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VulnerabilityPublished Updated

CVE-2023-52305: PaddlePaddle · Divide By Zero

Affects PaddlePaddleSource CVE Database V5

0

Not known to be exploited · CVSS 4.7 Medium : upgrade to 2.6.0 when convenient.

Data as of 26 Oct 2026, 10:12 UTC

CVSS 3.1 base score
4.7Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.4% chance of exploitation in 30 days – higher than 41% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 2.6.0
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:N/I:N/A:L

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 3 Jan 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

Upgrade PaddlePaddle to 2.6.0.

Affected software

ProductAffectedFixed in
PyPIpaddlepaddlepkg:pypi/paddlepaddleOther threatsAffected
  • <2.6.0
Fixed in
  • 2.6.0

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

AI-suggested

European organizations should take the following specific actions to mitigate this vulnerability: 1) Identify all instances of PaddlePaddle in their environments, including development, testing, and production systems. 2) Upgrade PaddlePaddle to version 2.6.0 or later once the patch is officially released, as this version addresses the divide-by-zero flaw. 3) Until patches are available, implement input validation and sanitization on data fed into the paddle.topk function to prevent triggering the divide-by-zero condition. 4) Employ runtime monitoring and anomaly detection to quickly identify crashes or abnormal terminations of AI services using PaddlePaddle. 5) Restrict access to AI model endpoints to trusted users and networks to reduce the risk of malicious or malformed input causing denial of service. 6) Incorporate fallback mechanisms in AI workflows to maintain service continuity if PaddlePaddle components fail. 7) Engage with PaddlePaddle community and vendor channels for updates and advisories. 8) Conduct security testing and fuzzing on AI model inputs to uncover similar vulnerabilities proactively.

Description

FPE in paddle.topk in PaddlePaddle before 2.6.0. This flaw can cause a runtime crash and a denial of service.

References

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