AI & LLM Security Testing
Security assessments purpose-built for AI systems and large language models. As AI becomes critical infrastructure, adversarial testing requires specialized methodology and tooling that traditional security approaches simply don't provide.
Why AI Systems Need Different Security Testing
Traditional security testing assumes deterministic software — given input X, the system always produces output Y. AI systems break this assumption entirely. Large language models respond non-deterministically, their behavior is shaped by training data and fine-tuning, and their attack surface includes natural language itself.
Prompt injection attacks, jailbreaks, and training data extraction have no direct equivalent in classical security. An OWASP Top 10 checklist won't catch them. SAST tools can't scan for them. They require an attacker who understands both traditional security and the specific mechanics of how language models process instructions.
Organizations deploying AI-powered applications — chatbots, RAG systems, AI assistants, LLM-integrated APIs — are introducing a new class of risk that most security teams are unprepared to evaluate. A compromised AI system can leak sensitive data, execute unauthorized actions, or be weaponized against users.
I build AI security tooling as part of my research work, including purpose-built testing frameworks for evaluating LLM security posture. This isn't just a new service category — it's an area I actively research and develop methodology for.
AI Security Testing Services
Assessments cover the OWASP LLM Top 10 and beyond, tailored to your specific AI architecture.
Prompt Injection Testing
Systematic testing for direct and indirect prompt injection vulnerabilities — where attacker-controlled inputs override system prompts, bypass safety filters, or manipulate model behavior in unintended ways.
Jailbreak Analysis
Evaluation of model safety controls using adversarial prompt techniques, multi-turn attack chains, and role-play exploitation to assess the robustness of content filtering and guardrails.
Training Data Exposure
Testing for training data memorization and extraction — identifying whether models inadvertently regurgitate sensitive training data including PII, credentials, or confidential information.
Adversarial Input Testing
Evaluation of model robustness against adversarial inputs, evasion techniques, and manipulation strategies designed to alter model outputs in ways that circumvent intended behavior.
Frequently Asked Questions
What is AI security testing?
Evaluation of AI systems and LLM-powered applications for prompt injection, jailbreaks, training data leakage, model inversion, and adversarial input vulnerabilities.
What is prompt injection?
An attack where malicious instructions injected into LLM input override its system prompt, bypass safety controls, or manipulate behavior — analogous to SQL injection but targeting language models.
Why do AI systems need specialized security testing?
Traditional security testing was designed for deterministic software. AI systems are non-deterministic, their attack surface includes natural language, and many vulnerabilities have no classical equivalent. Purpose-built methodology is essential.
Do you test AI-powered applications (not just standalone models)?
Yes. Chatbots, AI assistants, RAG applications, and LLM-integrated APIs inherit both traditional web vulnerabilities and AI-specific attack surfaces — making combined assessments particularly valuable.
Related Services
Building with AI? Let's assess it.
Describe your AI system or application. I'll scope a targeted assessment that addresses your specific AI attack surface.
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