LLM Security
Security practices, threat modeling, and controls for applications built on Large Language Models.
Definition
What is LLM Security?
LLM Security is the discipline of identifying, assessing, and mitigating security risks specific to applications built on Large Language Models (LLMs) such as GPT-4, Claude, Gemini, and open-source models. The OWASP LLM Top 10 defines the primary risk categories: Prompt Injection, Insecure Output Handling, Training Data Poisoning, Model Denial of Service, Supply Chain Vulnerabilities, Sensitive Information Disclosure, Insecure Plugin Design, Excessive Agency, Overreliance, and Model Theft. LLM security requires a new threat modeling approach because LLMs process natural language instructions — creating attack surfaces that don't exist in traditional software.
Why It Matters
AI-powered applications are being deployed at unprecedented velocity, often without security review processes appropriate for the new risk surface they introduce. LLMs can be manipulated to leak system prompts, bypass access controls, generate harmful content, and be used as pivot points to attack backend systems. For enterprises deploying internal AI assistants, customer-facing chatbots, and AI-powered workflows, LLM security is a board-level risk that most organizations are not yet equipped to manage.
How It Works
LLM security assessment follows the OWASP LLM Top 10 framework: testing for prompt injection vulnerabilities (both direct and indirect), evaluating output handling and injection into downstream systems, assessing data governance for training and retrieval, testing access controls on LLM-accessible tools and functions, and reviewing architecture for excessive agency (LLM-initiated actions with real-world consequences).
Our Approach
Paxanimi's Approach to LLM Security
Paxanimi's AI Security practice conducts OWASP LLM Top 10 assessments for enterprise AI deployments, designs security architectures for RAG systems and AI agents, implements prompt injection defenses, and builds data governance frameworks for AI systems handling sensitive data. We also conduct AI red teaming engagements to identify novel attack paths in AI-powered applications before they reach production.
Quick Reference
- Category
- AI Security
- Related Services
- Cybersecurity ServicesSoftware Development
Related Terms
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