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THE DAILY INDEX

Technology, AI, software, security, crypto and business

ATTACKS & VULNERABILITIES

TLDR InfoSec

OVER 3M RECORDS, INCLUDING PII OF STUDENT-ATHLETES AND COLLEGE COACHES EXPOSED IN DATA BREACH

Security researcher Jeremiah Fowler discovered a publicly accessible, unencrypted, and non-password-protected database belonging to PrepHero, which is a platform to assist high school athletes with securing scholarships. The database contained over 3M records that included full names, phone numbers, physical addresses, and passport data of students, as well as contacts of parents and college sports coaches. Fowler also discovered a CSV file containing links to passport images of student athletes and a mail archive that included PII sent to coaches.

WHAT IT TAKES TO DEFEND A CYBERSECURITY COMPANY FROM TODAY'S ADVERSARIES

Sentinel One details some of the threats it faces as a security vendor. It has faced North Korean threat actors trying to infiltrate its organization via workers, threat actors probing its network and tools to develop bypasses, and Chinese state-sponsored threat actors targeting supply chains that impact Sentinel One customers. Sentinel One urges practitioners to distribute threat intel information across operational stakeholders to expand detection, integrate context into attribution activities, and expand supply chain threat monitoring.

FRESH FROM THE DOCKS: UNCOVERING 100,000 VALID SECRETS IN DOCKERHUB

GitGuardian's analysis of 15 million DockerHub images revealed 100,000 valid secrets exposing databases, AWS infrastructures, GitHub Enterprise instances, and Artifactory repositories. 97% were found in image layers, with 60% created before 2024 and over 2,000 dating back to 2020, showing secrets often go unrotated for years. Docker images leak secrets due to build issues like persistent layers and build argument leaks, making regular security audits essential, as prevention is cheaper than responding to breaches.

CAN WE STOP DOCUMENTING OUR DETECTIONS?

Documentation is important for detections to explain to SOC analysts what the rule detects, how to investigate the alert, any necessary enrichments, how to identify a false positive, and any necessary steps if it is a true positive. This post compares documentation with rules generated by LLMs to those written by humans using examples from Elastic's open-source rule sets. The evaluation found that roughly 60% of investigative steps provided by the LLM matched human-created ones, but also noted that humans tended to provide a larger number of detailed investigative steps.

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