Dataset corruption via schema-blind DataFrame hashing
Published Jun 3, 2026 · Updated Jun 3, 2026
Weak DataFrame hashing in Iguazio MLRun 1.12.0-rc1 through 1.12.0-rc3 allows local users to corrupt dataset artifacts. calculate_dataframe_hash hashes normalized pandas values without schema or dtype metadata, so distinct DataFrames can receive the same hash-derived path. Authenticated users with artifact write access must supply colliding DataFrames, causing silent overwrite, stale reads, or incorrect metadata.
Summary
What happened
Weak DataFrame hashing in Iguazio MLRun 1.12.0-rc1 through 1.12.0-rc3 allows local users to corrupt dataset artifacts. calculate_dataframe_hash hashes normalized pandas values without schema or dtype metadata, so distinct DataFrames can receive the same hash-derived path. Authenticated users with artifact write access must supply colliding DataFrames, causing silent overwrite, stale reads, or incorrect metadata.
The record
- CVE
- CVE-2026-10766
- Published
- Jun 3, 2026
- Updated
- Jun 3, 2026
- Vendor
- Iguazio
- Product
- MLRun
- Classifications
- CWE-327, CWE-328, T1565.001
- Attack vector
- local
- Privileges
- authenticated
Timeline
How it unfolded
- Jun 3, 2026CVE publishedPublication date reported by the CVE source.
- Jun 3, 2026Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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Is a vulnerable build present?
Compare these published version ranges with your installed build and any vendor patches.
- Affected versionversion=1.12.0-rc1
- Affected versionversion=1.12.0-rc2
- Affected versionversion=1.12.0-rc3
What conditions does exploitation require?
What is affected?
Attacks
What attackers are doing with it
Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.
Weakness, pattern, technique
Public exploit references
- DataFrame hash collision reproducerproof of concept · demonstrated
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Technologies
Your stack
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Your stack
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