Business Need

A large global technology organization with widespread adoption of AI and Large Language Model (LLM)–powered applications was facing new security challenges driven by the rapid integration of emerging AI technologies. As AI/LLM usage expanded across business functions, the organization required greater assurance that these applications were secure, compliant, and resilient against misuse.

The introduction of AI-driven capabilities also raised concerns about data exposure, audit readiness, and escalating operational costs from uncontrolled model usage. These factors created the need for a focused security assessment approach tailored specifically to AI and LLM systems.

Business Challenges

The organization encountered several challenges related to the security and governance of its AI/LLM applications:

• Lack of Security Assurance for AI/LLM Applications: Existing security practices did not fully address LLM-specific risks, including prompt injection, jailbreaks, and insecure model interactions.
• Increased Compliance and Audit Risk: Limited visibility into AI application behavior increased the risk of audit gaps and regulatory concerns.
• Potential Data Exposure: Inadequate controls around prompts and outputs raised the risk of sensitive data leakage and disclosure of internal system details.
• Uncontrolled AI Usage and Cost Impact: Insufficient safeguards led to increased token consumption and higher operational costs.
• Limited Understanding of AI-Specific Threats: Traditional application security testing approaches did not adequately identify vulnerabilities unique to LLM-based systems.

Collectively, these challenges heightened business risk, impacted leadership confidence, and slowed secure adoption of AI technologies.

Solution Implementation

NuSummit Cybersecurity addressed these challenges by delivering AI/LLM Security Penetration Testing for the client’s AI-powered application, leveraging both commercial and open-source large language models, including GPT-4.0, GPT-4o mini, Llama, Phi 3.5, and Qwen.

The engagement focused on identifying security weaknesses, misuse scenarios, and design flaws specific to LLM-driven applications. Testing was mapped to the OWASP LLM Top 10, ensuring coverage of the most critical AI security risks, such as:

1. Prompt injection and jailbreak techniques.
2. Insecure output handling.
3. Data leakage and sensitive information exposure.

Real-world attack techniques, including direct and indirect prompt injection and multi- turn jailbreak scenarios, were simulated to reflect actual adversarial behavior. Critical vulnerabilities were reported immediately to enable timely remediation and minimize business impact. All findings were technically validated, reproducible, and mapped to clear root causes within the AI application flow.

Key Capabilities Delivered

The engagement delivered targeted AI/LLM security capabilities designed to identify, validate, and reduce risks unique to large language model–driven applications, including:

• AI/LLM-focused security penetration testing.
• OWASP LLM Top 10–aligned risk coverage.
• Simulation of real-world AI attack techniques.
• Identification of prompt injection, jailbreak, and hallucination risks.
• Verification of vulnerabilities with reproducible evidence.
• Implementation-ready remediation guidance.
• Retesting and validation of applied security controls.

Business Impact

The assessment resulted in measurable improvements to the client’s AI security posture, operational confidence, and readiness to deploy AI applications securely at scale. These included:

• Clear and repeatable approach to identifying AI security vulnerabilities.
• Early discovery of high-risk issues reduced misuse and unnecessary AI usage.
• Increased confidence among teams deploying AI applications.
• Improved alignment between security, engineering, and leadership.
• Stronger audit readiness and compliance visibility through documented findings.
• Lower operational and token consumption costs.

These outcomes improved operational efficiency, reduced security risk, and strengthened leadership confidence in the organization’s security posture.

Differentiators

NuSummit Cybersecurity’s approach combined extensive AI security expertise, realistic attack simulation, and close collaboration to deliver practical, high-impact outcomes. These include:

• AI-Specific Security Expertise: Deep understanding of vulnerabilities unique to LLM systems beyond traditional application
  security testing.
• Realistic Attack Simulation: Use of multi-turn prompt chaining, role escalation, and indirect prompt injection techniques.
• Actionable Remediation: Clear, implementation- ready guidance including prompt segregation, guardrails, and output controls.
  
• Speed and Practicality: Rapid assessment during development and UAT phases.
• Collaborative Delivery: Deep collaboration with engineering and product teams to validate fixes and retest mitigations.
• Knowledge Transfer: Enable teams to apply AI security best practices to future initiatives.
  

Conclusion

By conducting a focused AI/LLM security penetration testing engagement, NuSummit Cybersecurity helped a global technology enterprise identify and mitigate critical risks associated with AI-driven applications. The engagement improved security assurance, reduced misuse and cost exposure, strengthened compliance readiness, and empowered teams to adopt AI technologies with greater confidence, laying the foundation for secure and scalable AI innovation.