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ai-security-verification

Comprehensive AI security verification using OWASP AI Security Verification Standard (AISVS) framework. Provides structured checklist to verify security and ethical considerations across 13 categories of AI-driven applications, from training data governance to human oversight.

57

1.06x
Quality

36%

Does it follow best practices?

Impact

98%

1.06x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

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SKILL.md
Quality
Evals
Security

AI Security Verification Standard (AISVS)

Conduct comprehensive security verification of AI-driven applications using the OWASP AI Security Verification Standard (AISVS) framework's 13-category structured checklist.

Steps

  1. Training Data Governance & Bias Management — Assess data quality, provenance, bias detection, and governance controls throughout the data lifecycle.

  2. User Input Validation — Evaluate input sanitization, prompt injection defenses, adversarial input detection, and boundary validation mechanisms.

  3. Model Lifecycle Management & Change Control — Review model versioning, deployment controls, rollback capabilities, and change management processes.

  4. Infrastructure, Configuration & Deployment Security — Examine deployment security, container hardening, network controls, and infrastructure configuration.

  5. Access Control & Identity — Verify authentication mechanisms, authorization controls, privilege management, and identity governance.

  6. Supply Chain Security for Models, Frameworks & Data — Assess third-party model security, dependency management, and supply chain integrity.

  7. Model Behavior, Output Control & Safety Assurance — Evaluate output validation, safety guardrails, behavior monitoring, and harmful content prevention.

  8. Memory, Embeddings & Vector Database Security — Review vector database security, embedding protection, memory isolation, and context management.

  9. Autonomous Orchestration & Agentic Action Security — Assess agent coordination security, tool access controls, and autonomous decision-making safeguards.

  10. Adversarial Robustness & Attack Resistance — Test resilience against adversarial examples, evasion attacks, and model extraction attempts.

  11. Privacy Protection & Personal Data Management — Verify privacy controls, data minimization, consent management, and regulatory compliance.

  12. Monitoring, Logging & Anomaly Detection — Evaluate security monitoring, audit logging, anomaly detection, and incident response capabilities.

  13. Human Oversight and Trust — Assess human-in-the-loop controls, explainability mechanisms, and trust calibration measures.

Output

Use the finding format from templates/finding.md. Produce:

  • AISVS Compliance Assessment — Verification status across all 13 categories
  • Security Control Evaluation — Detailed analysis of implemented controls
  • Gap Analysis — Missing or inadequate security measures
  • Risk-Based Prioritization — Critical findings requiring immediate attention
  • Compliance Roadmap — Structured plan to achieve AISVS compliance
  • Verification Evidence — Documentation supporting compliance claims

OWASP References

  • OWASP AI Security Verification Standard (AISVS)
  • OWASP Top 10 for LLM Applications 2025
  • OWASP AI Security and Privacy Guide
  • OWASP Application Security Verification Standard (ASVS)
  • OWASP AI Testing Guide
Repository
OWASP/secure-agent-playbook
Last updated
Created

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