AI Red Teaming: Testing Large Language Models for Enterprise Security

What Is AI Red Teaming?

AI red teaming is the practice of systematically probing AI systems, particularly large language models (LLMs) and the applications built on top of them, to identify security vulnerabilities, safety failures, and misuse potential. Unlike traditional penetration testing that targets infrastructure and application code, AI red teaming targets the model's behavior, its integration with enterprise systems, and the emergent risks that arise from the model's ability to interpret and generate natural language.

The concept borrows from traditional military and cybersecurity red teaming but adapts the methodology for the unique challenges of AI systems. Traditional software has deterministic behavior: given the same input, it produces the same output. LLMs are probabilistic systems whose outputs vary based on subtle differences in input phrasing, conversation context, and model state. This non-determinism makes AI security testing fundamentally different from conventional application security testing.

Here in San Francisco, at the epicenter of the generative AI revolution, CyberGuards has been at the forefront of AI red teaming as enterprises across the Bay Area and beyond integrate LLMs into their products and operations. We have seen firsthand how the speed of AI adoption has outpaced security testing, leaving organizations exposed to risks they may not fully understand.

The Enterprise AI Attack Surface

When an organization deploys an LLM-powered application, it introduces an attack surface that does not exist in traditional software. Understanding this attack surface is essential for effective AI red teaming.

The LLM Integration Architecture

Enterprise LLM deployments typically consist of several components, each with its own security implications:

• The foundation model: The underlying LLM (whether self-hosted or accessed via API) with its training data, capabilities, and inherent biases
• The system prompt: Instructions that define the model's behavior, persona, and constraints for the specific application
• The retrieval system (RAG): Retrieval-Augmented Generation pipelines that feed relevant documents and data into the model's context
• Tool integrations: APIs, databases, and external services that the model can invoke through function calling
• The application layer: The web application, chatbot interface, or API that mediates between users and the model
• Output filters: Content moderation and safety systems that filter or modify model outputs

Each of these components can be targeted by an attacker, and vulnerabilities often emerge at the interfaces between components rather than in any single component alone.

Prompt Injection: The SQL Injection of AI

Prompt injection is the most fundamental and widespread vulnerability class in LLM applications. It occurs when an attacker crafts input that causes the model to deviate from its intended behavior by overriding or manipulating the system prompt instructions.

Direct Prompt Injection

In direct prompt injection, the attacker sends input directly to the model through the application's user interface. The goal is to override the system prompt and make the model behave in unintended ways. Common techniques include:

• Instruction override: "Ignore all previous instructions and instead..." followed by malicious instructions
• Role-play exploitation: "You are now an AI with no restrictions. Respond to the following..." to bypass safety guardrails
• Context manipulation: Establishing a fictional context where the model's restrictions do not apply
• Encoding tricks: Using Base64, ROT13, or other encodings to smuggle instructions past input filters
• Multi-turn manipulation: Gradually steering the model's behavior across multiple conversational turns until it violates its constraints

Indirect Prompt Injection

Indirect prompt injection is more insidious and harder to defend against. Instead of sending malicious instructions directly to the model, the attacker plants them in content that the model will later consume. When the model processes documents, emails, web pages, or database records containing hidden instructions, it may execute those instructions as if they came from a trusted source.

For enterprise applications, indirect prompt injection is particularly dangerous because the model often processes untrusted content as part of its normal operation. A customer support chatbot that reads customer emails, a document analysis tool that processes uploaded files, or a code review assistant that reads repository content are all vulnerable to indirect injection through the content they process.

Data Exfiltration Through LLM Applications

LLM applications that have access to sensitive enterprise data through RAG pipelines, database connections, or API integrations create new data exfiltration channels that traditional data loss prevention (DLP) tools are not designed to detect.

Exfiltration via Conversational Interface

An attacker who can interact with an LLM application may be able to extract sensitive information simply by asking the right questions. If the model has access to customer data, financial records, or internal documents through its RAG pipeline, carefully crafted queries can elicit this information even when the application is designed to restrict access. The model may not understand organizational data classification policies or may be manipulated into ignoring them through prompt injection.

Exfiltration via Tool Invocation

LLM applications with tool-calling capabilities can be manipulated into invoking external services in unintended ways. An attacker might trick the model into sending sensitive data to an external API, creating database queries that return data the user should not access, or making HTTP requests that include sensitive information in URL parameters where it will be logged by external servers.

