STRIDE

Spoofing violates the security property of authenticity. An attacker pretends to be a different person, system, or entity to gain unauthorized access.

• Examples: Using stolen credentials, forging an IP address, or presenting a fake biometric sample
• In ML systems: An adversary impersonates a legitimate federated learning client to inject poisoned model updates
• Mitigation: Strong authentication protocols, multi-factor authentication, and cryptographic signatures

Tampering violates the security property of integrity. An attacker maliciously modifies data, code, or model parameters in transit or at rest.

• Examples: Modifying training data labels, altering model weights during deployment, or injecting backdoor triggers
• In ML pipelines: An adversary intercepts and alters a serialized model file before it reaches the inference server
• Mitigation: Artifact signing, checksums, and tamper-evident logging

Repudiation violates the security property of non-repudiability. A malicious actor performs an action but denies having done so, and the system lacks sufficient proof to attribute the action.

• Examples: Deleting audit logs, submitting poisoned data without traceability, or denying a model inference request was made
• In ML governance: A data engineer alters a training dataset and claims the original version was used, with no data provenance records to disprove the claim
• Mitigation: Immutable audit trails, digital signatures on all artifacts, and secure logging

Information Disclosure violates the security property of confidentiality. An attacker gains unauthorized access to sensitive data, model parameters, or intellectual property.

• Examples: Extracting training data via model inversion attacks, inferring membership through membership inference probes, or stealing proprietary model weights
• In privacy-preserving ML: An adversary queries a model API and reconstructs faces from a facial recognition training set
• Mitigation: Differential privacy, homomorphic encryption, and output perturbation

Denial of Service violates the security property of availability. An attacker overwhelms system resources to prevent legitimate users from accessing the service.

• Examples: Flooding a model inference API with requests, triggering computationally expensive adversarial inputs, or exploiting memory leaks
• In ML operations: An adversary submits sponge examples—inputs specifically designed to maximize energy consumption and latency during inference
• Mitigation: Rate limiting, input validation, resource quotas, and auto-scaling infrastructure

Elevation of Privilege violates the security property of authorization. An attacker gains permissions beyond what they are legitimately entitled to, enabling broader system compromise.

• Examples: Exploiting a vulnerability to escalate from a read-only data scientist role to a model deployment admin, or bypassing access controls on a training pipeline
• In ML platforms: An attacker compromises a Jupyter notebook with limited permissions and exploits a container escape to access the underlying Trusted Execution Environment
• Mitigation: Principle of least privilege, role-based access control, and regular privilege audits

Protections against Information Disclosure attacks that reconstruct private training data from model parameters. These techniques prevent adversaries from extracting sensitive information through repeated queries.

• Differential Privacy SGD (DP-SGD): Adds calibrated noise to gradients
• Membership Inference Protections: Prevents determining if a record was in training
• Gradient Clipping: Caps per-example influence to bound information leakage

A defensive technique against Spoofing and Tampering threats that augments training data with adversarial examples. This forces the model to learn robust decision boundaries that resist manipulation.

• Evasion Attack Defense: Hardens models against input perturbations
• Certified Robustness: Provides mathematical guarantees via Randomized Smoothing
• Clean-Label Poisoning Resistance: Mitigates attacks using correctly labeled but perturbed samples

Controls against Tampering and Elevation of Privilege threats throughout the ML lifecycle. Ensures the integrity and authenticity of all components from pre-trained weights to datasets.

• Artifact Signing: Cryptographically signs model weights and data
• Data Provenance: Documents lineage and chain of custody
• MITRE ATLAS: Knowledge base of adversary tactics for AI systems

Techniques to identify and neutralize hidden triggers injected during training—a classic Tampering threat. These methods reverse-engineer or prune the malicious pathways attackers embed in models.

• Neural Cleanse: Reverse-engineers potential triggers via optimization
• Fine-Pruning: Removes dormant neurons activated only by triggers
• Activation Clustering: Separates clean and poisoned data by analyzing hidden layer activations

Structured adversarial assessment that simulates real-world attacks across all STRIDE categories. Security teams proactively probe for vulnerabilities before malicious actors can exploit them.

• Trigger Reconstruction: Recovers embedded backdoor patterns
• Model Inspection: Analyzes weights and decision boundaries for anomalies
• Distributional Shift Detection: Flags statistical deviations indicating potential poisoning