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The Hacker News

⚡ Weekly Recap: ShareFile Threat, Citrix Bleed 2 Ransomware, AI Coding Attacks, and More

Somewhere right now, a security tool is quietly finding bugs faster than any human can fix them. That's supposed to be the good news. The catch is that the attackers have the same tools, pointed the other way, and they don't file tickets. That's the shape of this week. Trusted code turns on the people who installed it. Old bugs from last year are still landing because the fix sat in a queue too

The Hacker NewsRead original
ExternalCybersecurity
Krebs on Security

Lessons Learned from CISA’s Recent GitHub Leak

The Cybersecurity and Infrastructure Security Agency (CISA) has issued a postmortem on a data leak in which a contractor published dozens of internal CISA credentials -- including AWS Govcloud keys -- in a public GitHub repository for almost six months before being notified by KrebsOnSecurity. Experts say the gaps identified in the agency's initial response provide important lessons that all security teams should absorb.

A Little SunshineData BreachesLatest Warnings
Krebs on SecurityRead original
ExternalSoftware Engineering
DZone

Performance Testing RAG Applications: Complete Engineering Guide

In this blog post, we will see how to perform a performance test on a retrieval-augmented generation (RAG) application properly, covering both speed and correctness, and how to wire both into a CI/CD pipeline so regressions get caught before they reach production. Performance testing a RAG application requires two separate testing gates: one for speed and one for answer quality. Traditional load testing tools measure response times but cannot detect hallucinations, where a model returns fast but

DZoneRead original
ExternalAI
NVIDIA Technical Blog

Extreme Event Likelihoods with Guided Generative Models

Across science, engineering, and finance, many of the most important risks come from low-likelihood, high-impact events. Estimating the probability of these... Across science, engineering, and finance, many of the most important risks come from low-likelihood, high-impact events. Estimating the probability of these events with brute-force Monte Carlo sampling—running a model repeatedly with randomly drawn inputs to estimate the probability of rare outcomes—can require an excessive volume of mode

NVIDIA Technical BlogRead original

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