LLM & GenAI
Security Testing
Comprehensive security testing for large language models and generative AI systems. Identify vulnerabilities before they're exploited.
Why Choose Us
New models, new attack surface.
As you adopt LLMs and generative AI, entirely new risks emerge — prompt injection, jailbreaks, data leakage. We red-team your AI before someone else does.
Testing across the AI stack.
Prompt Injection
Test for direct and indirect prompt injection attacks that can manipulate model behavior.
Jailbreak Testing
Evaluate resistance to jailbreaking techniques that bypass safety guardrails.
RAG Security
Assess retrieval-augmented generation systems for data poisoning and leakage risks.
AI Red Teaming
Comprehensive adversarial testing to uncover security and safety vulnerabilities.
Output Filtering
Test content moderation and output filtering mechanisms for bypass vulnerabilities.
Data Protection
Identify risks of training data extraction and sensitive information disclosure.
Why LLM Security Testing?
Prompt Injection Testing
Identify and remediate direct and indirect prompt injection vulnerabilities in your LLM applications.
Jailbreak Resistance
Evaluate your model's resistance to jailbreaking attempts and guardrail bypasses.
RAG Security Assessment
Test retrieval-augmented generation systems for data leakage and manipulation vulnerabilities.
AI Red Teaming
Adversarial testing of AI systems to uncover security and safety issues before deployment.
Output Filtering Review
Assess content filtering and safety mechanisms for effectiveness and bypass resistance.
Data Leakage Prevention
Identify risks of training data extraction and sensitive information disclosure.
Our Testing Process
A systematic approach to securing your LLM and generative AI systems
Scope & Objectives
Define testing targets, LLM applications, and specific security concerns.
Threat Modeling
Identify attack vectors relevant to your LLM implementation and use cases.
Prompt Injection Testing
Systematic testing for direct and indirect prompt injection vulnerabilities.
Adversarial Testing
Red team exercises to test guardrails, safety mechanisms, and edge cases.
Data Security Analysis
Assess risks of data leakage, training data extraction, and PII exposure.
Remediation Guidance
Deliver findings with specific recommendations for hardening your LLM systems.
What you get.
Vulnerability Report
Detailed findings of all discovered LLM vulnerabilities with severity ratings and evidence.
Attack Scenarios
Documentation of successful attack chains and exploitation techniques used during testing.
Remediation Guide
Specific recommendations for hardening your LLM systems against identified threats.
Security Roadmap
Strategic plan for ongoing LLM security improvement and monitoring.
Ready to Secure Your LLM?
Contact us for comprehensive security testing of your large language models and generative AI applications.
Security is a Virtue.