LLM-Powered Customer Service Platform Security
Specialised AI security testing for a Large Language Model-powered customer service platform handling sensitive customer data
The Challenge
A UK company deploying a Large Language Model-powered customer service platform needed specialised security assessment before handling sensitive customer data. The platform integrated multiple LLMs, RAG (Retrieval-Augmented Generation) systems, and third-party plugins, creating a complex attack surface requiring expertise in emerging AI security threats.
Traditional security testing approaches were insufficient for identifying AI-specific vulnerabilities such as prompt injection, training data extraction, and adversarial manipulation. The client needed assurance that their innovative platform was secure against cutting-edge threats while maintaining GDPR compliance.
Our Approach
We conducted specialised AI security testing using the OWASP LLM Top 10 framework and MITRE ATLAS:
Prompt Injection & Jailbreaking
Extensive testing of prompt injection techniques including direct prompt injection, indirect injection via external data sources, and jailbreaking attempts to bypass system guardrails. We tested the model's resilience to instruction override and context hijacking.
Training Data Extraction Testing
Attempts to extract training data, personally identifiable information (PII), and proprietary information memorised by the model. We tested membership inference attacks and data reconstruction techniques to identify potential data leakage.
Plugin & Tool Integration Security
Security review of LLM plugins, function calling mechanisms, and tool use capabilities. We tested for insufficient input validation, excessive permissions, and unsafe code execution in the agent ecosystem.
RAG System & Vector Database Assessment
Testing of Retrieval-Augmented Generation system security including vector database access controls, context injection vulnerabilities, and data segregation in embeddings. We assessed how external knowledge bases could be exploited.
Model Denial of Service Testing
Assessment of resource exhaustion attacks exploiting expensive model operations, infinite loops in agents, and excessive API calls. Testing for rate limiting and resource management vulnerabilities.
API Security & Rate Limiting
Traditional API security testing combined with AI-specific concerns including token usage limits, concurrent request handling, and authentication mechanisms for LLM access.
Key Findings
- Critical Prompt Injection Vulnerability: Discovered techniques allowing complete bypass of system instructions, enabling unauthorized access to customer data and administrative functions
- Training Data Leakage: Successfully extracted customer PII and internal data that had been inadvertently included in training data, violating GDPR requirements
- Insecure Plugin Design: Plugins had excessive permissions and insufficient input validation, allowing execution of arbitrary functions and unauthorised data access
- Context Manipulation in RAG System: Ability to inject malicious context into the RAG system, poisoning responses for other users
- Insufficient Guardrails: Content filtering and safety guardrails could be bypassed through carefully crafted prompts
- Lack of Output Validation: LLM outputs not properly validated before being used in downstream systems, creating injection vulnerabilities
- Model Resource Exhaustion: Ability to craft inputs causing excessive token usage and computational cost
Remediation & Implementation
We provided comprehensive remediation guidance aligned with OWASP LLM Top 10:
- Secure prompt engineering with role separation and instruction protection
- Training data sanitization and PII removal procedures
- Plugin security framework with principle of least privilege
- RAG system hardening with proper access controls and data segregation
- Multi-layer guardrail implementation with redundancy
- Output validation and sanitization before downstream use
- Resource management with per-user quotas and rate limiting
- Comprehensive logging for AI-specific security events
- GDPR-compliant data handling procedures for AI systems
Following remediation, we conducted extensive retesting validating that all critical AI security vulnerabilities were successfully addressed.
Impact & Outcome
The client successfully deployed their LLM-powered customer service platform with confidence in its security posture. By identifying and addressing AI-specific vulnerabilities before production deployment, they prevented potential data breaches affecting 500,000+ customer records, ensured GDPR compliance, and positioned themselves as a security-conscious AI innovator in their market.