AI Security for Executives Part 3 - Supply Chain
The Yin and Yang of AI
Executive Summary
Supply chain attacks, where adversaries compromise third-party providers, are nothing new. The 2019 SolarWinds breach affected 18,000 customers. Target's 2013 breach started through an HVAC vendor. When it comes to AI, the attack surface expands dramatically. Models trained on poisoned data, compromised development environments, and vulnerable third-party components create entirely new vectors for infiltration.
What Executives Must Do
Establish vendor due diligence protocols that specifically address AI security risks.
Source AI models exclusively from reputable vendors with verified security practices and clear liability frameworks, like Google, OpenAI, Anthropic.
Implement rigorous testing protocols for all AI components before production deployment.
Maintain detailed inventories of all AI components with regular security audits and update schedules.
A Scenario
This scenario illustrates real situations that have been reported in the news, though the specific incident and company name is fictional.
Marcus Rodriguez, CTO of TechFlow Manufacturing, believed he had found the perfect solution. Rather than investing in expensive custom development, his team integrated what appeared to be a cutting-edge computer vision model from VisionCore Analytics, a promising startup offering enterprise-grade defect detection at a fraction of the cost of established providers.
The implementation exceeded expectations. The model demonstrated remarkable accuracy during initial testing, identifying manufacturing defects with precision that outperformed their existing quality control processes. Within six months, TechFlow's AI-powered quality control was analyzing over 100,000 components daily, reducing waste by 15% while improving product reliability scores across all product lines.
The first warning sign emerged during a routine compliance audit. TechFlow's security team discovered that the AI occasionally flagged components using classification criteria that referenced proprietary manufacturing processes from competing industrial companies - technical knowledge that should have been impossible for the model to possess. When Marcus's team pressed VisionCore for explanations, the vendor's responses became increasingly evasive, citing trade secrets and proprietary training methodologies.
The investigation that followed revealed an unfortunate reality. VisionCore had trained their model using datasets systematically scraped from quality control systems across the manufacturing sector, including several of TechFlow's direct competitors. The startup had essentially built their competitive advantage on stolen intellectual property.
Faced with potential legal liability and at the strong recommendation of their legal counsel, TechFlow's board immediately terminated the AI project. Marcus found himself explaining to investors how a decision designed to save costs had instead created a multi-million dollar setback.
About This Series
This series addresses C-suite executives making critical AI investment decisions given the emerging security implications.
I structured this series based on recommendations from the Open Web Application Security Project because their AI Security Top 10 represents the consensus view of leading security researchers on the most critical AI risks.
This series provides educational overview rather than specific security advice, since AI security is a rapidly evolving field requiring expert consultation. The goal is to give you the knowledge to ask the right questions of your teams and vendors.
Understanding AI Supply Chain Risks
Traditional supply chain security focuses on code dependencies and infrastructure providers. AI introduces new risks. Consider a traditional software library: you can inspect its code, understand its functionality, and predict its behavior. AI models, by contrast, are black boxes trained on massive datasets whose contents and quality remain largely opaque.
The challenge extends beyond individual models to entire AI development ecosystems. Modern AI applications typically integrate multiple components: pre-trained foundation models, fine-tuning datasets, development frameworks, deployment platforms, and monitoring tools. Each component represents a potential attack vector, and the interdependencies between them create complex failure modes that are, naturally, also complex. It's the yin/yang of AI: The extreme speed to market also brings enormous complexity that is all too easy to ignore.
Attack Scenarios in Detail
Data Poisoning Through Training Sources. Adversaries compromise AI models by injecting malicious data into training datasets. These attacks don't require exploiting software vulnerabilities, they exploit the fundamental machine learning process itself. A model trained on poisoned data will reliably produce compromised outputs while appearing to function normally during standard testing.
Model Substitution Attacks. Attackers replace legitimate AI models with compromised versions that maintain similar performance on standard benchmarks while introducing subtle backdoors. These substituted models can be programmed to respond to specific trigger phrases or inputs, allowing attackers to manipulate system behavior at predetermined moments. This is one of the reasons to stick with mainline vendors.
Runtime Prompt and Configuration Compromise. Attackers target AI systems during operation to extract sensitive prompts, system instructions, or fine-tuning data through traditional exploitation techniques like SQL injection, API vulnerabilities, or compromised credentials. Once attackers gain access to runtime environments, they can steal proprietary prompts that represent significant intellectual property or manipulate system configurations to alter AI behavior in real-time.
Dependency Chain Attacks. AI applications rely on numerous third-party libraries and frameworks, each with their own security vulnerabilities. Attackers can compromise these dependencies to inject malicious code that executes whenever the AI system runs. The complexity of AI software stacks makes these attacks particularly difficult to detect and remediate.
Legal and Licensing Risk. AI supply chain attacks are reported to create complex vulnerabilities. Carefully review contracts that you sign with AI vendors. I'm certainly no lawyer, consult your own counsel.
Actions for Executives
1. Establish Vendor Due Diligence Protocols That Address AI Security Risks. Your vendor evaluation process must address AI-specific risks that traditional technology assessments overlook. Require vendors to provide detailed documentation of their data sources, model training methodologies, security practices, and right to use their training data. Demand transparency.
