automotive and embedded systems, ensure secure boot and integrity verification mechanisms are enabled to prevent unauthorized code execution.
  5. Limit physical and local access to devices, especially in enterprise and critical infrastructure environments, to reduce the attack surface.
  6. Conduct regular security audits and penetration testing focusing on local privilege escalation vectors.
  7. Educate users and administrators about the risks of installing untrusted applications or firmware that could facilitate local exploitation.
  8. Consider network segmentation and monitoring to detect anomalous device behavior indicative of exploitation attempts. These measures combined will reduce the likelihood and impact of exploitation until patches are available and deployed.

Description

Memory corruption while processing input message passed from FE driver.

References

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