While AI red teaming is a highly specialized discipline, it does not exist in a vacuum. Every AI system is built upon, and interacts with, a vast ecosystem of general information technology. A fluent understanding of the fundamental IT lexicon is non-negotiable. This section serves as a quick-reference guide to the common abbreviations you will encounter when assessing the infrastructure, networks, and operational environments that host AI models.
Core Infrastructure and Networking
These terms relate to the foundational hardware, software, and network layers where AI systems are deployed. Misconfigurations or vulnerabilities at this level can bypass even the most robust model-level defenses.
| Abbreviation | Stands For | Relevance in AI Red Teaming |
|---|---|---|
| API | Application Programming Interface | The primary attack surface for interacting with most production AI models. Testing focuses on input validation, rate limiting, authentication, authorization, and potential for injection attacks. |
| CDN | Content Delivery Network | Often used to serve model assets or API endpoints. Can introduce vulnerabilities related to caching, misconfigured rules, or IP address obfuscation, affecting availability and access control. |
| CPU / GPU / TPU | Central / Graphics / Tensor Processing Unit | The hardware executing the model. Understanding the target hardware can inform resource exhaustion attacks, side-channel analysis, or exploits targeting specific hardware-level libraries (e.g., CUDA). |
| DNS | Domain Name System | Critical for locating model endpoints. DNS hijacking, spoofing, or cache poisoning can redirect legitimate traffic to a malicious model or intercept sensitive data in transit. |
| HTTP(S) | Hypertext Transfer Protocol (Secure) | The protocol used for most API communication. Assess for weak TLS/SSL ciphers, certificate issues, and standard web vulnerabilities (e.g., header injection) that apply to the AI service’s web layer. |
| IP | Internet Protocol | Understanding IP addressing, subnetting, and routing is fundamental for network reconnaissance, identifying exposed services, and bypassing network access controls (ACLs). |
| OS | Operating System | The software layer managing the hardware. OS-level vulnerabilities (e.g., unpatched kernels, insecure services) can lead to container escape, privilege escalation, and full system compromise. |
| SDK | Software Development Kit | A common way for developers to interact with an AI service. The SDK itself can be an attack surface, containing vulnerabilities, insecure defaults, or leaking sensitive information like API keys. |
| VPN / VPC | Virtual Private Network / Virtual Private Cloud | Mechanisms for creating secure, isolated networks. Misconfigurations are common and can expose internal AI development environments, data stores, or management interfaces to unauthorized access. |
Cloud and Service Models
Most modern AI development and deployment occurs in the cloud. The service model dictates the scope of your responsibility and the potential attack vectors available to you during an engagement.
| Abbreviation | Stands For | Relevance in AI Red Teaming |
|---|---|---|
| IaaS | Infrastructure as a Service | The client manages the OS, middleware, and applications. Red teaming scope is broad, including OS hardening, network configuration, and everything up the stack to the model itself. |
| PaaS | Platform as a Service | The provider manages the OS and underlying infrastructure. Focus shifts to application-level security, identity and access management (IAM), and secure configuration of the platform services being used. |
| SaaS | Software as a Service | The provider manages everything; the client is a user. Red teaming is typically limited to the application’s exposed features, API abuse, data privacy issues, and business logic flaws. |
| IAM | Identity and Access Management | A critical control plane in cloud environments. Misconfigured IAM roles and policies are a primary vector for privilege escalation and unauthorized access to training data, models, and infrastructure. |
Data and Development
These terms are central to the data that fuels AI models and the processes used to build and deploy them.
| Abbreviation | Stands For | Relevance in AI Red Teaming |
|---|---|---|
| CI/CD | Continuous Integration / Continuous Delivery | The automated pipeline for building, testing, and deploying software. A prime target for supply chain attacks, such as poisoning training data, injecting backdoors into model code, or stealing credentials. |
| DB | Database | Stores training data, user data, logs, or model metadata. A primary target for data exfiltration. Assess for injection vulnerabilities (e.g., SQLi), poor access controls, and unencrypted sensitive data. |
| JSON / XML / YAML | JavaScript Object Notation / Extensible Markup Language / YAML Ain’t Markup Language | Common data-interchange and configuration formats. Often used for API requests/responses and infrastructure-as-code. Can be vectors for injection, deserialization attacks, or exposing sensitive configuration details. |
| ETL | Extract, Transform, Load | The process of moving data from sources to a data warehouse. Vulnerabilities in ETL pipelines can be exploited to poison training data at its source, leading to widespread model corruption. |