Tag: llm

All the articles with the tag "llm".

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  llm-security

  5 Aug, 2026 · 6 min read

  Starving the Model: LLM Denial of Service

  Context-window exhaustion and denial-of-wallet attacks against LLM applications, and the rate limiting, input caps, and resource budgeting that contain them.

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  llm-security

  22 Jul, 2026 · 7 min read

  Poisoning the Well: Training-Data Attacks

  How poisoned training data plants backdoors and bias in language models, where it enters the pipeline, and the defenses that catch it early.

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  llm-security

  15 Jul, 2026 · 8 min read

  Insecure Output Handling

  Why model output must be treated as untrusted input, how it becomes XSS, SSRF, and code execution downstream, and the encoding and validation that contain it.

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  llm-security

  8 Jul, 2026 · 7 min read

  Prompt Injection: The XSS of LLMs

  How prompt injection subverts large language models through direct and indirect input, why it has no clean fix, and the layered defenses that contain it.

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  llm-security

  1 Jul, 2026 · 9 min read

  The OWASP Top 10 for LLMs: A Field Guide

  A field guide to the OWASP Top 10 for LLM Applications, the ten vulnerability categories that define modern AI application security.

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  llm-concepts

  1 Jun, 2026 · 8 min read

  Personal AI Infrastructure: The Climb From Chatbot to Assistant

  Most AI tools today are chatbots in a costume. The real ladder has three tiers, and 2026 finally made the top one buildable at home.

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  ai

  21 Apr, 2026 · 8 min read

  Context Windows: Why Your AI Has a Working Memory Limit

  Context windows are not memory. They are working memory. Here is what the model can see right now, why extending that limit is hard, and what it costs to try.

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  ai

  20 Apr, 2026 · 9 min read

  Positional Encoding and Sampling: How the Transformer Finds Position and Picks Its Next Word

  Attention cannot tell 'the dog bit the man' from 'the man bit the dog.' Positional encoding fixes that. Then sampling decides what word the model actually says.

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  ai

  20 Apr, 2026 · 7 min read

  Tokens and Embeddings: How Raw Text Becomes Numbers the Model Can Use

  Before the transformer can do anything, it must turn your prompt into numbers. Here is exactly how that works, from raw characters to dense vectors.