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NVD
NVD
•added 2026/07/29 7:16 a.m.•18 views

CVE-2026-63241

An insecure direct object reference vulnerability in Koollab LMS allowed an authenticated user to query the course completion progress of any other user without authorisation, disclosing private learning progress information...

3.1CVSS0.00221EPSS
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Vulnrichment
Vulnrichment
•added 2026/07/29 6:30 a.m.•16 views

CVE-2026-63241 Insecure direct object reference vulnerability

An insecure direct object reference vulnerability in Koollab LMS allowed an authenticated user to query the course completion progress of any other user without authorisation, disclosing private learning progress information...

3.1CVSS5.9AI score0.00221EPSS
SaveExploits0References1
Rapid7 Vulnerability Database (full)
Rapid7 Vulnerability Database (full)
•added 2026/07/29 12:00 a.m.•4 views

CVE-2026-63241: Authorization Bypass Through User-Controlled Key

An insecure direct object reference vulnerability in Koollab LMS allowed an authenticated user to query the course completion progress of any other user without authorisation, disclosing private learning progress information...

3.1CVSS5.8AI score0.00221EPSS
SaveExploits0References2
Positive Technologies
Positive Technologies
•added 2026/07/29 12:00 a.m.•17 views

PT-2026-65712

An insecure direct object reference vulnerability in Koollab LMS allowed an authenticated user to query the course completion progress of any other user without authorisation, disclosing private learning progress information...

3.1CVSS5.9AI score0.00221EPSS
SaveExploits0References1
Packet Storm News
Packet Storm News
•added 2025/06/09 12:00 a.m.•16 views

Correlated Noise Mechanisms for Differentially Private Learning

This monograph explores the design and analysis of correlated noise mechanisms for differential privacy DP, focusing on their application to private training of AI and machine learning models via the core primitive of estimation of weighted prefix sums. While typical DP mechanisms inject...

6.8AI score
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Packet Storm News
Packet Storm News
•added 2025/05/10 12:00 a.m.•13 views

An \Tilde{O}Ptimal Differentially Private Learner for Concept Classes with VC Dimension 1

We present the first nearly optimal differentially private PAC learner for any concept class with VC dimension 1 and Littlestone dimension $d$. Our algorithm achieves the sample complexity of $\tildeO\varepsilon,δ,α,δ\log^ d$, nearly matching the lower bound of $Ω\log^ d$ proved by Alon et al...

6.8AI score
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