AI Security

LLM Security Risk Scoring Tool

Score your LLM deployment security against the OWASP Top 10 for Large Language Model Applications.

This tool evaluates your defenses across all ten OWASP LLM risk categories including prompt injection, data poisoning, model theft, and excessive agency. Each category is scored by maturity level to generate an overall LLM security posture rating. Designed for AI security engineers and CISOs evaluating enterprise LLM deployments.

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Enterprise LLM Security Risk Assessment
LLM Application Registration
LLM Security Questionnaire
Evidence Checklist

All values are calculated from entered answers. Unknown and missing evidence increase risk and reduce confidence.

LLM Security Risk Scorecard

Overall LLM Security Risk Score-

Complete the LLM assessment to calculate security risk, evidence confidence, and approval recommendation.

Overall LLM Security Risk Score

-/100

Not calculated

Risk Score by Domain Radar

Risk by Severity

Risk Heatmap

Risk Trend Over Time

Planning trend generated from this assessment, not live telemetry.

OWASP LLM Risk Coverage

Compliance Framework Coverage

Top LLM Security Risks

Top LLMs by Risk Score

Recent Critical Findings

Remediation Summary

Risk Distribution Pyramid

Evidence Completeness

Approval Workflow

Reports Section

Global Standards Alignment

AI-Generated Executive Summary

This summary is generated from the entered assessment data and should be reviewed by the responsible AI security owner before final decision.

Assessment Disclaimer

Based on OWASP Top 10 for LLM Applications and MITRE ATLAS concepts.

  • Complete LLM security assessment requires testing against real attack scenarios.
  • Use scores to prioritize remediation and guide architecture decisions.
  • Consult AI security specialists for production-grade deployments.

Search topics covered

  • LLM security risk scoring
  • OWASP top 10 LLM assessment
  • large language model security
  • prompt injection prevention
  • AI model theft protection
  • LLM vulnerability assessment tool
  • generative AI security posture
  • AI application security testing
  • LLM deployment risk calculator
  • OWASP LLM compliance checker
  • AI security maturity model
  • enterprise LLM risk management
  • insecure output handling LLM
  • training data poisoning risk
  • excessive AI agency controls

How to use this calculator

Understand OWASP Categories

Each of the 10 inputs corresponds to an OWASP LLM risk category. Review the descriptions to understand what each covers. Accurate self-assessment depends on understanding the difference between partial and managed maturity for each specific risk area.

Rate Your Maturity

For each category, select the maturity level that best describes your current state. None means no controls. Partial means some ad-hoc measures. Managed means formal documented controls. Optimized means automated, continuously monitored defenses.

Review Overall Score

The total score represents your aggregate LLM security posture. Scores below 50 indicate significant gaps. The maturity tier maps to industry benchmarks and helps communicate risk to leadership.

Identify Weak Areas

The breakdown table shows your score per category. Categories at 3 or below are flagged as at-risk. The Weakest Area KPI highlights the most urgent category needing remediation.

Plan Remediation

Use the guidance section to understand defensive measures for each category. Map low-scoring categories to projects like input validation, output filtering, or permission restriction. Create a roadmap with quarterly milestones.

Export And Track

Export to PDF or CSV and track scores over time. Quarterly reassessments create a trend showing whether your LLM security posture is improving. Share reports with your CISO and AI governance team.

Advantages of this calculator

OWASP Framework Alignment

Maps directly to OWASP Top 10 for LLM, ensuring your assessment uses the same taxonomy recognized by the global security community for audit conversations and vendor evaluations.

Comprehensive Coverage

Covers the full attack surface of LLM deployments from prompt injection through model theft, including categories like excessive agency that most ad-hoc assessments miss.

Priority Identification

Weakest-area highlighting and at-risk counter instantly show where to focus, preventing over-investment in familiar risks while ignoring equally dangerous but less obvious categories.

Executive Communication

Single score out of 100 and maturity tier translate complex AI security concepts into business language that board members and executives easily understand.

Trend Tracking

Regular reassessments create a measurable improvement trajectory. Security teams can demonstrate ROI on AI security investments by showing score improvements over time.

Vendor Evaluation

Use this framework to evaluate third-party AI vendors. Require vendors to self-assess against the same categories, creating comparable risk profiles for procurement decisions.

Q&A

What is the OWASP Top 10 for LLM?

A framework identifying ten critical risks in LLM applications, published by OWASP to guide developers and security teams in building safer AI systems.

What is prompt injection?

An attack embedding malicious instructions in inputs to manipulate LLM behavior, potentially causing data leakage or unauthorized actions.

What does excessive agency mean?

When an LLM has too many permissions or takes autonomous actions without human oversight, risking unintended consequences.

What score should we target?

Most organizations should aim for 70+. Critical infrastructure should target 85+ for production LLM deployments.

How often should this be repeated?

Quarterly for production deployments, or whenever significant changes occur like model upgrades or new integrations.

Does this guarantee compliance?

No. This is a self-assessment tool. Formal compliance requires third-party audits and comprehensive documentation.

What is model theft?

Unauthorized extraction of a proprietary AI model through repeated API queries or insider access.

What is insecure output handling?

When LLM output is used without validation, potentially enabling XSS, injection, or other attacks.