Ai application security
Skill wfprieto/wcbs-build-kit/skills/ai-application-security
Use when auditing, building, testing, or releasing AI applications, LLM apps, RAG systems, vector databases, embedding pipelines, prompt templates, system prompts, tool-using agents, model routing, AI data leakage, prompt injection, jailbreak resistance, retrieval poisoning, system prompt leakage, or AI security guardrails.From its SKILL.md
npx -y skills add wfprieto/wcbs-build-kit --skill ai-application-securityAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
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SKILL.md
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AI Application Security
Use this skill for AI-specific security risks. Route through skills/cybersecurity-risk-routing/SKILL.md first when scope or authorization is uncertain.
Required Files
40_knowledge/SECURITY_FRAMEWORK_MAPPING.md60_templates/SECURITY_EVIDENCE_LEDGER_TEMPLATE.mdfor Standard and above60_templates/SECURITY_AUTHORIZATION_AND_SCOPE_TEMPLATE.mdwhen testing live endpoints or user/third-party data
APIVR Routing
- Phase 1 Audit: map model, prompts, tools, data sources, vector stores, tenants, retrieval filters, output channels, and secrets.
- Phase 2 Plan: define allowed tests, safe corpus, non-production targets, leak criteria, evidence, and rollback/containment.
- Phase 3 Implement: test or build controls with least privilege, safe fixtures, and redacted logs.
- Phase 4 Audit Implementation: check prompt/data boundaries, tool boundaries, tenant isolation, and logging.
- Phase 5 Verify Implementation: run safe regression probes or document
Not Run/Blocked. - Phase 6 Re-Audit: update residual risk, guardrail gaps, and release verdict.
AI Security Checklist
| Area | Check |
|---|---|
| Prompt boundary | No secrets, credentials, auth logic, or hidden business rules live only in prompts. |
| Prompt injection | Direct and indirect instruction override is tested or explicitly marked Not Run. |
| RAG | Retrieved content is treated as untrusted input and separated from system/developer authority. |
| Vector stores | Tenant isolation, metadata filters, write paths, and retrieval poisoning risks are reviewed. |
| Tools | Tool calls use allowlists, schemas, least privilege, and approval gates for high-impact actions. |
| Data leakage | Logs, traces, model outputs, and retrieved chunks avoid secrets and private data exposure. |
| Evaluation | Regression probes exist for material prompt, retrieval, or tool-routing changes. |
Decision Flow
flowchart TD
A["AI application security work"] --> B{"Uses tools or MCP?"}
B -- "Yes" --> C["Load mcp-tool-governance"]
B -- "No" --> D{"Uses RAG/vector store?"}
C --> D
D -- "Yes" --> E["Check retrieval injection, poisoning, tenancy, embeddings"]
D -- "No" --> F{"Prompt or model output security?"}
F -- "Yes" --> G["Check leakage, jailbreak, prompt boundaries"]
F -- "No" --> H["Use general security audit"]
E --> I["Security evidence ledger"]
G --> I
Good / Bad
<Bad> Put API keys, admin routing rules, or customer permissions in the system prompt and rely on the model not revealing them. </Bad> <Good> Keep secrets and authorization server-side, treat prompts as steerable text, test leakage attempts, and enforce tool permissions outside the model. </Good>Worked Example
Scenario: Internal document chatbot uses RAG over HR and finance docs.
- Select Comprehensive because private employee and finance data are in scope.
- Verify corpus ingestion paths and tenant/role filters.
- Test with safe fixture documents containing benign injected instructions.
- Confirm retrieved instructions cannot override system authority or call privileged tools.
- Record retrieved chunks, responses, filters, and mitigation evidence.
- Final verdict is blocked if cross-role retrieval is
UnknownorVerifiedfailing.
Final Output
Report system boundary, AI-specific risks, tests run, evidence state, failed or blocked checks, mitigations, release-gate status, and APIVR verdict.