Sat.Apr 23, 2022

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T-Mobile Breached Again; Lapsus$ Behind the Attack

Data Breach Today

Company Says No Sensitive Customer or Government Information Leaked The US telecom carrier T-Mobile has confirmed that the Lapsus$ ransomware group has breached its internal network by compromising employee accounts. The company says hackers did not steal any sensitive customer or government information during the incident.

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T-Mobile confirms Lapsus$ had access its systems

Security Affairs

Telecommunication giant T-Mobile confirmed the LAPSUS$ extortion group gained access to its networks in March. Telecom company T-Mobile on Friday revealed that LAPSUS$ extortion gang gained access to its networks. The popular investigator and journalist Brian Krebs first surmised that the LAPSUS$ gang has breached T-Mobile after he reviewed a copy of the private chat messages between members of the cybercrime group.

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Phishing attacks using the topic “Azovstal” targets entities in Ukraine

Security Affairs

Ukraine CERT-UA warns of phishing attacks on state organizations of Ukraine using the topic “Azovstal” and Cobalt Strike Beacon. The Computer Emergency Response Team of Ukraine (CERT-UA) warns of phishing attacks aimed at organizations in the country using the topic “Azovstal” The phishing message use the subject “Azovstal” and a weaponized office document.

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Are you using Java 15/16/17 or 18 in production? Patch them now!

Security Affairs

A researcher has released proof-of-concept (PoC) code for a digital signature bypass vulnerability in Java. Security researcher Khaled Nassar released a proof-of-concept (PoC) code for a new digital signature bypass vulnerability, tracked as CVE-2022-21449 (CVSS score: 7.5), in Java. The vulnerability was discovered by ForgeRock researcher Neil Madden, who notified Oracle on November 11, 2021.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.