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Attributed metadata history

REPORTPUBLIC METADATA

Machine Learning Attack Series: Backdooring Keras Models and How to Detect It

This post is part of a series about machine learning and artificial intelligence. Adversaries often leverage supply chain attacks to gain footholds. In machine learning model deserialization issues are a significant threat, and detecting them is crucial, as they can lead to arbitrary code execution. We explored this attack with Python Pickle files in the past. In this post we are covering backdoor

Reference details

Source category
Research & news
Report title
Machine Learning Attack Series: Backdooring Keras Models and How to Detect It
First reported
May 18, 2024 23:00 UTC
Last reported
May 18, 2024 23:00 UTC
Catalog updated
Oct 08, 2026 03:44 UTC
Record ID
ecba6e5b47df5ef3ab0705368c3d197a8c60ec229e567e8a29870e0ab04ff371

Published indicators

Network indicators are visibly defanged. This page does not make them clickable or send them to another service.

No validated file hashes or network indicators are stored for this record.

Source observations

src_c25d91fa3ad9ae9ac7

Source-provided label
Not reported
Evidence class
mentioned in report
Confidence
Not reported
Observed
May 18, 2024 23:00 UTC
Retrieved
Oct 08, 2026 03:44 UTC

This record contains metadata and attribution only. It does not host, download, unpack, render, or execute malware samples.

Feed collection status

Sources represented on this page. Publication and observation dates describe the content; successful batch dates describe local collection. A recent batch does not certify complete coverage or current provider availability.

1 sources: 0 stale, 0 needing attention, 0 paused, 1 unverified.

Review source update dates and status
  • src_c25d91fa3ad9ae9ac7Collection status unverifiedLast successful batch: Unverified

Stale means older than twice the recorded collection interval, with a minimum of one hour; without an interval, the threshold is 24 hours. Cached content remains available while a feed is stale, paused, or unverified.