AI Model Security Checklist
26 security checks for AI models covering model poisoning, adversarial attacks, model extraction, inference attacks, supply chain integrity, and deployment hardening.
🚨 AI Model Threat Landscape
AI models are high-value targets — a single compromised model can affect every downstream application:
• 62% of ML models use at least one dependency with known vulnerabilities (Protect AI)
• Pickle deserialization enables arbitrary code execution in 34% of model files on Hugging Face
• $250K+ average cost to retrain a poisoned production model (MITRE ATLAS)
• Model extraction achieves 95%+ fidelity with <1% of training budget (Google DeepMind)
• Backdoor attacks survive fine-tuning in 89% of cases (MIT Lincoln Lab)
• 42% of organizations have no model security testing in their ML pipeline (Gartner)
CATEGORIES
Training Data Integrity Verification
CriticalTraining datasets validated for integrity — no injected, modified, or corrupted samples
Backdoor Detection
CriticalModel tested for hidden backdoor triggers that cause targeted misclassification
Fine-Tuning Poisoning Defense
CriticalFine-tuning datasets audited for adversarial examples that could compromise the base model
Label Poisoning Detection
HighLabels in supervised learning datasets verified against ground truth — no systematic mislabeling
Adversarial Input Robustness
CriticalModel tested against adversarial perturbations — small input changes don't cause misclassification
Evasion Attack Resistance
HighSecurity-critical models (fraud detection, malware classification) resist input manipulation to bypass detection
Input Validation & Preprocessing
HighModel inputs validated for anomalous patterns, out-of-distribution data, and adversarial signatures
Transferability Defense
MediumAdversarial examples crafted against surrogate models don't transfer to the production model
Query-Based Extraction Defense
CriticalAPI rate limiting and output perturbation prevent model cloning through repeated queries
Confidence Score Protection
HighFull probability distributions not exposed — only top-k predictions or class labels returned
Model Watermarking
MediumModels contain verifiable watermarks that survive extraction and prove ownership
Membership Inference Protection
HighAttackers cannot determine whether specific data points were in the training set
Attribute Inference Defense
HighModel outputs don't reveal sensitive attributes about individuals in the training data
Model Inversion Resistance
HighModel cannot be reverse-engineered to reconstruct training data (faces, text, records)
Pre-trained Model Verification
CriticalPre-trained models verified for integrity — checksums match, no tampered weights
Serialization Format Security
CriticalModel files use safe serialization formats — no pickle, no arbitrary code execution on load
Dependency Vulnerability Scanning
HighML framework dependencies (PyTorch, TensorFlow, transformers) scanned for known CVEs
Model Registry Access Control
HighInternal model registry enforces access control — only authorized users can publish or modify models
Model Serving Isolation
HighModel inference runs in isolated containers with restricted system access
Weight Encryption at Rest
HighModel weights encrypted when stored — decryption only at inference time in secure enclaves
Inference API Authentication
HighModel endpoints require authentication — no unauthenticated access to model predictions
Model Version Rollback Protection
MediumDeployment pipeline prevents unauthorized model downgrades that could reintroduce vulnerabilities
Data Provenance Tracking
HighFull lineage tracking for all training data — source, transformations, and quality scores recorded
Training Environment Isolation
HighTraining infrastructure isolated from production — no shared credentials, networks, or storage
Model Risk Assessment
HighFormal risk assessment completed before deployment — threat model, impact analysis, and mitigations documented
Model Behavior Monitoring
HighProduction model performance continuously monitored for drift, degradation, and anomalous predictions
Need an AI Model Security Audit?
Zealynx audits AI models against real-world attack patterns — poisoning, adversarial attacks, extraction, and supply chain risks. We test what matters before attackers do.
