Procurement teams, third-party risk managers, and compliance leads often face questions about AI vendor contracts that don't address Adversarial Simulation. These questions are practical and urgent, yet existing policies rarely provide clear answers.
Understanding "Secure" Claims from Vendors
When a vendor claims their model is secure, demand specifics. A model might be secure against unauthorized access but still vulnerable to prompt injection. It might have strong API authentication but fail under adversarial inputs.
Focus your due diligence on three threat categories:
Input manipulation: Can attackers craft inputs that cause the model to leak data or bypass safety filters? This includes prompt injection and extraction attacks.
Model extraction: Can someone query the model enough to reverse-engineer its behavior? Rate limiting and query monitoring are crucial here.
Supply chain compromise: Did the vendor inherit vulnerabilities from their foundation model provider or data sources? This risk is often overlooked in contracts.
Your RFP should demand evidence of adversarial simulation (red teaming) results. Ask for details on the scope of their testing, the attack vectors covered, and how they addressed findings.
Adapting Penetration Testing for AI Models
Traditional penetration testing targets infrastructure vulnerabilities. AI models introduce a different attack surface: the decision logic itself.
Think of Adversarial Simulation as penetration testing for model behavior. You're not just checking access; you're checking if the model can be manipulated. Under SR 11-7, this is part of "ongoing monitoring" and "outcomes analysis".
Incorporate adversarial simulation into your model validation workflow. For outsourced models, require vendors to provide:
- Red teaming reports on safety, bias, and data leakage
- Documentation of model limitations based on adversarial findings
- Instructions for use that specify known failure modes
If your vendor can't provide these, you're accepting a model with unknown risks, a control gap under ISO/IEC 42001 Annex A controls.
Writing Adversarial Simulation Requirements into Contracts
You don't need an in-house red team to specify vendor requirements. Align them with your risk tier and regulatory obligations.
For high-risk AI systems under the EU AI Act or material models under SR 11-7, require:
Pre-deployment adversarial simulation: Vendors must conduct red teaming before release and provide a summary of attack vectors tested and mitigations implemented.
Ongoing adversarial monitoring: For adaptive models, require periodic re-testing. Quarterly for high-risk systems; annually for lower-risk.
Responsible disclosure process: Vendors should have a process for security researchers to report vulnerabilities. Lack of this is a red flag.
Incident response obligations: Define what constitutes a model security incident and require timely notification.
You can also require alignment with the NIST AI RMF "Manage" function.
Scanning AI Models for Vulnerabilities
You can't run traditional vulnerability scans on AI models, but you can apply similar concepts.
Treat model APIs like any other API: Use authentication, authorization, and rate limiting controls. Monitor for abnormal query patterns.
Integrate model behavior into your SIEM: Log predictions, confidence scores, and input/output pairs. Alert on anomalies.
Test for known attack patterns: Use libraries like IBM's Adversarial Robustness Toolbox to check robustness.
Map AI risks to your existing framework: Add AI-specific threats to your cybersecurity risk register.
Under ISO/IEC 42001, this is part of your AI Management System's risk treatment.
Evaluating Foundation Model Providers
Foundation model providers should demonstrate systematic Adversarial Simulation. Ask for:
Safety red teaming results: Evidence of testing for harmful content generation and bias.
Capability evaluations under adversarial conditions: How does the model perform under adversarial examples?
Data extraction testing: Have they tested for data memorization, crucial for GDPR compliance?
Third-party validation: Independent assessment is preferable. Ask if they've engaged external researchers.
If the provider claims their model has "systemic risk" under the EU AI Act, verify they have the necessary infrastructure.
Re-testing Models for Adversarial Robustness
The frequency of re-testing depends on change frequency and risk tier:
After any model update: Re-test after new versions or changes to safety filters.
When attack techniques evolve: Stay informed on new attack methods and re-test accordingly.
As part of ongoing monitoring: Align Adversarial Simulation with your performance monitoring schedule.
Before regulatory audits: Ensure your Adversarial Simulation documentation is current for audits.
Document your testing schedule in your AI system impact assessment or model validation report.
Addressing Vulnerabilities in Vendor Models
If Adversarial Simulation reveals a vulnerability, treat it like any security issue:
Assess impact: Determine if the vulnerability affects your specific use case.
Check contractual obligations: Ensure your vendor is required to remediate issues promptly.
Implement compensating controls: Add input validation or output filtering as needed.
Escalate per your risk framework: Suspend the model if the risk is unacceptable and the vendor can't fix it quickly.
Report if required: Notify authorities if the incident meets reporting thresholds under the EU AI Act or SR 11-7.
Where to Go for More
ISO/IEC 42001:2023, Annex A.6.1.3 covers verification and validation requirements, including Adversarial Simulation for high-risk systems. The NIST AI RMF Playbook provides specific practices for adversarial robustness assessment. For financial services, SR 11-7 ongoing monitoring expectations include Adversarial Simulation as part of outcomes analysis. The EU AI Act's General-Purpose AI Code of Practice will establish Adversarial Simulation baselines for foundation model providers.
