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
36%
Does it follow best practices?
Impact
98%
1.06xAverage score across 3 eval scenarios
Passed
No known issues
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npx tessl skill review --optimize ./.claude/skills/ai-security-verification/SKILL.mdConduct comprehensive security verification of AI-driven applications using the OWASP AI Security Verification Standard (AISVS) framework's 13-category structured checklist.
Training Data Governance & Bias Management — Assess data quality, provenance, bias detection, and governance controls throughout the data lifecycle.
User Input Validation — Evaluate input sanitization, prompt injection defenses, adversarial input detection, and boundary validation mechanisms.
Model Lifecycle Management & Change Control — Review model versioning, deployment controls, rollback capabilities, and change management processes.
Infrastructure, Configuration & Deployment Security — Examine deployment security, container hardening, network controls, and infrastructure configuration.
Access Control & Identity — Verify authentication mechanisms, authorization controls, privilege management, and identity governance.
Supply Chain Security for Models, Frameworks & Data — Assess third-party model security, dependency management, and supply chain integrity.
Model Behavior, Output Control & Safety Assurance — Evaluate output validation, safety guardrails, behavior monitoring, and harmful content prevention.
Memory, Embeddings & Vector Database Security — Review vector database security, embedding protection, memory isolation, and context management.
Autonomous Orchestration & Agentic Action Security — Assess agent coordination security, tool access controls, and autonomous decision-making safeguards.
Adversarial Robustness & Attack Resistance — Test resilience against adversarial examples, evasion attacks, and model extraction attempts.
Privacy Protection & Personal Data Management — Verify privacy controls, data minimization, consent management, and regulatory compliance.
Monitoring, Logging & Anomaly Detection — Evaluate security monitoring, audit logging, anomaly detection, and incident response capabilities.
Human Oversight and Trust — Assess human-in-the-loop controls, explainability mechanisms, and trust calibration measures.
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