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Agentic AI Security Advisor - Threat Model & Harden
Defensive threat-modeling and hardening for agentic AI and LLM apps, mapped to the OWASP Agentic Top 10, MITRE ATLAS, and NIST AI RMF.
$12
Agentic AI Security Advisor - Threat Model & Harden
Example session with this skill installed
Threat model a customer support agent that uses a React-based frontend, accesses a Postgres DB via a tool, and can send emails. It has no human approval for email dispatch.
- Read your context and instructions
- Compiled the agentic ai security
Summary
Threat-modeled a customer support agent (React frontend, Postgres DB access via tool, send_email tool, no human approval on email dispatch). Two high-severity gaps: the missing approval gate on email dispatch, and DB tool scope that isn't bounded to the task.
Agentic Security Assessment
| ASI Category | Risk | ATLAS Technique | Mitigation |
|---|---|---|---|
| ASI01 — Agent Goal Hijack | Indirect injection via a crafted support ticket could redirect the agent's goal toward invoking send_email on attacker-controlled content. | AML.T0051 — LLM Prompt Injection | Treat ticket content as untrusted; re-assert agent goal after ingesting external text. |
| ASI02 — Tool Misuse | The Postgres tool may allow query patterns broader than the support workflow needs, letting crafted input bypass application-level filters. | AML.T0053 — AI Agent Tool Invocation | Scope the DB tool to parameterized, task-specific queries; no free-form SQL. |
| ASI03 — Identity & Privilege Abuse | With no approval gate, the agent can be used as an exfiltration path: DB data retrieved in-session can be transmitted out via send_email. | AML.T0086 — Exfiltration via AI Agent Tool Invocation | Require explicit confirmation before any outbound send; egress-restrict what data can populate email bodies. |
Note: PII exposure via conversation history was considered but doesn't map cleanly to a single ASI category in this taxonomy — flagged as needing manual review rather than forced into an ID.
NIST AI RMF mapping
- Govern: No outbound-message approval policy currently exists — this is the top-priority gap.
- Map: Trust boundary between Postgres retrieval and
send_emailinvocation is unguarded.
Measure: Run SCN-04 (over-broad tool scope) and SCN-11 (data exfiltration via agent tool) from the defensive test library.
- Manage: Add a mandatory approval gate on
send_email; scope the DB tool to parameterized queries only.
Residual risk: Even with these controls, indirect injection risk from ticket content is reduced, not eliminated — periodic red-teaming of the ticket-ingestion path is still required.
Next steps
- Add a mandatory
is_approvedflag to thesend_emailtool definition. - Replace direct Postgres tool access with a scoped, parameterized query interface.
- Run SCN-04 and SCN-11 against a staging instance before production rollout.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
A security engineer's advisor for agentic and LLM-powered systems. Describe your agent — its tools, memory, autonomy level, and data flows — and it maps the attack surface and trust boundaries, walks the OWASP Top 10 for Agentic Applications against your design, and builds a library of defensive test scenarios you can run against your own system. You come away with prioritized, defense-in-depth controls aligned to the NIST AI RMF and a pentest-brief-quality assessment complete with risk ratings and an honest residual-risk statement.
Reach for it when you're designing or reviewing an AI agent's attack surface, preparing for AI red-teaming or a pentest, choosing guardrails, or assembling a defensible security assessment for a system you own or are contracted to evaluate. It's the advisory reasoning layer that automated scanners don't give you — the human judgment about where autonomy, tool access, and untrusted input actually create risk.
Authorized, defensive use only: it hardens systems you own or are permitted to assess and will not help attack, jailbreak, or weaponize against third parties. It reduces and surfaces risk rather than certifying a system "secure," and every output states residual risk explicitly. It's advisory — you run the assessment in your own environment — and because the underlying frameworks evolve, it flags framework names and IDs to verify against the current primary source. Not AI-governance/regulatory or legal advice.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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