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 NT$252,900~US$7,930Reference date: Oct 9, 2026 · Local price is authoritative; this is not a payment or settlement rate.
Buyer outcome
A structured security review of an existing AI deployment or model-connected workflow — covering prompt injection risk, retrieval exposure, tool access boundaries, permission gap documentation, and written findings with prioritised remediation recommendations. The recommended first AI engagement for Taiwan engineering teams that have already deployed or are actively building model-connected systems. Written scope confirmed before any proposal, deposit, or payment link.
The recommended first AI engagement for Taiwan engineering teams
Taiwan engineering and product teams building LLM-based features — RAG systems, AI coding assistants, model-connected APIs, or customer-facing AI agents — typically move fast to ship capability. Permission model design, prompt boundary hardening, and retrieval scope documentation are often deferred. A security review creates a written baseline before access is widened, a client security questionnaire arrives, or a significant data event occurs.
This review focuses on prompt injection via user-supplied inputs reaching connected tools or systems, retrieval exposure where RAG systems return documents outside the intended knowledge boundary, tool access permission gaps where the model has write or action capability beyond what the use case requires, agent chain risk where multi-step workflows can be redirected or manipulated via injected instructions, trust boundary gaps between the AI layer and internal APIs or databases, and missing human approval nodes before consequential AI-driven actions.
BilgeQor's AI security reviews produce written findings, not generic checklists. Remediation is prioritised by severity and exploitability. Findings are documented before implementation, not after deployment has expanded.
Scope drivers
Number of AI deployments or model-connected workflows reviewed
Scope of tool access — APIs, databases, internal systems, external services
Retrieval-augmented generation components and knowledge source boundaries
Agent and automation chains with multi-step action capability
Integration depth — customer-facing vs internal-only
Required findings depth and remediation detail
Ideal for
- Taiwan engineering and product teams that have deployed AI agents, RAG systems, or model-connected workflows
- SaaS companies that have integrated LLM-based features into customer-facing or internal products
- Security and product leads who need written risk findings before a board review, procurement audit, or enterprise client security questionnaire
- Engineering teams that have moved fast and need to verify their permission model and prompt boundaries retrospectively
- Teams preparing for broader AI rollout who need a security baseline before expanding access
What is included
- Prompt injection risk assessment
- Retrieval exposure and knowledge boundary review
- Tool access and permission boundary gap analysis
- Agent action chain risk mapping
- Data flow and trust boundary documentation
- Human approval node gap identification
- Written findings with severity classification
- Prioritised remediation recommendations
What is not included
- Live penetration testing or red team exercise
- Automated scanning or SAST/DAST tooling
- Legal advice or 個資法 compliance certification
- Implementation of remediation recommendations
- Formal SOC or ISO certification
- Unlimited workflow or system coverage
- Payment before written scope is confirmed
Delivery process
1
Scope confirmation
Confirm which AI deployments, systems, workflows, and integration points are in scope in writing before any review work begins.
2
Deployment and architecture review
Review prompt design, retrieval configuration, tool access grants, agent action chains, and data flows against documented or observable permission boundaries.
3
Risk identification and classification
Identify prompt injection vectors, retrieval exposure gaps, permission boundary weaknesses, missing approval nodes, and data flow trust boundary issues. Classify by severity and exploitability.
4
Findings and recommendations walkthrough
Deliver written findings with severity classification and prioritised remediation recommendations. Review call to walk through critical issues and next steps.
Representative deliverable
AI Security Review Report — Prompt Injection, Retrieval Exposure, Tool Access, and Permission Gap Findings
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.