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    AI Agent Threat Model — Prompt Injection & Tool Misuse

    by monna

    1

    Map trust boundaries, trace injection-to-tool attack paths, and prioritize concrete controls before an AI agent ships.

    $19

    30-day refund guarantee

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    See it in action

    You say

    Threat-model our support agent. It reads customer emails and RAG results, can search the knowledge base, send emails, and issue refunds up to $500 without human approval. We have not documented vector-store write validation or customer-level retrieval scoping.

    Your agent does

    VERDICT: NOT-DEPLOYABLE

    CRITICAL — Combined tampering + elevation path Untrusted email content reaches the model context while the refund tool can act without human review.

    Evidence: Stated Confidence: 88% — based mainly on stated autonomy and tool authority Priority control: require human confirmation for refunds, isolate untrusted content from instructions, and cap tool authority.

    What you get

    Audit RAG pipelines for indirect prompt injection and memory poisoning.Analyze agentic systems for unauthorized tool execution and privilege escalation.Map AI-specific vulnerabilities to concrete security controls and mitigations.Generate structured DFDs for AI applications to identify hidden trust boundaries.

    About this skill

    What it does

    An AI agent can turn untrusted text into a real action. This skill reconstructs the system as a data-flow diagram, applies STRIDE to every relevant element, and treats prompt injection plus high-impact tool access as one compound path instead of two disconnected checklist items.

    What you get

    • A deployment verdict and AI data-flow inventory.
    • Trust boundaries and a STRIDE-per-element matrix.
    • Evidence-tagged findings with confidence scores.
    • A mitigation roadmap ordered by consequence.
    • A deterministic coverage check for every applicable STRIDE category and the combined injection-to-tool path.

    Worked path included

    Send no architecture and the skill runs a labeled demonstration on its built-in customer-support agent. The package also includes a complete worked example and a universal prompt edition for capable chat models that do not load skills directly.

    Known limitations

    This is a design-stage threat model, not a penetration test or safety certification. It works from the architecture and permissions you describe, does not inspect a live system, and does not generate adversarial instruction payloads or working exploits. Classic web/API logic and cloud IAM require their own reviews.

    security
    ai-agents
    threat-modeling
    llm-ops
    cybersecurity

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Recently published to Agensi

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    Creator

    monna

    monna

    11 skills on Agensi

    I design high-value AI prompts, reasoning systems, and practical workflows for people who need more than generic outputs. My work focuses on turning complex tasks into structured AI systems for research, education, RAG, validation, decision-making, systems engineering, and business workflows. I build prompts that are designed to: • reduce ambiguity and weak reasoning • ground outputs in evidence and clear constraints • detect gaps, contradictions, and misconceptions • guide AI through complex multi-step decisions • produce structured, usable deliverables instead of generic answers I’m especially interested in RAG, ontology-driven reasoning, evidence validation, adaptive learning, and AI workflow design. If you’re looking for prompts that do real analytical work—not just better wording—you’re in the right place.

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