Why AWS, Terraform, and Kubernetes Are Must-Have Cloud Skills by 2026

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The cloud industry is rapidly evolving, and simply knowing how to launch a virtual machine is no longer sufficient for modern infrastructure roles. A combination of AWS, Terraform, and Kubernetes has emerged as a core skill set, enabling teams to build scalable, automated, and highly available applications. Terraform allows engineers to define and manage cloud infrastructure as code, making it repeatable, version-controlled, and auditable. Kubernetes automates the deployment and management of containerized workloads, addressing the complexity of running large numbers of microservices. Complementary tools like Docker, Git, and the Go programming language round out a skill set increasingly in demand across the cloud-native ecosystem.

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Critical GiveWP flaw allows unauthenticated attackers to execute server commands

A critical vulnerability, tracked as CVE-2026-82222, has been discovered in GiveWP, a popular WordPress donation plugin, affecting versions 4.16.7.1 and earlier. The flaw allows unauthenticated attackers to chain multiple weaknesses — including a registration bypass, PHP object injection via the last_name field, and a TCPDF gadget chain — to achieve remote code execution on vulnerable servers. Exploitation requires no user interaction and can be carried out entirely through API requests, making it particularly dangerous. Successful attacks could lead to theft of donor data and database credentials, installation of web shells, and lateral movement within the hosting environment. Site administrators are urged to update GiveWP to version 4.16.7.2 or later and audit their installations for suspicious accounts, sessions, and legacy forms.

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ProgrammingDEV Community ·

PaperCut Issues Second Emergency Patch for Actively Exploited RCE Vulnerabilities

PaperCut has released a second emergency patch addressing two critical flaws, CVE-2026-81578 and CVE-2026-82078, affecting its MF and NG print management software. The vulnerabilities chain an authentication bypass with insecure dynamic class loading, allowing unauthenticated attackers to execute Java code with SYSTEM-level privileges on the server. The initial fix left residual bypass vectors, making the Release 2 update mandatory for full remediation. Active exploitation has been confirmed, with attackers observed stealing credentials, deploying additional payloads, and moving laterally within networks. Administrators are urged to apply Release 2 immediately and restrict management interface access to trusted IPs or VPNs.

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ProgrammingDEV Community ·

Zabbix Agent Flaw Lets Low-Privileged Users Escalate to Admin via Malicious DLL

A medium-severity vulnerability, tracked as CVE-2026-59781, has been disclosed in Zabbix agent versions prior to 7.0.24 and 7.4.8. The flaw stems from incorrect file access permissions during installation, allowing a local low-privileged attacker to plant a malicious DLL in a path searched by the installer. When an administrator runs the vulnerable installer, it inadvertently loads the attacker's DLL, executing arbitrary code with administrator privileges. Successful exploitation could lead to privilege escalation, credential theft, persistence, and disruption of endpoint monitoring. Users are advised to upgrade to Zabbix agent 7.0.24, 7.4.8, or later, and to restrict write permissions on installer directories.

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ProgrammingDEV Community ·

Why RAG Systems Need Independent Layer Metrics, Not Just Output Reviews

Retrieval-Augmented Generation (RAG) systems can produce fluent, confident-sounding answers even when the underlying retrieval is failing, making eyeball evaluation dangerously misleading. A real-world demo incident revealed a system retrieving the correct document only 30% of the time, yet appearing flawless because demo questions were hand-picked. Experts argue RAG evaluation must measure the retrieval and generation layers independently using metrics such as Hit Rate, MRR, Precision@k, and NDCG. Hit Rate at k=5 is considered the single most critical metric, with scores below 80% pointing squarely to a broken retriever. Without structured, labelled evaluation sets and layer-specific scoring, teams have no reliable way to identify where quality is being lost or where to focus fixes.

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