KALI LINUX LLMs SECURITY: Develop Security Methods in AI Models with High-Performance Tools

KALI LINUX LLMs SECURITY: Develop Security Methods in AI Models with High-Performance ToolsThis book presents a practical approach to auditing, defense, and security validation in applications with LLMs using Kali Linux as the central platform for laboratory, automation, and evidence production. Aimed at professionals, students, and cybersecurity operators, the content demonstrates how to analyze language models, inference pipelines, RAG, vector databases, autonomous agents, plugins, external tools, logs, and downstream systems in authorized AI Security scenarios.The work explores current risks in generative AI based on OWASP Top 10 for LLM Applications 2025 and OWASP Top 10 for Agentic Applications 2026, connecting prompt injection, jailbreaks, data leakage, insecure output handling, excessive agency, data poisoning, failures in embeddings, supply chain, uncontrolled consumption, and attacks against agents. Workflows with Python, Bash, Docker, curl, jq, grep, FastAPI, Pydantic, ChromaDB, Garak, PyRIT, Wireshark, tcpdump, JSONL logs, synthetic canaries, schemas, allowlists, AI Gateways, output validation, hardening, monitoring, and incident response in applications with LLMs are also covered.You will learn to: * Build LLM Security laboratories in Kali Linux with isolation, evidence, and automation * Analyze inference pipelines, prompts, context, RAG, embeddings, and vector databases * Test prompt injection, jailbreaks, data leakage, and insecure output handling * Validate autonomous agents, plugins, tools, external APIs, and downstream systems * Apply OWASP LLM 2025 and OWASP Agentic 2026 to real threat models * Use Python, Docker, curl, jq, grep, Garak, and PyRIT in authorized audits * Build hardening, observability, incident response, and maturity controls in AI securityBy the end, you will be able to execute complete LLM Security routines with Kali Linux, integrating technical reconnaissance, controlled adversarial tests, RAG validation, agent analysis, hardening, monitoring, evidence collection, incident response, and production of professional reports for audits, authorized AI red teaming, and defense of applications with generative AI.

Pagina's: 484, Paperback, Independently published