LLM-Unbound-KrishnaG-CEO

LLM10:2025 – Unbounded Consumption in LLM Applications: Business Risk, ROI, and Strategic Mitigation

At its core, Unbounded Consumption refers to an LLM application’s failure to impose constraints on inference usage—resulting in an open door for resource abuse. Unlike traditional software vulnerabilities that might involve code injection or data leakage, Unbounded Consumption exploits the operational behaviour of the model itself—by coercing it into performing an excessive number of inferences.

Sudo-Upgrade-Ubuntu-Linux-KrishnaG-CEO

Sudo in the Spotlight: Strategic, Secure, and Scalable Access Management

Upgrade Sudo demonstration in Ubuntu Linux 24.04 LTS from v1.9.15p5 to v1.9.17. CLI with video explanation even for beginners and Geeks.

Ubuntu-Linux-Kernel-Upgrade-KrishnaG-CEO

Upgrading Ubuntu Kernel v6.15.2

Upgrading Ubuntu Kernel v6.15.2 🔐 Linux Kernel v6.15 – What’s New & Security-Relevant 🗓️ Released: 26 May 2025 (With follow-up patches v6.15.1 and v6.15.2) 🗓️ Released: v16.15.2 on 10th June 2025 1. 🛡️ Security Enhancements 🔸 LSM (Linux Security Modules) Improvements 🔸 Spectre/Meltdown Updates 🔸 Kernel Address Space Layout Randomisation (KASLR) 2. 🔗 Networking Security …

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MetaSploit-PenTest-KrishnaG-CEO

The Ultimate Guide to Metasploit Alternatives for Penetration Testers

When it comes to offensive security and penetration testing, Metasploit Framework is a name that needs no introduction. As a powerful and widely adopted open-source platform, Metasploit continues to be a staple in the arsenal of security professionals. However, in recent years, several alternatives and competitors have emerged, offering varied capabilities in red teaming, post-exploitation, command and control (C2), and exploit development.

AI-RAG-Vulnerabilities-KrishnaG-CEO

LLM08:2025 – Vector and Embedding Weaknesses: A Hidden Threat to Retrieval-Augmented Generation (RAG) Systems

Retrieval-Augmented Generation is an advanced technique that augments pre-trained LLMs with external, domain-specific knowledge bases. Instead of relying solely on static training data, RAG-enabled models retrieve real-time contextual information, thereby enhancing relevance and accuracy.