Practical guide to hardening the AI supply chain by eliminating pickle risks with SafeTensors, verifying SHA-256 integrity, scanning weights with Fickling and ModelScan, detecting Keras Lambda backdoors, auditing dependencies with pip-audit, generating CycloneDX SBOMs with Syft, and governing API model behavior.

Technical analysis of Retrieval-Augmented Generation (RAG) architecture, inference-time data poisoning, context injection, and defensive engineering across vector retrieval pipelines.

Practical forensic investigation of AI supply chain attack vectors, dissecting Python pickle deserialization RCE, dependency confusion, typosquatting, and prompt template injection.

Comprehensive analysis of AI supply chain attack surfaces across models, datasets, frameworks, and dependencies, covering serialization vulnerabilities, transfer learning risks, and real-world incidents.

Detailed walkthrough and prompt injection methodology for the TryHackMe White Rabbit challenge, exploiting Agent Smith v1.0 to escape the Matrix and capture all three flags.

Practical walkthrough and exploitation analysis of the TryHackMe LLMborghini challenge, demonstrating indirect prompt injection against CalBot to exfiltrate confidential branch sales data.

A complete walkthrough of the TryHackMe room Prompt Defence (Module 3), analyzing probabilistic security, system prompt hardening, input/output guardrails, deployment least privilege, and bypassing hardened defenses.

A complete walkthrough of the TryHackMe room Jailbreaking (Module 3), exploring safety alignment, the helpfulness-harmlessness paradox, classic jailbreak methods, multi-turn conditioning, and the TryJailbreakMe challenge.

A complete walkthrough of the TryHackMe room Prompt Injection (Module 3), understanding context windows, direct overrides, simulated dialogue injection, indirect prompt injection vectors, and practical agent exploitation.

A complete walkthrough of the TryHackMe challenge room AI Threat Modelling Assessment (Module 2 capstone), applying STRIDE and OWASP LLM Top 10 frameworks to audit enterprise AI systems and retrieve both assessment flags.