Setting Objectives and Scope for LLM Red Teaming

Just as a well-defined blueprint is essential before constructing a building, clearly articulated objectives and a precise scope are fundamental to any successful LLM red teaming engagement. Without them, your efforts can become unfocused, inefficient, and ultimately less impactful. This section details how to establish these critical parameters, ensuring your red teaming activities are targeted, effective, and aligned with stakeholder expectations.

Why Objectives and Scope Matter

Before diving into specific techniques, it's important to understand why dedicating effort to defining objectives and scope is so beneficial. In any technical assessment, especially one as open-ended as testing an LLM, it's easy to get sidetracked by interesting but irrelevant avenues of exploration.

• Focus: Clear objectives keep the team concentrated on the most significant risks and desired outcomes.
• Efficiency: A well-defined scope prevents "scope creep," where the engagement expands uncontrollably, consuming more time and resources than planned.
• Measurability: Objectives provide a benchmark against which the success of the red teaming operation can be measured.
• Clarity: All stakeholders (red team, blue team, developers, management) will have a shared understanding of what will be tested, how, and why.
• Expectation Management: It helps set realistic expectations about what the red team will deliver.

Think of this phase as drawing the map and setting the destination for your red teaming journey.

Defining Clear Objectives for LLM Red Teaming

The objectives of an LLM red teaming engagement specify what you aim to achieve. They should be driven by the specific concerns your organization has about the LLM in question. Vague goals like "test the LLM's security" are not helpful. Instead, objectives should be precise and actionable.

Common objectives for LLM red teaming include:

• Identify Vulnerabilities to Prompt Injection: Determine if the LLM can be manipulated through specially crafted inputs to bypass safety instructions or perform unintended actions. For example, can it be made to reveal its system prompt?
• Assess Resilience to Harmful Content Generation: Test the LLM's propensity to generate responses that are biased, discriminatory, hateful, or promote illegal activities, even when prompted benignly or adversarially.
• Evaluate Effectiveness of Safety Guardrails: If the LLM system includes input filters, output sanitizers, or other defensive mechanisms, assess their robustness and identify bypass techniques.
• Test for Sensitive Information Leakage: Determine if the LLM can be tricked into revealing confidential information it was trained on or has access to (e.g., personally identifiable information (PII), proprietary algorithms, or internal company data).
• Examine Jailbreaking Potential: Investigate how easily the LLM's built-in restrictions and ethical guidelines can be circumvented to make it comply with inappropriate requests.
• Assess Susceptibility to Misinformation Generation: Evaluate if the model can be prompted to generate plausible but false information, and how readily it does so.
• Verify Controls Against Specific Threat Actor Personas: Simulate attacks from particular types of adversaries (e.g., a disgruntled employee, an external activist) to see if their likely TTPs (Tactics, Techniques, and Procedures) would succeed.

When formulating objectives, consider making them SMART:

• Specific: Clearly state what will be tested and for what purpose. Instead of: "Test for bad outputs." Use: "Identify if the LLM can be induced to generate instructions for creating a phishing email, bypassing its content safety filter for financial harm."
• Measurable: Define how success or failure will be determined. Example: "Successfully elicit three distinct types of harmful content (e.g., hate speech, self-harm advice, illegal act promotion) within 20 attempts per category."
• Achievable: Ensure the objectives are realistic given the available time, resources, and access to the LLM.
• Relevant: The objectives must align with the organization's risk profile and the LLM's intended application. Testing for vulnerabilities that are not pertinent to the LLM's use case might be a misallocation of resources.
• Time-bound: Specify a timeframe for achieving the objectives. This is typically part of the overall engagement timeline.

These objectives often arise from discussions with various stakeholders, including the LLM developers, product managers, legal teams, and existing security teams. Their input is invaluable for ensuring the red team focuses on areas of genuine concern.

Defining the Scope of the Engagement

Once objectives are set, the next step is to define the scope. The scope outlines the boundaries of the red teaming engagement: what systems, models, interfaces, data, and techniques are included, and, just as importantly, what is excluded.

Key elements to consider when defining the scope for LLM red teaming:

1. Target LLM(s) and Versions:

   • Which specific LLM or models are being tested? (e.g., gpt-4-turbo, claude-3-opus, a custom fine-tuned llama-3-70b-instruct).
   • Are specific versions or deployment instances in scope? (e.g., the model in the staging environment vs. production).
   • Is it a base model, a fine-tuned variant, or an LLM integrated into a larger application stack?

