Secure Your AI with Comprehensive LLM Pentesting

Identify and mitigate vulnerabilities in Large Language Model applications before they compromise data, compliance, or trust.

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What is LLM Pentesting?

LLM Pentesting involves simulating real-world attacks on applications and systems that integrate Large Language Models. As AI becomes increasingly adopted in chatbots, virtual assistants, and data analysis tools, it introduces novel risks such as prompt injection, data leakage, and unauthorized model manipulation. Our ethical hackers and AI security experts help you identify and remediate these AI-specific vulnerabilities, ensuring your models operate safely and reliably.

By testing the entire ecosystem from model APIs and access controls to underlying training data and prompt handling LLM Pentesting uncovers critical gaps that attackers could exploit, giving you a roadmap to harden your AI infrastructure.

Why Invest in LLM Pentesting?

  • Specialized AI Security

    Address unique AI-related threats like prompt injection and data extraction.

  • Protect Sensitive Data

    Prevent unauthorized model access and leakage of private or proprietary information.

  • Regulatory Readiness

    Stay compliant with data privacy standards when using AI solutions.

  • Risk Prioritization

    Focus remediation efforts on the most impactful AI vulnerabilities first.

  • Continuous Assurance

    Regularly evaluate AI deployments for new model versions, training data changes, and evolving threats.

  • Expert Guidance

    Leverage our cybersecurity and AI teams for best practices in secure model operation.

How Our LLM Pentesting Works & Key Features

Discover our testing methodology and explore the standout features that ensure robust AI security for your organization.

Testing Methodology

AI Asset Inventory

Identify all LLM-based apps, APIs, and data pipelines in scope to define your AI ecosystem.

Threat Modeling

Map potential attack paths, including data poisoning, model evasion, and adversarial prompts.

Vulnerability Assessment

Use automated tools and manual techniques to expose weaknesses in prompt handling, API authentication, and more.

Exploit Simulation

Attempt to exploit discovered flaws using realistic adversarial tactics and queries.

Remediation Guidance

Provide clear, actionable steps to patch vulnerabilities and harden AI deployments.

Ongoing Monitoring

Reassess after fixes, model retraining, or new features to maintain consistent AI security posture.

Key Features of Our Service

AI-Specific Risk Analysis

Review model training data, inference endpoints, and prompt logs for hidden threats.

Integration with CI/CD

Embed LLM security checks into your DevOps pipelines, ensuring continuous AI protection.

Adversarial Testing

Test the model’s resilience against malicious or out-of-distribution prompts and inputs.

Privacy & Compliance

Ensure data masking and encryption, and maintain compliance with GDPR, HIPAA, etc.

Multi-Cloud Support

Assess AI workloads across AWS, Azure, GCP, or on-prem solutions for uniform security.

Reporting & Dashboards

Track vulnerabilities, severity levels, and remediation progress in a unified view.

Our Clients

Our Clients Feedback

Mark Jefferson

“Impressed by their rapid response to threats, thorough vulnerability testing, and clear communication. Our network feels safer than ever.”

Carlos Ramirez

“Their team strengthened our firewalls, reduced downtime, and offered comprehensive training. Perfect partner in security.”

Angela Patel

“Expert guidance, robust solutions, and proactive monitoring our data is secure and compliance assured.”

Elena Smith

“Timely alerts, transparent reporting, and professional service give us full confidence. An essential cyber defense ally.”

LLM Pentesting vs. Other Security Testing Methods

See how LLM-specific testing compares to traditional approaches like network, web app, and code-level security methods.

Criteria LLM Pentesting Network Pentesting Web App Pentesting
Scope AI workflows, model APIs, prompt handling Internal/external network infrastructure Web applications, front-end & back-end
Focus Prompt injection, data leakage, AI tampering Ports, protocols, firewall configurations OWASP Top 10 issues, logic flaws
Techniques AI exploitation, adversarial queries, model poisoning attempts Credential brute force, VLAN hopping, port scanning SQL injection, XSS, CSRF, session hijacking
Outcomes Secure AI models, robust prompt protections, minimized data leaks Hardened network perimeter, improved segmentation Safer web applications, reduced user data exposure

Frequently Asked Questions

We are the agency that always prioritizes your questions, allowing you to easily ask questions from a variety of options.