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Free AI Agent Risk Scorer Online
Run a free AI agent risk assessment before you ship. Answer 10 weighted questions across autonomy, data access, blast radius, exposure, and safeguards to get a 0 to 100 risk score with the exact controls to require. This tool is built and maintained by TestMu AI (formerly LambdaTest).
Categories
How much can the agent do without a human in the loop?
What happens when the agent is unsure or fails?
This agent can cause real but recoverable damage. Gate its external actions behind approval, watch its inputs, and make sure someone owns the logs before launch.
- Require human approval for any external action
- Force citations or sources in agent outputs
- Redact PII before it reaches the model
- Log every action and alert on anomalies
- Rate limit actions and keep a kill switch
- Add an approval gate before the agent executes actions on its own.
- Route low-confidence or failed runs to a human queue instead of silent retries.
What is an AI Agent Risk Scorer?
An AI agent risk scorer is an assessment tool that measures how much damage an autonomous AI agent could cause in production. It converts 10 questions about autonomy, data access, action scope, exposure, and safeguards into a weighted 0 to 100 score with a Low, Medium, High, or Critical risk tier and a matching control checklist.
The risk categories in this scorer track the failure modes cataloged in the OWASP Top 10 for Agentic Applications, a peer-reviewed framework published by the OWASP Gen AI Security Project on December 9, 2025. If you are still designing the agent itself, map its steps first in the AI Agent Workflow Builder and then score the finished design here.
How does the AI Agent Risk Scorer work?
The scorer turns your answers into a weighted composite score. All processing happens in your browser, and no data is uploaded. Here is what happens under the hood:
- Weighted answers: every answer maps to a value from 0 (lowest risk) to 4 (highest risk), multiplied by a question weight between 0.8 and 1.3.
- Heaviest factors: data sensitivity and action privilege weigh 1.3 because they set the ceiling on how much damage a mistake can cause.
- Four risk tiers: the weighted sum is normalized to 0 to 100 and mapped to Low, Medium, High, or Critical.
- Lethal trifecta floor: described by security researcher Simon Willison in June 2025, the combination of private data, untrusted content, and external communication floors the score at 50.
- Framework alignment: the approach follows the NIST AI Risk Management Framework, released January 26, 2023, which organizes AI risk work into Govern, Map, Measure, and Manage functions.
How to use the AI Agent Risk Scorer?
Scoring an agent takes about 60 seconds and requires no signup. Follow these steps:
- Answer the 10 questions: Pick one option per question from its dropdown. Questions appear two at a time, one slide per risk dimension, and answering the second question advances the carousel automatically.
- Watch the live score: The result panel below the questions updates with every answer, showing your 0 to 100 score, risk tier, and a progress bar.
- Read the dimension bars: Five sub-scores show where your risk concentrates, and the highest-risk dimension gets two targeted fixes below the checklist.
- Review the required controls: The checklist scales with your tier, from 3 controls at Low risk to 8 at Critical.
- Copy or download the report: Use the copy and download icons beside the result panel to export a Markdown summary of your answers, sub-scores, and controls.
What are the key features of the AI Agent Risk Scorer?
The AI Agent Risk Scorer gives you a full risk breakdown, a control checklist, and an exportable report in one page. Here are its key features:
- Guided carousel: 10 questions appear two at a time by dimension, with a running counter and automatic advance.
- Live weighted scoring: the 0 to 100 score and risk tier recompute instantly on every answer, with no submit step.
- Dimension breakdown: five sub-score bars split your risk across Autonomy & Oversight, Data Access & Memory, Action Scope & Blast Radius, Exposure Surface, and Reversibility & Safeguards.
- Lethal trifecta detection: the scorer automatically flags the private data, untrusted content, and external communication combination.
- Tier-scaled control checklists: each risk tier lists 3 to 8 concrete controls to require before the agent launches.
- Targeted fixes: the highest-risk dimension gets two specific mitigation actions, not generic advice.
- Markdown report export: copy or download a report of answers, sub-scores, and controls for design reviews and launch tickets.
What is the difference between Low, Medium, High, and Critical AI agent risk?
The scorer maps every result to one of four tiers. Each tier carries its own interpretation and its own minimum set of launch controls:
| Tier | Score range | What it means | Controls required |
|---|---|---|---|
| Low | 0 to 24 | Small blast radius, mistakes stay internal. | 3 baseline controls |
| Medium | 25 to 49 | Real but recoverable damage is possible. | 5 controls, approval on external actions |
| High | 50 to 74 | Hard-to-undo damage, do not ship fully autonomous. | 7 controls, two-person sign-off |
| Critical | 75 to 100 | High autonomy plus sensitive access plus irreversible actions. | 8 controls, staged rollout gates |
What are the main use cases of the AI Agent Risk Scorer?
