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Attributed metadata history

REPORTPUBLIC METADATA

Rolling the cyber dice with open-source and open-weight AI models

With typical cybersecurity exposure, I can conduct pen testing with deterministic tools. I am able to predict how a piece of software is going to respond. I even stand a decent chance of finding vulnerabilities before they can be exploited against me. What we are dealing with now is a new kind of exposure. For LLMs and AI models, there are invisible risks that are unscannable and virtually undetec

Reference details

Source category
Research & news
Report title
Rolling the cyber dice with open-source and open-weight AI models
First reported
Oct 02, 2026 09:00 UTC
Last reported
Oct 02, 2026 09:00 UTC
Catalog updated
Oct 08, 2026 03:43 UTC
Record ID
5efbd3e19c2e21ee8f56e5461111c3c311f531f16dbecfd76a72907edbe8c328

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_cdcffed32aa78fd61f

Source-provided label
Not reported
Evidence class
mentioned in report
Confidence
Not reported
Observed
Oct 02, 2026 09:00 UTC
Retrieved
Oct 08, 2026 03:43 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_cdcffed32aa78fd61fCollection 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.