I will secure your ai chatbot and llm app against prompt injection attacks

S
suliman_khan7
S
suliman_khan7
Suliman Khan
secure your ai chatbot and llm app against prompt injection attacks
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secure your ai chatbot and llm app against prompt injection attacks
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secure your ai chatbot and llm app against prompt injection attackssecure your ai chatbot and llm app against prompt injection attackssecure your ai chatbot and llm app against prompt injection attacks

About this gig

Worried your AI chatbot or LLM app could leak sensitive data, produce unsafe output, or be manipulated by abusive user input? I'll harden it against the OWASP LLM Top 10 risks defensively, methodically, with a clear remediation report.

I will perform a defensive AI safety and assurance review of YOUR OWN LLM application or chatbot:

OWASP LLM Top 10 risk review (LLM01 to LLM10)

Guardrail and input-handling assessment

Sensitive-data leakage and output-filter review

RAG / document-ingestion safety check (indirect-input risks)

Rate-limiting and resource-abuse exposure review

Written PDF report with risk ratings, evidence, and fixes

WHY ME:

Built a production AI safety gateway from scratch (public on GitHub)

Research background in AI safety and cloud-native engineering

Maps findings to OWASP LLM Top 10, NIST AI RMF, ISO 42001

Defensive review only your system, your authorization

STANDARDS & TOOLING:

OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS, ISO 42001, promptfoo, Garak

SUPPORTED PLATFORMS:

OpenAI, Anthropic Claude, Groq, Llama (Ollama), Mistral, Gemini, custom RAG, AI agents, chatbots

YOU GET:

PDF executive-summary report

Detailed findings with severity ratings

Concrete fix recomme

  • Model expertise

    • Custom model development
    • Fine-tuning models
    • Predicitive analyatics
    • Recommendation systems

  • Industry

    • Cyber security

  • Programming language

    • C++
    • Go
    • PHP
    • Python
    • React
    • PyTorch

  • Language

    • English

  • Technical expertise

    • Machine learning (Supervised, Unsupervised, Reinforcement)
    • Deep learning (Neural networks, GANs)
    • Computer Vision (Object detection, Image recognition)
    • Reinforcement learning (Decision-making systems)
    • AI ethics and bias mitigation

Get to know Suliman Khan

Suliman Khan

Cloud Native Security and DevSecOps Engineer, Kubernetes CI CD AI Safety

  • FromPakistan
  • Member sinceAug 2025
  • Avg. response time1 hour

  • Languages

    English
I'm Suliman Khan — a cloud-native security engineer focused on Kubernetes, DevSecOps, and AI/LLM safety. I help teams ship secure containerized apps, harden K8s clusters with Kyverno + CIS benchmarks, build CI/CD pipelines with SBOM and image signing (Sigstore), and protect LLM apps against OWASP Top 10 risks. Background: BS CS from FAST-NUCES, IEEE TIFS author, 5 flagship defensive projects on GitHub. Defensive-first. Measurable outcomes. On-time delivery. Free 10-min scoping call.

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