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CVE-2024-5452VulnerabilityPublished Updated

CVE-2024-5452: lightning-ai/pytorch-lightning · Mass Assignment

Affects lightning-ai/pytorch-lightningSource CVE Database V5

0

Not known to be exploited · 26% EPSS · CVSS 9.8 Critical.

Data as of 26 Oct 2026, 06:26 UTC

CVSS 3.0 base score
9.8Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
26.8% chance of exploitation in 30 days – higher than 98% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 6 Jun 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 2026-10-26.

What to do

Affected software

ProductAffectedFixed in
PyPIpytorch-lightningpkg:pypi/pytorch-lightningOther threatsAffected
  • =2.2.1
Fixed in–

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

AI-suggested

1. Immediately disable or restrict access to the delta endpoint in pytorch-lightning deployments until a patch is available. 2. Monitor official lightning-ai channels for security updates and apply patches promptly once released. 3. Implement strict input validation and sanitization on all data deserialized by the application, especially from untrusted sources. 4. Employ network segmentation and firewall rules to limit access to pytorch-lightning services only to trusted internal users and systems. 5. Use application-layer security controls such as Web Application Firewalls (WAFs) to detect and block suspicious payloads targeting the delta endpoint. 6. Conduct thorough code reviews and penetration testing focusing on deserialization and object attribute handling. 7. Consider deploying pytorch-lightning instances behind VPNs or secure tunnels to reduce exposure. 8. Educate development and operations teams about the risks of deserialization vulnerabilities and secure coding practices related to dynamic attribute modification.

Description

A remote code execution (RCE) vulnerability exists in the lightning-ai/pytorch-lightning library version 2.2.1 due to improper handling of deserialized user input and mismanagement of dunder attributes by the `deepdiff` library. The library uses `deepdiff.Delta` objects to modify application state based on frontend actions. However, it is possible to bypass the intended restrictions on modifying dunder attributes, allowing an attacker to construct a serialized delta that passes the deserializer whitelist and contains dunder attributes. When processed, this can be exploited to access other modules, classes, and instances, leading to arbitrary attribute write and total RCE on any self-hosted pytorch-lightning application in its default configuration, as the delta endpoint is enabled by default.

References

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