LLM Penetration Testing

Case Study Detail: LLM Penetration Testing

 

Title: Securing Generative AI for a Regional Latin American Bank
Service: AI/LLM Red Teaming · Sector: Financial Services · Duration: 3 weeks

 

Background

 

A mid-sized Latin American financial institution had begun experimenting with generative AI tools, including LLM-powered chat interfaces for customer support and internal knowledge retrieval. While innovation was the goal, security concerns were growing rapidly, especially around prompt injection, hallucination, and model misuse.

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The client needed a trusted third-party to simulate threats, identify vulnerabilities, and validate alignment with NIST 800‑53 and GLBA controls before expanding adoption across the enterprise.

 

The Challenge

 

Despite having strong general cybersecurity practices, the client's AI stack introduced novel risks:

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• No formal prompt injection detection

• Shadow use of internal GPT deployments with inconsistent access controls

• No documentation for LLM-specific risk models

• Lack of linkage to existing regulatory frameworks

 

Our Approach

 

HOSTA Analytics delivered a 3-week red team engagement including:

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1. LLM threat model development customized to the bank's internal stack

2. Simulated prompt injections, including system override, escalation-of-access, and role leakage

3. Analysis of hallucination under constrained and adversarial prompts

4. Remediation plan linked to specific NIST and GLBA control areas

5. Executive-level workshop for cybersecurity and compliance leadership

 

All testing was done ethically using containerized deployments and masked prompts to preserve operational integrity.

 

Results

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• 4 critical vulnerabilities identified, including one involving prompt chaining that accessed masked PII

• Model misuse map created to define abuse boundaries by department

• Delivered remediation matrix linked to 6 NIST 800‑53 controls

• Client now uses the HOSTA LLM Risk Framework as a standard AI onboarding tool

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