Selected Engineering Work
x402Shield
Verified selected engineering work for Rust-based AI-agent, MCP-tool, and API-payment security.
View caseAI System & Model Security Review
From THB 280,000~US$8,330Reference date: Oct 9, 2026 · Local price is authoritative; this is not a payment or settlement rate.
Buyer outcome
A scoped security review of a deployed or near-launch AI system for Thai businesses — LLM workflow, LINE OA bot, internal assistant, retrieval system, or API-connected automation. Written findings covering prompt injection in Thai-language channels, data exposure, permission boundaries, and remediation priorities before wider rollout.
Why security-led AI integration matters
AI systems connect directly to user inputs, internal data, APIs, and business tools. Without a deliberate security review, common failure modes go undetected: prompt injection through LINE OA or Messenger chat inputs, data exposure through retrieval systems, unsafe outputs reaching users or downstream systems, and actions taken without human approval.
This review focuses on prompt injection in Thai-language bot inputs from LINE OA and Messenger channels, overly permissive retrieval where customer records, pricing data, and booking system data are accessible to AI without scope limits, insecure output handling where AI-generated content is rendered directly in LINE messages without review, unsafe API connections from chatbot platforms to PMS, POS, or CRM — common with third-party LINE OA integration platforms — and webhook security gaps including token exposure and PII in webhook payloads. Data handling is reviewed with Thailand-appropriate data handling practices and applicable personal data protection obligations in mind.
BilgeQor's AI services are built around written scope, approved data sources, permission boundaries, human approval points, and review before wider rollout.
Scope drivers
Number of AI workflows or systems reviewed
Internal vs customer-facing exposure — LINE OA, Messenger, web chat, helpdesk, CRM, PMS, POS, or internal tool
Model, tool, and integration architecture — including LINE OA platform and third-party chatbot platform configuration
Knowledge source and retrieval design
Permission boundaries and action capability
Sensitive data exposure and handling risk
Prompt injection and abuse scenario depth — including Thai-language adversarial inputs
Required remediation and retest depth
Ideal for
- Thai hospitality, retail, logistics, and clinic teams preparing to launch or expand an AI feature
- Businesses with LINE OA chatbots, internal assistants, or retrieval-based knowledge systems
- Technical teams or business owners who have built an AI workflow on LINE OA, Manychat, ChatPlatform, or similar platforms
- Operations and support teams using AI-connected LINE, Messenger, CRM, PMS, or helpdesk workflows
- Leaders who want practical risk findings and remediation priorities before wider rollout
What is included
- AI system and workflow scope review
- Prompt injection and abuse scenario testing — including Thai-language input scenarios
- Data exposure and sensitive information review
- Tool, permission, and action boundary review
- Knowledge source and retrieval risk observations
- Unsafe output and handoff review
- Prioritised findings and remediation notes
- Written review summary
What is not included
- Full penetration test of unrelated systems
- Formal compliance certification or audit
- Legal advice
- Model training, fine-tuning, or model replacement
- 24/7 monitoring or managed detection
- Production fixes unless separately scoped
- Payment or checkout before written scope is confirmed
Delivery process
1
Scope and architecture intake
Review the AI workflow, user exposure, data context, tools, knowledge sources, intended actions, and API or channel connections — including LINE OA webhook configuration and connected backend systems.
2
AI risk testing
Test agreed scenarios covering prompt injection in Thai-language inputs, data exposure, unsafe output, permission boundaries, and handoff behavior.
3
Findings and prioritisation
Document risk observations, severity, business impact, and practical remediation notes.
4
Review and next steps
Walk through findings, limitations, remediation priorities, and any recommended retest scope.
Representative deliverable
Prompt Injection, Data Exposure and AI Permission Boundary Findings Report
All AI services are request-first. Scope is confirmed in writing before any payment, deposit, or implementation commitment.
Frequently asked questions
Related evidence
Relevant Engineering work
Selected public case records related directly to this service scope. Each record keeps its attribution and disclosure boundary visible.
Request a scope review
Tell us about your team, workflows, and data context. We will respond with a written scope and confirmed deliverables before any commitment.