Recommending Actionable Mitigation Steps

Translating identified and prioritized vulnerabilities into clear, effective guidance for remediation is essential. Simply pointing out problems isn't enough; recommendations form the bridge that connects discoveries to tangible security improvements in the LLM system. The goal is to provide advice that development and security teams can understand, implement, and verify.

Core Principles for Crafting Recommendations

To ensure your mitigation advice leads to real change, keep the following principles in mind. Effective recommendations are not just about what to fix, but how to fix it in a practical manner.

• Clarity and Precision: Use unambiguous language. Your recommendations should be easy to understand by their intended audience, which might range from developers to product managers. If technical terms are necessary, ensure they are either commonly understood within the team or briefly explained. For example, instead of "harden the prompt interface," be specific about how it should be hardened.

• Specificity: This is critical. A vague recommendation like "improve input validation" is unlikely to be actioned effectively. Pinpoint:

  • What: The exact change or control to implement.
  • Where: The specific component, API endpoint, model, or data pipeline stage affected.
  • How (if applicable): Suggest particular techniques, algorithms, or configuration adjustments. For instance, "Implement an allow-list for special characters in prompts submitted to the /v1/query endpoint."

• Feasibility: Propose solutions that are realistic for the organization. Consider their existing technology stack, available resources, and operational constraints. A theoretically perfect solution that's impossible to implement is not helpful. Acknowledge potential trade-offs if a mitigation might impact performance or user experience, and perhaps suggest ways to balance these.

• Prioritization-Driven: Your recommendations should naturally flow from the risk assessment of each vulnerability. High-risk findings warrant more immediate and potentially more comprehensive mitigation strategies. Ensure that the urgency and depth of your recommendations align with the severity you've assigned.

• Verifiability: A good recommendation is one whose implementation can be tested. Frame your suggestions so that it's clear how one would verify that the fix is in place and effective. For example, "After implementing the output filter, attempts to elicit Social Security Numbers should result in masked output, which can be verified by re-running Test Case #123."

Structuring Your Recommendations

Organizing your recommendations logically within your report makes them easier to digest and act upon. For each significant finding, or a group of closely related findings, consider including the following:

1. Vulnerability Recap: A brief (1-2 sentence) reminder of the vulnerability being addressed. This provides context without requiring the reader to flip back and forth in the report.

   • Example: "Finding 3.1: Indirect prompt injection via ingested document allows unauthorized API calls."

2. Recommended Action(s): This is the core of your advice. Detail the specific steps to be taken. If multiple steps are involved, list them clearly.

   • Example: "1. Sanitize all text extracted from ingested documents to neutralize or remove common control characters and markdown that could be interpreted as instructions by the LLM. 2. Implement stricter parsing and validation on the content passed to the LLM's context window from external sources."

3. Rationale: Explain why this action is recommended and how it addresses the identified vulnerability. This helps stakeholders understand the purpose behind the proposed change.

   • Example: "Sanitizing document content before it's added to the LLM's context prevents malicious instructions embedded in those documents from being executed. Stricter parsing ensures that only intended data structures are processed."

4. Expected Outcome: Describe what successful implementation of the mitigation will achieve.

   • Example: "This mitigation should prevent the LLM from acting on hidden instructions within processed documents, thereby blocking the unauthorized API call pathway."

5. (Optional) Level of Effort/Resources: A high-level estimate (e.g., Low, Medium, High) can assist teams in planning and allocating resources for remediation.

6. (Optional) Alternative Solutions: If there are other ways to address the vulnerability, you might briefly mention them and explain why your primary recommendation is preferred. This shows you've considered various angles.

Examples: From Vague to Actionable

Let's look at how to transform general ideas into concrete, actionable recommendations. The table below illustrates this for common LLM vulnerabilities.

Vulnerability Type Example	Weak Recommendation	Strong, Actionable Recommendation	Actionable Elements
Direct Prompt Injection	"Secure against prompt injection."	"Implement input sanitization on the user prompt submission API (/api/chat). Specifically, escape or reject meta-characters and instruction-like sequences (e.g., 'Ignore previous instructions...'). Regularly update these patterns based on emerging attack techniques."	Specific API, types of patterns, continuous improvement
Sensitive Data Leakage in Output	"Prevent the model from leaking PII."	"Deploy an output scrubbing module that post-processes all LLM responses. This module should use regular expressions to detect and mask common PII patterns (e.g., credit card numbers, phone numbers) and a named entity recognition (NER) model trained to identify and redact internal project codenames."	Specific techniques (regex, NER), target data types
Jailbreaking / Policy Bypass	"Make the model follow rules."	"1. Enhance the existing input content filter to detect and block known jailbreak preambles and persona adoption requests. 2. Implement an output monitor that flags responses exhibiting characteristics of a successful jailbreak (e.g., sudden generation of restricted content, affirmative responses to forbidden requests) for human review and filter refinement."	Multi-layered defense, specific detection points
Training Data Poisoning	"Ensure training data is clean."	"Establish a data validation pipeline for all new training and fine-tuning datasets. This pipeline should include anomaly detection to flag outliers in data distribution and manual spot-checks for adversarial or biased content, especially for data sourced from untrusted external feeds."	Specific process, types of checks, data sources

Notice how the "Strong, Actionable Recommendation" column provides much more guidance. It tells the development team what to do, often where to do it, and sometimes even hints at how to do it.

Tailoring Recommendations to Different Audiences

While your main red team report will contain the full technical details, you'll often need to communicate these recommendations to different groups.

• For Development Teams: They need the technical specifics. Provide details about algorithms, libraries, configuration settings, or even pseudo-code if it helps clarify the implementation.
• For Product Managers and Leadership: Focus on the risk reduction achieved by implementing the recommendation. Explain how it improves user safety, trust, or aligns with business objectives. A high-level summary of the effort versus benefit can be very effective here.

Often, a comprehensive report serves as the source of truth, and then executive summaries or targeted presentations are created for different stakeholders.

Proposing Both Short-Term and Long-Term Fixes

Sometimes, a vulnerability might require a quick fix to immediately reduce risk, while a more strategic solution takes longer to develop and deploy.

• Short-Term (Tactical): These are often simpler changes, like blocking a specific malicious input pattern, adding a temporary rule to a WAF, or disabling a minor feature that’s being exploited.
  • Example: "Immediately block prompts containing the exact string ''."
• Long-Term (Strategic): These are more comprehensive solutions, such as retraining a model with new safety data, re-architecting an input processing pipeline, or implementing a sophisticated anomaly detection system.
  • Example: "Develop and integrate a context-aware input filtering system that uses a secondary LLM to assess the intent of user prompts before they reach the primary generation model."

If you propose both, clearly differentiate them and explain the rationale. This allows the organization to manage risk effectively while working towards more permanent solutions.

The Role of Collaboration

Remember, as a red teamer, you are an advisor. Your recommendations are expert suggestions, but the teams responsible for the LLM system (often called the blue team or development team) will ultimately implement them. Frame your recommendations as starting points for a discussion. They may have deeper insights into system constraints or alternative approaches that are equally effective. The next section, "Working with Development Teams for Remediation," will explore this collaborative aspect further.

By focusing on creating actionable, clear, and well-reasoned mitigation steps, you significantly increase the likelihood that your red teaming efforts will lead to a more secure and reliable LLM.