Red Teaming RAG Healthcare Chatbots

June 29, 2024

Retrieval-augmented generation (RAG) models use retrieval and generative techniques to craft relevant responses to user queries. Retrieval in RAG refers to the ability of RAG models to fetch information from a knowledge base, and Generation refers to the creation of relevant and personalized responses for users. Combining the two technologies gives RAG models strong support in various applications, including medical diagnosis and patient support through medical chatbots. 

However, RAG models risk being exploited by hidden biases in data, security vulnerabilities, and factual inaccuracies. Red teaming is a technique that simulates real-world scenarios for introducing a strategic attack to RAG systems to identify their vulnerabilities and protect against cyberattacks.

Patient outcomes, such as patient care, safety, etc., depend on the reliability of healthcare large language models (LLMs). For instance, hallucinations in healthcare chatbots can harm patient well-being, damage reputation, make artificial intelligence (AI) distrustful, and incur legal penalties. Therefore, reliable chatbots are the key to patient welfare and a streamlined healthcare workflow.

But how does red teaming protect healthcare chatbots from cyberattacks? 

Understanding Red Teaming

Red teaming surpasses traditional testing methods by mimicking real-world attackers’ Tactics, Techniques, and Procedures (TTPs). In contrast, traditional methods, like penetration testing, rely on simulated attacks to identify system vulnerabilities and assess their security effectiveness.

However, red teaming goes beyond penetration testing by using a zero-knowledge perspective. This approach ensures that no one in the organization is notified about the attack beforehand. Traditional methods focus on identifying the weaknesses in a system. In contrast, red teaming assesses an organization’s entire security posture and identifies an attacker’s potential to disrupt systems or steal data. 

Red teaming has the following benefits that conventional methods fail to offer:

  • It helps identify various attacks associated with business information, such as financial data, customer data, intellectual property, etc
  • It assesses how vulnerable these assets are using real-world simulations of adversaries.
  • Red teaming evaluates an organization’s ability to withstand those attacks and the effectiveness of the incident response teams
  • It uses the CREST (Council of Registered Ethical Security Testers) and STAR (Simulated Targeted Attack & Response) frameworks to ensure the standardized and consistent implementation of red teaming
  • It helps organizations prioritize security improvements based on the impact of potential attacks

Importance of Red Teaming in Healthcare AI

Healthcare chatbots have been transforming the healthcare industry, offering 24/7 support, access to necessary information without hassle, and basic symptom analysis. However, like other AI systems, healthcare chatbots are prone to hallucination. Chatbot hallucinations can result from bias in training data, incomplete training data, lack of contextual understanding of the chatbot, inability to handle sensitive data, etc. 

Risks and Challenges in Healthcare Chatbots

Below are the challenges healthcare chatbots face due to hallucinations:


Healthcare chatbots can provide inaccurate advice to patients. This can occur due to the lack of contextual understanding or generating false information. This can range from misdiagnosis to unnecessary anxiety for patients.


Training datasets can contain inherent bias which perpetuates in chatbot responses, resulting in hallucinations. Chatbot bias can be in the form of gender disparity or racial/cultural stereotypes. For example, overlooking critical symptoms in patients from a certain demographic. 

Data Privacy

Healthcare chatbots often handle sensitive information such as patient identity and history. Inappropriately handling sensitive information can lead to data leakage or cyber attacks, resulting in confidentiality breaches or system failure.

These challenges can give rise to serious issues like delayed treatment, health disparities, and erosion of trust among the public.

Confidentiality breaches due to sensitive information leaks can result in monetary loss. Additionally, the fear of data leakage can add psychological pressure on patients that leads to a loss of trust in AI, affecting their overall health. The reluctance of patients to share medical information can also lead to delays in medical treatment or misdiagnosis due to lack of information.

How Red Teaming Enhances Healthcare Chatbot Reliability

The simulated attacks act as a malicious user feeding a chatbot with intentionally misleading information. This exposes a chatbot’s ability to stand against real cyberattacks and prevent hallucinations. Red teaming tests usually take several weeks and involve bombarding the chatbot with many queries and unexpected questions. This reveals a chatbot’s ability to handle unusual requests and real-world scenarios.

The simulated attacks and stress tests offer insights into a chatbot’s accuracy and security. Chatbot’s response to queries reveals its frequency of generating accurate, unbiased, and secure outputs. A thorough comparison of chatbot responses with the RAG knowledge base and adherence to privacy policy guides chatbot developers toward its analysis and improvement.

