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AI Model Security Risk Calculator

Score AI system risk against the OWASP Top 10 for LLM Applications: data exposure, tool access, output handling and guardrails.

About AI Model Security Risk Calculator

The two factors that dominate real AI incidents are what the model can do and how its output is consumed — OWASP's excessive agency and improper output handling. A chatbot that returns escaped text with no tools is a content problem; the same model wired to write actions and a shell is a remote-code-execution surface reachable by anyone who can phrase a sentence. Guardrails reduce likelihood but never to zero, because natural language has no parser to sanitise. This is a prioritisation estimate, not assurance. Every AI security control eventually reduces to the same question — if the model is fully manipulated, what can it actually reach — and that is a design decision, not a filter setting.

Formula

AI Model Security Risk

likelihood = mean(exposure, guardrail weakness, provenance risk, monitoring gap); impact = mean(data sensitivity, tool access, output-handling risk); risk = likelihood × impact ÷ 10, reduced 15% where a human approves consequential actions.

exposure
Who can send prompts to the system
toolAccess
What the model can do rather than say
outputHandling
Whether output is escaped text or executed code
provenance
Trustworthiness of the weights and training data

Worked example

With the default values below, AI Model Security Risk Calculator returns AI Risk Band of Low. Change any input above and every figure here is recalculated in your browser.

Inputs used

Sensitivity of data in context or retrievalone of Public content only — 2, Internal documents — 5, Regulated personal data — 8, Credentials, keys or health records — 10
Regulated personal data — 8
Who can reach the modelone of Internal team behind SSO — 3, All employees — 5, Authenticated customers — 7, Anonymous internet — 10
Authenticated customers — 7
Tools and actions the model can invokeone of None — text output only — 1, Read-only retrieval — 4, Write actions with approval — 7, Autonomous write and payment actions — 10
Write actions with approval — 7
How output is consumed downstreamone of Displayed as escaped text — 2, Rendered as markdown or HTML — 6, Parsed into code, SQL or shell — 9, Executed automatically — 10
Rendered as markdown or HTML — 6
Input and output guardrailsone of Layered filters plus a policy model, red-teamed — 2, Prompt-level instructions and basic filters — 5, Vendor defaults only — 7, None — 9
Prompt-level instructions and basic filters — 5
Model and data provenanceone of First-party model, curated data — 2, Commercial API with contractual assurances — 4, Open weights from a public hub — 6, Unverified fine-tune or community checkpoint — 9
Commercial API with contractual assurances — 4
Logging of prompts, outputs and tool callsone of Full logging with abuse detection — 1, Logged and sampled — 4, Logged, never reviewed — 6, Not logged — 9
Logged and sampled — 4
Human in the loop for consequential actions
Yes

Results

AI Risk Band
Low
AI Security Risk Score
2.98 / 10
Likelihood
5.00 / 10
Impact
7.00 / 10
Excessive Agency Risk
3.90 / 10
Highest-Value Control
Proportionate — keep continuous red-teaming as capabilities expand

Step by step

  1. 1Values used
  2. 2AI Model Security Risk
  3. 3AI Risk Band= Low
  4. 4AI Security Risk Score= 2.98 / 10
  5. 5Likelihood= 5.00 / 10
  6. 6Impact= 7.00 / 10
  7. 7Excessive Agency Risk= 3.90 / 10
  8. 8Highest-Value Control= Proportionate — keep continuous red-teaming as capabilities expand

Assumptions and limits

  • The result is derived only from the 8 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

How is AI Model Security Risk calculated?

likelihood = mean(exposure, guardrail weakness, provenance risk, monitoring gap); impact = mean(data sensitivity, tool access, output-handling risk); risk = likelihood × impact ÷ 10, reduced 15% where a human approves consequential actions. The two factors that dominate real AI incidents are what the model can do and how its output is consumed — OWASP's excessive agency and improper output handling. A chatbot that returns escaped text with no tools is a content problem; the same model wired to write actions and a shell is a remote-code-execution surface reachable by anyone who can phrase a sentence. Guardrails reduce likelihood but never to zero, because natural language has no parser to sanitise. This is a prioritisation estimate, not assurance.

Why does AI Model Security Risk matter?

Every AI security control eventually reduces to the same question — if the model is fully manipulated, what can it actually reach — and that is a design decision, not a filter setting.

What values do I need to enter?

This calculator takes 8 inputs: Sensitivity of data in context or retrieval, Who can reach the model, Tools and actions the model can invoke, How output is consumed downstream, Input and output guardrails, Model and data provenance, Logging of prompts, outputs and tool calls, Human in the loop for consequential actions. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.

Why do guardrails not reduce risk further?

Because prompt filtering is heuristic. There is no formal grammar separating instruction from data in natural language, so guardrails raise the cost of an attack rather than eliminating the class. Design assuming they will eventually be bypassed.

Does a human in the loop solve excessive agency?

It helps materially, which is why the model reduces the score — but approval fatigue is real. A human clicking approve on the two-hundredth request of the day is not a control, so keep the volume of consequential actions low enough that review stays meaningful.

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