Use the scorer any time an AI agent is about to gain new access or reach. Common situations include:
- Pre-launch review: score an agent before it gets production credentials, then attach the downloaded report to the launch ticket in Test Manager as evidence. Pair it with the AI Agent Readiness Quiz to confirm your team and process are ready.
- QA and test automation: teams adopting AI-native testing agents such as KaneAI by TestMu AI can score the agent's planned autonomy and access before wiring it into CI pipelines and real test environments.
- Design comparison: score two candidate architectures and pick the one that delivers the same outcome at a lower tier, for example approval-gated instead of fully autonomous.
- Vendor evaluation: run the questions against a third-party agent you are buying, using the vendor's documentation as the source, then line up finalists side by side with the AI Agent Comparison tool.
- Quarterly rescoring: track whether risk went up or down after each release by comparing downloaded reports over time. New to the terminology? The AI Agent Glossary defines every term this scorer uses, and the AI Agent Prompt Generator helps you write safer system prompts for the agent you just scored.
Frequently Asked Questions (FAQs)
What is AI agent risk assessment?
AI agent risk assessment is the practice of measuring how much damage an autonomous AI agent could cause before you deploy it. It examines autonomy, data access, action scope, exposure to untrusted content, and safeguards, then assigns a score or tier that guides which controls the agent needs.
What is the lethal trifecta for AI agents?
The lethal trifecta, a term coined by security researcher Simon Willison in June 2025, is the combination of access to private data, exposure to untrusted content, and the ability to communicate externally. An agent with all three can be tricked into leaking data, so this scorer never rates that combination below High.
What are the biggest risks of deploying an AI agent?
The biggest AI agent risks are prompt injection from untrusted input, excessive agency from over-permissioning, sensitive data exposure, and unsafe actions like refunds or database writes. The lethal trifecta, which combines untrusted input, private data, and external communication, is the most dangerous pattern. This scorer flags all of these across its five risk dimensions.
What risk score is safe for shipping an AI agent?
No score guarantees safety, but agents below 25 typically ship with baseline logging and a kill switch, while 25 to 49 calls for approval gates on external actions. Scores of 50 or more mean the agent can cause hard-to-undo damage and needs every listed control before launch.
How can you reduce AI agent risk?
Reduce AI agent risk with least-privilege permissions, a kill switch, human approval for high-risk actions, real-time monitoring, and audit trails that prove what the agent did. Keeping untrusted input away from sensitive data and external actions breaks the lethal trifecta. Rescore the agent after each change so new risks surface early.
When should an AI agent require human approval before acting?
An AI agent should require human approval whenever an action crosses a risk threshold: irreversible or high-cost operations, first-time writes to production, spending above a set limit, or anything touching sensitive data. Low-risk, read-only actions can run autonomously. Gating high-stakes steps keeps the agent's authority bounded, observable, and reversible before launch.
Who is responsible for AI agent security?
AI agent security is a shared responsibility, but accountability sits with the organization deploying the agent, not the model vendor. Engineering owns guardrails and least-privilege access, security teams own monitoring and audit trails, and leadership owns governance. When an agent acts autonomously and causes harm, the deploying company remains answerable.
What regulations exist for AI agent security?
No single law governs AI agents yet, but several frameworks apply. The EU AI Act regulates high-risk AI systems, the NIST AI Risk Management Framework guides governance, and the OWASP Top 10 for Agentic Applications catalogs agent-specific threats. This scorer aligns its five risk dimensions to the NIST and OWASP guidance.
What is agentic AI governance?
Agentic AI governance is the set of policies, controls, and oversight that keeps autonomous agents accountable and safe. It defines who can deploy agents, what actions they may take, how decisions are logged, and which frameworks apply, such as the NIST AI Risk Management Framework. Governance turns one-time risk scoring into ongoing oversight.
How often should I rescore an AI agent?
Rescore an AI agent whenever it gains a new tool, data source, or integration, and at least once per quarter. Agent permissions drift over time, which is why enterprise platforms recalculate agent risk continuously. Download the report after each run so you can compare scores between releases.
Is my assessment data uploaded to a server?
No. All processing happens in your browser. Your answers, the computed score, and any report you copy or download never leave your device, and nothing is stored between visits. You can safely assess internal or regulated agent workflows without exposing details to TestMu AI or any third party.
Is the AI Agent Risk Scorer free to use?
Yes. The AI Agent Risk Scorer is completely free, with no signup, no email gate, and no usage limits. Every feature, including the dimension breakdown, control checklists, and the Markdown report export, is available to everyone. It is maintained by TestMu AI as part of its free online tools collection.
Can I score a third-party or vendor AI agent?
Yes. Answer the 10 questions using the vendor's documentation, security whitepaper, or sales engineering answers as your source. The resulting tier and control checklist give you a structured way to compare vendors and decide which contractual safeguards, such as audit logs or kill switches, to require.
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