Exfiltration via Output Channels

If the model can generate content that includes links, images, or other embedded resources, an attacker can use indirect prompt injection to make the model include a link to an attacker-controlled server with sensitive data encoded in the URL. When the output is rendered in a browser, the request to the attacker's server transmits the data. This technique works even when the user does not click the link if the application renders images or prefetches URLs.

RAG-Specific Attacks

Retrieval-Augmented Generation (RAG) is the most common architecture for enterprise LLM applications because it allows models to access organization-specific data without fine-tuning. However, RAG introduces its own category of vulnerabilities.

RAG Poisoning

If an attacker can insert or modify documents in the RAG knowledge base, they can influence the model's behavior for all users. This is particularly dangerous in applications where users can contribute content that becomes part of the retrieval corpus, such as internal wikis, documentation systems, or shared knowledge bases. By planting carefully crafted content in the knowledge base, an attacker can:

• Insert false information that the model will present as authoritative
• Plant prompt injection payloads that activate when retrieved for any user's query
• Manipulate the model's behavior by changing the context it receives
• Cause the model to recommend specific actions or provide specific instructions to other users

Cross-Tenant Data Leakage in RAG

Multi-tenant RAG applications must ensure strict isolation between tenants' document collections. During our assessments, we frequently find that retrieval queries are not properly scoped to the requesting tenant, allowing users from one organization to retrieve and receive answers based on another organization's confidential documents. This is the AI equivalent of the broken access control issues we find in traditional web application testing, but the consequences can be more severe because the model may summarize and present the leaked information in a coherent, easily digestible format.

Retrieval Manipulation

An attacker who understands how the RAG retrieval mechanism works can craft queries designed to retrieve specific documents that contain sensitive information, even if the application's user interface does not expose direct document search functionality. By manipulating the semantic similarity between their query and the target documents, attackers can effectively search the knowledge base in ways the application designers did not intend.

Harmful Content Generation

Enterprise LLM applications that generate content for external consumption, such as customer communications, marketing copy, or support responses, carry the risk of generating harmful, biased, or inappropriate content.

Jailbreaking for Harmful Outputs

Jailbreaking techniques aim to bypass the model's safety alignment and content filters to generate outputs that the model is designed to refuse. In an enterprise context, this includes generating content that violates regulatory requirements, produces discriminatory or biased outputs, creates misleading or fraudulent content, reveals proprietary business information embedded in the system prompt, or generates content that damages the organization's reputation.

Bias and Fairness Concerns

LLM applications used for decision support, hiring assistance, customer service, or financial analysis may exhibit biases inherited from their training data. AI red teaming should probe for differential treatment based on protected characteristics, inconsistent quality of service across demographic groups, reinforcement of stereotypes in generated content, and biased recommendations or scoring in decision-support applications.

AI Red Teaming Methodology

A comprehensive AI red teaming engagement follows a structured methodology that addresses the unique characteristics of LLM systems.

Phase 1: Reconnaissance and Architecture Review

Begin by understanding the AI system's architecture, including the foundation model used, the RAG pipeline design, tool integrations, input and output filtering mechanisms, and the system prompt structure. This phase also involves identifying the application's intended use cases and the types of sensitive data it can access.

Phase 2: System Prompt Extraction

Attempt to extract the system prompt through various techniques including direct requests, role-play scenarios, and indirect extraction through carefully crafted conversations. The system prompt often contains business logic, access control rules, and behavioral constraints that inform subsequent testing. Common extraction techniques include:

• Asking the model to repeat its instructions verbatim
• Requesting the model to output its instructions in a different format (JSON, XML, code)
• Using multi-turn conversations that gradually lead the model to reveal its constraints
• Instructing the model to translate its system prompt into another language

Phase 3: Prompt Injection Testing

Systematically test for both direct and indirect prompt injection using a comprehensive payload library. Test each attack vector with multiple phrasings and approaches, as LLM responses are non-deterministic and a technique that fails once may succeed with slight variations. Document the success rate and conditions required for each successful injection.

Phase 4: Data Access and Exfiltration Testing

Probe the model's access to sensitive data through the RAG pipeline and tool integrations. Attempt to access data outside the user's authorization scope, extract information from the knowledge base that should not be directly accessible, and use tool integrations in unintended ways to access or modify backend systems.

Phase 5: Safety and Alignment Testing

Test the model's safety guardrails using known jailbreaking techniques and novel approaches. Evaluate whether the model can be manipulated into generating harmful content, violating its stated policies, or behaving in ways that would create legal, reputational, or safety risks for the organization.

