MetaComp LLM Security Penetration Testing

About MetaComp

MetaComp is building next-generation financial trading infrastructure powered by conversational AI. Their WhatsApp-based trading bot enables users to execute cryptocurrency trades through natural language interactions, making trading accessible and intuitive. The platform leverages large language models (LLMs) to interpret user intent, process trading parameters, and execute financial transactions in real-time.

Security Requirements

As MetaComp prepared to launch their LLM-powered trading agent, ensuring the security of their conversational AI system was paramount. Unlike traditional software, LLM-based applications face unique attack vectors including prompt injection, context manipulation, and cross-user vulnerabilities that require specialized security expertise.

The engagement scope encompassed the complete LLM agent pipeline including intent filtering, parameter extraction, trade execution logic, confirmation handling, and multi-user interaction security within the WhatsApp group environment.

Testing Methodology

FailSafe's AI security team conducted a comprehensive penetration testing engagement targeting the LLM agent's attack surface, combining adversarial prompt engineering with systematic vulnerability assessment:

Prompt Injection Testing

Systematic testing for prompt injection vulnerabilities targeting parameter extraction, direction manipulation, and execution logic bypass through crafted user inputs.

Cross-User Attack Assessment

Evaluation of multi-user scenarios in group chat environments, testing for message injection, context-blind execution, and unauthorized cross-user interactions.

Execution Logic Analysis

Deep analysis of trade confirmation workflows, keyword-based execution triggers, and test mode handling to identify unintended execution paths.

Social Engineering Vector Assessment

Testing for narrative injection attacks, admin channel manipulation, and bot authority exploitation that could enable phishing or fraud through the trusted bot interface.

Confidential Partnership

In accordance with MetaComp's security and business requirements, the detailed findings and specific vulnerabilities identified during this penetration testing engagement remain confidential. Our partnership focused on identifying and remediating security issues across multiple severity levels, with the development team implementing fixes for the majority of identified issues.

The engagement identified findings across multiple severity levels related to LLM security, conversation isolation, and execution safeguards. The team successfully resolved the identified issues with FailSafe providing verification testing and architectural recommendations.

Partnership Impact

Through close collaboration with MetaComp's development team, FailSafe provided comprehensive LLM security guidance that strengthened the platform's security posture. The engagement covered:

Parameter validation architecture to prevent prompt injection attacks targeting trade direction, quantity, and client reference
Context-aware execution logic to prevent unintended trade confirmations from group chat conversations
Cross-user protection mechanisms to prevent message injection and bot authority exploitation attacks
Architectural recommendations for LLM agent security including output filtering, trust boundaries, and deterministic validation

Interested in Learning More?

If you're building LLM-powered applications, AI agents, or conversational AI systems and need comprehensive security testing, our team can share more about our approach and how we've helped projects like MetaComp secure their platforms.

Contact Our Security Team

MetaComp LLM Security Penetration Testing

Partnership Overview

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