mlrun up to 1.12.0-rc3 DataFrame Hash mlrun/utils/helpers.py mlrun.utils.helpers.calculate_dataframe_hash weak hash
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 3.3 | $0-$5k | 1.26- |
Summary
A vulnerability was found in mlrun up to 1.12.0-rc3. It has been declared as problematic. Affected is the function mlrun.utils.helpers.calculate_dataframe_hash of the file mlrun/utils/helpers.py of the component DataFrame Hash Handler. The manipulation results in weak hash.
This vulnerability is identified as CVE-2026-10766. The attack is only possible with local access. Additionally, an exploit exists.
The pull request to fix this issue awaits acceptance.
Details
A vulnerability classified as problematic has been found in mlrun up to 1.12.0-rc3. Affected is the function mlrun.utils.helpers.calculate_dataframe_hash of the file mlrun/utils/helpers.py of the component DataFrame Hash Handler. The manipulation with an unknown input leads to a weak hash vulnerability. CWE is classifying the issue as CWE-328. The product uses an algorithm that produces a digest (output value) that does not meet security expectations for a hash function that allows an adversary to reasonably determine the original input (preimage attack), find another input that can produce the same hash (2nd preimage attack), or find multiple inputs that evaluate to the same hash (birthday attack). This is going to have an impact on integrity, and availability.
The advisory is shared for download at github.com. This vulnerability is traded as CVE-2026-10766. The exploitability is told to be difficult. The attack needs to be approached locally. Technical details and a public exploit are known. The MITRE ATT&CK project declares the attack technique as T1600.001.
The exploit is shared for download at github.com. It is declared as proof-of-concept. The pull request to fix this issue awaits acceptance.
There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.
VulDB is the best source for vulnerability data and more expert information about this specific topic.
Product
Name
Version
Website
- Product: https://github.com/mlrun/mlrun/
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 3.6VulDB Meta Temp Score: 3.3
VulDB Base Score: 3.6
VulDB Temp Score: 3.3
VulDB Vector: 🔒
VulDB Reliability: 🔍
CVSSv2
| AV | AC | Au | C | I | A |
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| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
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| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: Weak hashCWE: CWE-328 / CWE-327 / CWE-310
CAPEC: 🔒
ATT&CK: 🔒
Physical: Partially
Local: Yes
Remote: No
Availability: 🔒
Access: Public
Status: Proof-of-Concept
Download: 🔒
Price Prediction: 🔍
Current Price Estimation: 🔒
| 0-Day | Unlock | Unlock | Unlock | Unlock |
|---|---|---|---|---|
| Today | Unlock | Unlock | Unlock | Unlock |
Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: no mitigation knownStatus: 🔍
0-Day Time: 🔒
Timeline
06/03/2026 Advisory disclosed06/03/2026 VulDB entry created
06/03/2026 VulDB entry last update
Sources
Product: github.comAdvisory: 9691
Status: Not defined
Confirmation: 🔒
CVE: CVE-2026-10766 (🔒)
GCVE (CVE): GCVE-0-2026-10766
GCVE (VulDB): GCVE-100-368136
scip Labs: https://www.scip.ch/en/?labs.20161013
Entry
Created: 06/03/2026 17:45Changes: 06/03/2026 17:45 (58)
Complete: 🔍
Submitter: Dem0
Cache ID: 216::103
Submit
Accepted
- Submit #831419: mlrun v1.12.0-rc3 Hash Collision (by Dem0)
VulDB is the best source for vulnerability data and more expert information about this specific topic.
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