Engineering
Leading technology and transportation giant enhances AI capabilities with HCLTech AssureAI
Our client, a global leader in the technology and transportation industry, operating in over 70 countries and 10,000 cities, developed an AI-powered chatbot for internal channel partners. This solution leveraged advanced text and image generation LLMs to provide accurate, safe, ethical and trustworthy responses, even when handling complex queries.
HCLTech’s red teaming experts were engaged to rigorously test the chatbot’s resilience and performance. Through comprehensive simulations and evaluations, we helped the client’s engineering team strengthen the chatbot's reliability and security, successfully launching their LLM-based solution into the market.
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The Challenge
Testing the chatbot against adversarial inputs
LLM-powered chatbots face inherent risks and require rigorous testing to ensure reliability, effectiveness and alignment with client objectives. Before launching their chatbot, the client prioritized evaluating its AI model to identify vulnerabilities in its decision-making processes. The evaluation aimed to ensure that the chatbot could not bypass security measures, avoid disseminating inaccurate information and refrain from executing unintended actions when confronted with hostile, ambiguous or deceptive inputs.
The challenge extended to making the chatbot resilient against coordinated, large-scale and sophisticated attack scenarios, including subtle manipulations, harmful requests and logic exploitation attempts. Additionally, the chatbot's contextual awareness and ability to maintain accuracy and coherence in multi-turn conversations were critical evaluation points.
To facilitate the testing process, comprehensive documentation and detailed reporting were essential. This ensured that all identified vulnerabilities were clearly articulated, actionable and easily understandable, enabling effective remediation and enhanced security.
The Objective
Strategies for resilience against attacks
- To simulate real-world adversarial attacks designed to deceive or manipulate the chatbot into generating incorrect or harmful responses
- To assess the resilience of the chatbot's Natural Language Processing (NLP) algorithms in handling complex, deceptive and malicious queries
- To strengthen security measures, ensuring the chatbot consistently delivers accurate, safe and trustworthy interactions, even in challenging scenarios
- To identify and evaluate vulnerabilities, revealing potential weaknesses in the chatbot's design, implementation and security framework
- To enhance the chatbot’s overall security posture by providing actionable recommendations that address identified risks and mitigate future threats
The Solution
HCLTech AssureAI: Comprehensive LLM testing with red team attack simulations
HCLTech deployed AssureAI, our robust testing framework for Large Language Models (LLMs), supported by a dedicated team of red teamers to conduct comprehensive, multi-layered attack simulations that mirror real-world threats.
The simulated attack vectors encompassed evasion attacks, injection attacks and perturbation attacks. We tested for prompt injection vulnerabilities by using malicious prompts and commands and assessed the chatbot's capability to handle deceptive or misleading information. Furthermore, we incorporated social engineering scenarios to evaluate user vulnerability, while data poisoning inputs and elicitation queries aimed to extract sensitive information from the chatbot.
The team was also tasked with evaluating vulnerabilities through a thorough analysis of adversarial testing results, enabling us to identify and categorize the findings effectively.
Ultimately, we compiled our insights into a detailed report, offering actionable recommendations for remediation to enhance overall security.
The Impact
Turning failures into $500K savings: Red teaming's strategic advantage
Static test results:
Total prompts executed: 1590, Passed: 1443 (91%), Failed: 157 (9%)
Advanced test results:
Total prompts executed: 1500, Passed: 1288 (86%), Failed: 212 (14%)
Red teaming identified 1500 adversarial inputs during advanced testing, reducing the system's pass rate from 91% to 86%. This 14% increase in the failure rate highlighted the critical role played by the red teamers in identifying vulnerabilities and stress-testing system resilience.
The early detection and resolution of these vulnerabilities translated into significant financial benefits, saving an estimated $500000 by preventing potential downtime, data breaches and expensive system repairs. These results underscored how proactive red teaming not only strengthens security but also delivers measurable cost-saving advantages for organizations.
