Freelance Security Engineers in Nairobi
Freelance Security Engineers in Nairobi
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Stephen Kisong'e
Nairobi, Kenya
Cyber Security Analyst
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Cyber Security Analyst
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Built a controlled Kiso Secure AI red teaming lab using Promptfoo to test a local RAG application for prompt injection, system prompt leakage, indirect prompt injection, and sensitive data exposure. The setup used AnythingLLM with a Kiso Secure knowledge base, automated adversarial testing, and a hardened workspace to compare security behavior before and after remediation.
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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.
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I conducted an authorized AI security assessment of Lily Cybersecurity 7B to evaluate how effectively a hardened system prompt could resist jailbreak and prompt injection attacks. Using Prompt Fuzzer, I ran 15 attack techniques against the model. The system prompt successfully blocked 8 attempts, including most roleplay and social engineering attacks. The successful bypasses mainly used translated or altered wording to disguise the intent of the request. This project demonstrates why system prompts should be supported by input validation, moderation, output filtering, and continuous AI red team testing.
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Designed and documented a secure environment for running Robin, an AI-powered dark web OSINT tool, inside a compartmentalized Qubes OS and Qubes-Whonix setup. The environment was structured to separate research activity, Tor-routed traffic, sensitive credentials, untrusted content, and final reporting. Robin was deployed through Docker inside a dedicated research qube, with traffic routed through sys-whonix and sensitive notes stored separately in an offline vault qube. Disposable qubes were incorporated for opening potentially unsafe links and files. The setup was built around security by compartmentalization rather than relying on a single tool for protection. Particular attention was given to Docker mount restrictions, credential hygiene, network-boundary verification, lawful research scope, and keeping raw research data isolated from personal or client environments. The final result was a repeatable AI-assisted OSINT workflow that supports faster search refinement, result filtering, investigation summarization, and structured reporting while maintaining stronger operational security and clearer separation between collection, analysis, and final output.
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263
Security Engineer
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Moses Mwangau
Nairobi, Kenya
Passionate Security Engineer & Developer
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Passionate Security Engineer & Developer
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Web Development Portfolio
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24
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Penetration Testing Project
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63
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Devops Host on Azure
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30
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