2. Interfaces and Access Methods:

   • How will the red team interact with the LLM?
   • Examples: Direct API access (e.g., REST endpoints), a web application UI, a chatbot interface, SDKs.
   • What level of access will be provided? (e.g., user-level, admin-level if testing admin interfaces, black-box, grey-box, or white-box).

3. Attack Vectors to be Explored:

   • Which categories of attacks (as discussed in Chapter 2) are in scope?
   • Examples: Prompt injection, jailbreaking, attempts to elicit biased outputs, probing for training data leakage.
   • Are attacks against supporting infrastructure (e.g., the API gateway, vector databases if the LLM uses RAG) in scope, or is the focus strictly on the LLM's direct input/output behavior?

4. Data Considerations:

   • What types of data can the red team attempt to input or exfiltrate?
   • Is interaction with real sensitive data permitted, or should only mock/test data be used? This is a critical point requiring explicit approval.
   • Are there restrictions on storing or handling any data obtained from the LLM?

5. Permitted Techniques and Tools:

   • Are there any restrictions on the types of tools or techniques that can be used (e.g., automated fuzzers, specific open-source tools)?
   • Will the engagement primarily involve manual prompt crafting, or will automated approaches be employed?

6. Out-of-Scope Elements:

   • Explicitly list what is not part of the engagement. This avoids misunderstandings.
   • Common out-of-scope items might include:
     • Denial of Service (DoS) attacks against production systems.
     • Attacks on the underlying cloud infrastructure unless specified.
     • Social engineering of personnel.
     • Direct attacks against the training data pipeline (unless a data poisoning test is explicitly an objective).

The following diagram illustrates how different components of an LLM system might be categorized as in or out of scope for a typical red teaming engagement focused on prompt-based attacks.

This diagram shows a common LLM system setup. Components colored green are generally in scope for prompt-based attacks, yellow components might be conditionally in scope depending on objectives, and gray components are typically out of scope for direct attacks in such a scenario, though their interactions with the LLM might still be relevant.

The Interplay Between Objectives and Scope

Objectives and scope are not defined in isolation; they are deeply interconnected.

• Objectives drive the scope: If an objective is to test for data exfiltration from a connected knowledge base, then the LLM's interface to that knowledge base must be within the scope.
• Scope constrains the objectives: If the red team only has black-box access to the LLM (meaning no insight into its internal workings or training data), then objectives requiring white-box analysis (like gradient-based attacks) are not achievable and should be scoped out.

It's often an iterative process. You might start with a broad objective, then refine it based on what parts of the system can realistically be included in the scope given time and resource constraints.

Documenting Objectives and Scope: The Rules of Engagement

All these details, objectives, scope (inclusions and exclusions), timelines, allowed techniques, and any other constraints, must be formally documented. This document is often called the "Rules of Engagement" (RoE) or included in a "Statement of Work" (SoW).

This document serves several purposes:

• Provides a clear mandate for the red team.
• Ensures all parties (red team, system owners, management) have a shared understanding.
• Acts as a reference point if questions or disputes arise during the engagement.
• Helps in defining the success criteria for the operation.

Getting formal sign-off on this document from all relevant stakeholders is a standard practice before commencing any red teaming activities.

Common Hurdles in Setting Objectives and Scope

Defining objectives and scope is not always straightforward. Be aware of these common challenges:

• Scope Creep: As the engagement progresses, new, interesting attack vectors or vulnerabilities might be discovered that are technically outside the initial scope. It's important to either stick to the agreed-upon scope or formally discuss and approve any changes.
• Ambiguity: Vague language in objectives or scope can lead to misunderstandings. Strive for precision. For example, instead of "test API security," specify "test the /v1/chat API endpoint for prompt injection vulnerabilities."
• Overly Broad Scope: Trying to test "everything" in a single engagement can dilute focus and lead to superficial testing. It's often better to have multiple, focused engagements.
• Overly Narrow Scope: If the scope is too restricted, significant vulnerabilities might be missed. This is where understanding the LLM's full context and potential integration points becomes important.
• Resource Mismatch: The defined scope and objectives might be too ambitious for the allocated time or team size. Regular reality checks are needed.

Moving Towards Practical Application

Understanding how to set clear objectives and define a precise scope is a foundational skill for any LLM red teamer. These principles lay the groundwork for a structured and effective testing process. As you move through this course, and particularly in the hands-on exercise at the end of this chapter, you'll get a chance to apply these ideas to a simulated LLM red teaming scenario. By carefully planning your engagements, you maximize the value of your red teaming efforts and contribute meaningfully to the security and safety of LLM systems.