Cybersecurity Projects in NairobiCybersecurity Projects in NairobiDesigned and implemented a hands-on AI red teaming environment using Microsoft PyRIT to evaluate the security of a local Retrieval-Augmented Generation application. The project connected a Parrot OS testing environment to a Windows-based AnythingLLM RAG application, with DeepSeek running locally through LM Studio.
I configured PyRIT to communicate with the AnythingLLM workspace through a custom HTTP target and used controlled adversarial objectives to examine how the application handled requests involving financial records, customer information, payroll data, and restricted credentials. The testing workflow included API connectivity validation, isolated Python environment setup, automated prompt execution, response collection, and evidence review.
The lab was built entirely around fictional Kiso Secure business data, allowing realistic sensitive-data disclosure scenarios to be tested without using real customer information or credentials. The resulting workflow demonstrates how automated AI red teaming can be used to identify potential data leakage, retrieval-boundary weaknesses, and unsafe handling of sensitive information in AI-powered applications. This project focused on building and testing a practical AI security assessment lab for evaluating LLM defenses against prompt injection and jailbreak attacks.
I integrated Spikee by Reversec with a locally hosted cybersecurity model running through LM Studio, then added NVIDIA NeMo Guardrails to compare model behavior under three conditions: no guardrails, input filtering, and combined input/output protection.
The work included configuring the local model environment, building a custom FastAPI gateway, integrating NeMo Guardrails, troubleshooting model latency and timeout issues, creating a reusable Spikee target, and analyzing attack results using Spikee’s built-in reporting tools.
The project also explored different adversarial testing approaches, including prompt injection datasets, obfuscation, encoded attacks, Best-of-N testing, synthetic canary leakage tests, and structured benchmark comparisons.
The objective was to measure how much the guardrails reduced successful attacks while keeping the model, dataset, and testing conditions consistent.
Tools used: Spikee, NVIDIA NeMo Guardrails, LM Studio, Python, FastAPI, PowerShell, Parrot OS, local LLMs, JSONL datasets, and custom security testing scripts.
This project demonstrates a hands-on approach to LLM red teaming, AI safety testing, prompt-injection assessment, and guardrail validation for organizations deploying generative AI systems. 