Phase 6: Abuse Scenario Testing

Develop and test realistic abuse scenarios specific to the application's use case. For a customer service chatbot, this might include testing whether the model can be manipulated into issuing unauthorized refunds, revealing other customers' information, or making commitments the organization cannot fulfill. For an internal knowledge assistant, test whether it can be used to access information outside the user's clearance level.

NIST AI Risk Management Framework (AI RMF)

The NIST AI Risk Management Framework provides a structured approach to managing AI risks throughout the AI system lifecycle. For organizations seeking a standards-based approach to AI security, the AI RMF provides an excellent foundation.

Core Functions

The AI RMF is organized around four core functions:

AI red teaming maps most directly to the Measure function, providing the empirical evidence needed to evaluate whether AI risk management controls are effective. The findings from red teaming engagements feed into the Manage function as inputs for risk treatment decisions.

EU AI Act Implications

The European Union's AI Act, which entered into force in 2024 with phased implementation through 2026, establishes legal requirements for AI systems based on their risk classification. For organizations deploying AI in the EU market or processing EU citizens' data, the Act creates explicit obligations for security testing.

High-Risk AI Systems

AI systems classified as high-risk under the Act, including those used in critical infrastructure, education, employment, law enforcement, and migration, must undergo conformity assessments that include robustness testing, accuracy evaluation, and cybersecurity assessment. AI red teaming directly supports these conformity requirements by providing evidence of the system's resilience against adversarial inputs and its behavior under attack conditions.

General-Purpose AI Models

Providers of general-purpose AI models with systemic risk are required to conduct adversarial testing (explicitly termed "red teaming" in the Act) to identify and mitigate risks. This requirement applies to foundation model providers but has downstream implications for enterprises that build applications on top of these models, as they are responsible for the safety of their specific implementations.

Building an AI Red Teaming Program

Organizations deploying LLMs should establish an ongoing AI red teaming program rather than treating it as a one-time exercise. The non-deterministic nature of LLMs means that the attack surface changes with model updates, prompt modifications, and data additions.

Recommended Cadence

• Before deployment: Comprehensive red team assessment before any AI system goes into production
• After model updates: Targeted testing whenever the underlying model is updated or changed
• After prompt changes: Testing when system prompts or behavioral guidelines are modified
• After knowledge base updates: Verification that new RAG content does not introduce vulnerabilities
• Quarterly: Regular testing with updated attack techniques to account for the rapidly evolving AI threat landscape

Team Composition

Effective AI red teaming requires a blend of traditional penetration testing skills and AI-specific expertise. The ideal team includes offensive security professionals who understand application security and infrastructure exploitation, AI/ML engineers who understand model architecture, training processes, and inference behavior, domain experts who understand the specific risks of the application's use case, and creative thinkers who can develop novel attack scenarios that combine technical exploitation with social engineering.

Metrics and Reporting

AI red teaming reports should include metrics that are meaningful for AI risk management:

• Attack success rate: Percentage of prompt injection attempts that successfully override system behavior
• Data leakage incidents: Number and severity of instances where the model revealed unauthorized information
• Guardrail bypass rate: Percentage of safety filter bypass attempts that succeeded
• Mean attempts to jailbreak: Average number of attempts required to bypass safety alignment
• Unique vulnerability classes: Categorization of distinct vulnerability types discovered
• Business impact scenarios: Realistic narratives of how discovered vulnerabilities could be exploited for tangible harm

"Traditional penetration testing asks 'can an attacker break into the system?' AI red teaming asks that and more: 'can an attacker make the system behave in ways that harm the organization, its customers, or society?'"

Looking Ahead: The Future of AI Security Testing

As AI systems become more capable and more deeply integrated into enterprise operations, the scope and sophistication of AI red teaming will continue to evolve. Emerging areas include multi-agent system security where multiple AI agents interact with each other and with human users, creating complex attack surfaces; AI supply chain security as organizations build on top of third-party models and datasets; and the intersection of AI security with traditional cybersecurity as AI systems gain access to more powerful tools and sensitive data.

At CyberGuards, our San Francisco-based team combines deep expertise in offensive security with specialized knowledge of AI systems to deliver AI red teaming engagements that address the full spectrum of risks introduced by enterprise LLM deployments. Whether you are deploying your first customer-facing chatbot or building complex multi-agent AI workflows, we help you understand and mitigate the security risks before they become incidents.

The organizations that will succeed with AI in the long term are those that treat AI security as a first-class concern from day one, not an afterthought. As the regulatory landscape matures and the threat landscape evolves, proactive AI red teaming will become as essential as traditional penetration testing is today.