Implementing Red Teaming for RAG Healthcare Chatbots

Implementing red teaming requires following a step-by-step process to ensure effective testing. The implementation of red teaming in RAG healthcare chatbots involves the following steps:

Steps to Red Team a RAG Healthcare Chatbot

1. Setting Objectives and Scope:

A successful red teaming assessment begins by identifying the objectives and scope of the test. The objective can be identifying vulnerabilities in the chatbot’s responses, testing its ability to handle sensitive data, or both. The scope involves specific functionalities or parts of a chatbot, such as backend infrastructure or user data handling processes. 

2. Gathering a Team:

Having clear objectives in place, you need to gather a team of experts who understand the needs and risks of red teaming in RAG systems. This includes domain experts like healthcare professionals who verify medical information, security analysts who analyze security threats in a chatbot, and AI specialists who understand the intricacies of RAG systems. The team may also involve compliance experts to ensure the healthcare regulations during testing.

3. Simulating Attacks:

Finally, the team develops scenarios that mimic real-world attacks that a chatbot might encounter. Some examples of simulating attacks include providing a chatbot with misinformation, seeking advice from an underrepresented user profile, malicious user attempts to reveal sensitive patient information, Denial of Service (DoS), etc. 

A chatbot’s response against these attacks, including logs, response times, and accuracy rates, helps the experts identify weaknesses in the system. A thorough analysis guides them in fine-tuning the chatbot’s algorithm and mitigating identified vulnerabilities.

Tools and Techniques for Red Teaming

Red teaming attack simulations are designed and formulated using various techniques. A few techniques are:

PASTA (Process for Attack Simulation and Threat Analysis)

PASTA is a threat modeling framework that encourages collaboration between stakeholders to understand a software’s likelihood of attack. It offers a contextualized approach that focuses on the business objectives for simulating attacks and leverages existing security testing activities in the organization.

Adversarial Testing

Adversarial testing involves mimicking real-world cyber attacks to identify vulnerabilities in AI systems. This allows organizations to strengthen their systems and withstand cyberattacks when deployed for real-world usage. Regular adversarial testing continuously improves AI systems’ performance, ensuring robust RAG systems.

Stress Testing

Stress testing aims to identify weaknesses in AI systems by simulating extreme conditions. Exposing AI systems to extreme conditions reveals their stability in the real world, such as high traffic loads for chatbots. Insights from stress testing allow organizations to take necessary actions to address RAG system weaknesses.

Case Studies and Real-World Examples

A team of 80 experts, including clinicians, computer scientists, and industry leaders, conducted a red teaming test to stress test in healthcare LLMs. The tests assessed safety, privacy, hallucinations, and bias in AI-generated healthcare advice. Three hundred eighty-two unique prompts were given to the LLMs, which generated 1146 total responses. Prompts were carefully crafted to reflect real-world scenarios, and six medically-trained reviewers evaluated all responses to ensure appropriateness. 

Common Vulnerabilities Discovered

Nearly 20% of AI-generated responses were inappropriate. This includes racial bias, gender bias, misdiagnosis, fabricated medical notes, and revealing patient information.  

Misinformation and Irrelevant Citations

When asked about specific allergies, LLMs responses mentioned any allergy, not necessarily the one queried. The LLMs also provided citations (references to articles) to support their claims. However, these articles often did not discuss the specific allergy queried.

Inaccurate Information Extraction

LLMs struggled to understand medical notes and, as a result, missed important information within the queries and their knowledge base.

Privacy Concerns

LLMs included protected health information (PHI) in their responses, raising privacy concerns and loss of trust. 

Effective Strategies to Address These Vulnerabilities

LLMs frequently provide misleading information, including factual errors, irrelevant citations, and fabricated medical notes. This necessitates significant improvements in data verification and model training to ensure trustworthy outputs. Additionally, LLM developers must address biases by using balanced datasets and incorporating fairness checks during model development. 

Robust safeguard practices are crucial to prevent privacy breaches and ensure patient data security. Lastly, the LLMs must be able to understand user intent to effectively address questions in an indirect tone. and unbiased text.


Red teaming is a powerful tool for mitigating AI threats in healthcare chatbots. As AI continues to develop, with new tools and innovations released every month, organizations must ensure their red teaming methodologies adapt to address emerging vulnerabilities. Adaptability to changing needs aids healthcare organizations in building robust and trustworthy RAG chatbots. 

The proactive approach of red teaming attacks empowers organizations to stay ahead of new vulnerabilities and build robust chatbots. Collaboration among team members builds a security culture within the organization, making stakeholders more invested in best practices. How has your experience been with red teaming? Share any tips and tricks you learned during the process. 

Contact us today to consult a team of experts who can help you develop and implement secure and reliable AI solutions with effective red teaming.

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