2. Source AI Models Exclusively from Reputable Vendors with Verified Security Practices. Traditional procurement processes are inadequate for AI technologies. Develop specialized requirements that address model provenance, training data quality, and ongoing security monitoring capabilities. Require vendors to provide regular security updates and maintain vulnerability disclosure programs specifically for their AI offerings. Prioritize established providers like Google, OpenAI, and Anthropic that have demonstrated commitment to security practices and transparency.
3. Implement Rigorous Testing Protocols for All AI Components Before Production Deployment. Standard software testing methodologies are insufficient for AI systems. Develop testing protocols that specifically address adversarial attacks, data leakage, and model integrity. This includes red team exercises designed to identify AI-specific vulnerabilities and ongoing monitoring for model drift and performance degradation.
4. Maintain Detailed Inventories of All AI Components with Regular Security Audits. Implement comprehensive monitoring for all AI components in your environment. This includes tracking model performance metrics, monitoring for unusual outputs or behaviors, and maintaining detailed logs of all AI-related activities. Establish baseline behaviors for your AI systems and implement automated alerting for deviations that might indicate compromise or degradation.
Let me put this one in a much simpler and more direct way: Don't make your security team try and figure out the structure of your systems while the crisis is actually happening. It's better to go through the work of documenting environments ahead of time.
Actions for Developers
I do not attempt to go into any sufficient detail for these (and some of them are duplicative). I want you to understand the developer perspective at a high level. These are straight from OWASP.
Vetting and Auditing. Carefully evaluate all data sources and suppliers, including their terms and conditions, privacy policies, and security practices. Conduct regular security assessments and maintain ongoing monitoring of vendor security posture.
Component Management. Apply established vulnerability management practices to AI components, including regular scanning, patch management, and version control. Treat AI models as critical software components requiring the same security discipline as traditional applications.
AI Red Teaming and Evaluations. Conduct comprehensive adversarial testing when selecting third-party models, particularly for planned use cases. Understand that fine-tuning can bypass published security benchmarks, requiring specialized testing approaches.
Software and AI Bills of Materials. Maintain current inventories of all AI components to enable rapid vulnerability detection and response. Implement automated alerting for newly discovered vulnerabilities and establish clear procedures for component replacement.
License Management. Create comprehensive inventories of all licenses associated with AI components and conduct regular compliance audits. Use automated tools to ensure ongoing compliance and transparency regarding intellectual property rights.
Model Integrity Checks. Source models exclusively from verifiable providers and implement cryptographic verification for all AI components. Use third-party integrity verification services when available and maintain detailed provenance records for all models.
Monitoring Collaborative Environments. Implement strict oversight for AI development environments, particularly those involving external collaboration. Use specialized tools designed for AI development security and maintain detailed audit trails for all development activities.
Anomaly Detection and Adversarial Testing. Deploy automated systems to detect model tampering and data poisoning attempts. Integrate these capabilities into your MLOps pipelines and conduct regular adversarial robustness assessments.
Patching Policy. Ensure all AI applications use maintained versions of APIs and underlying models. Establish clear procedures for updating AI components and maintain compatibility testing for all updates.
Edge Security. Implement encryption and integrity verification for AI models deployed at edge locations. Use vendor attestation APIs when available and implement monitoring for tampered applications and models.
Summary
The complexity of AI systems creates new third party risk by providing complexity in which malicious users can hide. We've gone over some common concerns, but this list certainly isn't exhaustive. The area is somewhat inscrutable, so choose your vendors wisely.
What Executives Must Do
Rigorous vendor diligence
Use reputable vendors
Rigorously test for AI specific problems like prompt injection
Maintain detailed inventories of all AI components
Glossary
Supply Chain Attack: Cyber attack that compromises third-party vendors to infiltrate customer organizations, exploiting trusted relationships to bypass traditional security controls
Data Poisoning: Injection of malicious data into AI training datasets, causing models to produce compromised outputs while appearing to function normally during testing
Model Substitution: Replacement of legitimate AI models with compromised versions that maintain similar performance while introducing hidden backdoors or vulnerabilities
SBOM (Software Bill of Materials): Detailed inventory of all software components, including AI models and dependencies, enabling rapid vulnerability detection and response
Cryptographic Signing: Digital verification method using mathematical signatures to ensure AI models haven't been tampered with or replaced by unauthorized versions
Red Team Exercise: Simulated adversarial testing designed to identify AI-specific vulnerabilities through systematic attack scenarios and security assessments
MLOps: Machine learning operations practices that integrate AI model development, deployment, and monitoring with established software engineering and security disciplines
Model Provenance: Documentation of AI model origins, training data sources, and development history, critical for assessing security risks and legal liability
Adversarial Testing: Security evaluation technique that uses malicious inputs designed to manipulate AI system behavior or extract sensitive training data
OWASP AI Security Top 10: Industry-standard framework identifying the most critical AI security risks, developed by leading security researchers and practitioners