Secure Model Training & Validation

Threats mitigated

• T1 Memory Poisoning

  Attackers inject malicious data into the agent's memory to manipulate future decisions, affecting any memory type from in-agent session to cross-agent cross-user memory.

• T2 Tool Misuse

  Manipulation of tools, APIs, or environment access to perform unintended actions or access unauthorized resources, including exploitation of access to external systems.

• T5 Cascading Hallucination Attacks

  Foundation models generate incorrect information that propagates through the system, affecting reasoning quality and being stored in memory across sessions or agents.

• T7 Misaligned & Deceptive Behaviors

  Model alignment issues leading to unintended behaviors that impact users, organizations, or broader populations through subtle reasoning or tool usage misalignments.

• T13 Rogue Agents in Multi-Agent Systems

  Compromised AI agent activity outside monitoring limits or orchestration in multi-agent systems.

OWASP AISVS coverage

The AISVS 1.01 chapters and verification requirements this control helps satisfy.

C1 Training Data Integrity & Traceability

• C1.3.1 Level 2

  Verify that training and fine-tuning pipelines implement poisoning detection techniques to identify potential data poisoning or unintentional corruption in training data.

• C1.3.5 Level 3

  Verify that defenses against clean-label poisoning attacks are implemented.

C3 Model Lifecycle Management & Change Control

• C3.2.1 Level 1

  Verify that models undergo automated input validation testing, safety evaluation testing, and output sanitization testing before deployment.

• C3.2.2 Level 2

  Verify that models subjected to post-training quantization are re-evaluated against the same safety and alignment test suite on the compressed artifact before deployment.

• C3.5.1 Level 2

  Verify that models used in RLHF fine-tuning are versioned and integrity-verified before use in a training run.

• C3.5.2 Level 3

  Verify that RLHF training stages include automated detection of reward hacking or reward model over-optimization.

C11 Adversarial Robustness

• C11.1.1 Level 1

  Verify that the model has undergone alignment and safety training or fine-tuning to prevent the model from generating disallowed content categories.

• C11.1.2 Level 1

  Verify that a version-controlled alignment test suite is run on every model update or release.

• C11.1.3 Level 1

  Verify that models are evaluated against known adversarial attack techniques relevant to their modality.

• C11.2.4 Level 2

  Verify that training on sensitive datasets employs differentially-private optimization.

Design phase

Architecture, threat modelling and policy decisions made before code is written. 3 tasks.

Secure Training Pipeline Design

• Design secure training pipelines with isolated environments, data integrity checksums, and reproducible training configurations
• Define model validation criteria including adversarial robustness benchmarks and bias detection thresholds
• Establish secure model registries with versioning and provenance tracking

Build phase

Implementation, testing and hardening while the agent is developed. 3 tasks.

Training Environment Hardening and Validation

• Implement training environment hardening with container isolation, restricted network access, and secure GPU allocation
• Build automated validation suites that test model outputs against adversarial examples, distribution shifts, and boundary conditions
• Integrate integrity verification at each pipeline stage

Operations phase

Monitoring, response and continuous improvement once the agent is live. 3 tasks.

Training Monitoring and Pre-Deployment Scanning

• Monitor training jobs for anomalous resource usage or unexpected convergence patterns
• Maintain immutable audit logs of all training runs including hyperparameters, data snapshots, and resulting metrics
• Implement automated model scanning for backdoors and trojans before deployment promotion

Tools and frameworks

Common Tooling

• MLflow
• Kubeflow Pipelines
• DVC (Data Version Control)
• Weights & Biases
• TensorFlow Model Analysis
• ART (Adversarial Robustness Toolbox)
• ModelScan
• NVIDIA NeMo Guardrails

Related controls

• M13 Secure Development Lifecycle Integration shares T1, T2, T5, T7, T13
• M4 Prompt Hardening & Jailbreak Prevention shares T5, T7, T1
• M5 Multi-Stage Reasoning Validation shares T5, T7, T1
• M11 Content Security & Output Filtering shares T5, T7, T1

References

• OWASP ML Security Top 10
• MITRE ATLAS AML.T0020: Training Data Poisoning