Question 1

What is LLM security and why does it matter now?

Accepted Answer

LLM security encompasses the vulnerabilities, attack techniques, and defensive controls specific to Large Language Model deployments. As organizations integrate LLMs into customer-facing products, internal workflows, and agentic systems, new attack surfaces emerge that traditional application security doesn't cover — prompt injection (where malicious input manipulates model behavior), insecure output handling (where LLM responses are unsafely processed by downstream systems), training data exposure, and supply chain risks from third-party model providers. OWASP formalized these risks in the OWASP LLM Top 10 in 2023, and the attack landscape has continued to evolve rapidly.

Question 2

What is prompt injection and how is it different from SQL injection?

Accepted Answer

Prompt injection is an attack where an adversary injects instructions into an LLM's input — either directly (by crafting malicious user inputs) or indirectly (by embedding instructions in external content the LLM processes, such as web pages, documents, or database records). Like SQL injection, it exploits a failure to separate instructions from data — but in LLMs, the 'parser' is the model itself and the injection target is natural language instructions rather than database queries. In agentic systems where LLMs take real-world actions (sending emails, executing code, browsing the web), successful prompt injection can have significant real-world consequences beyond data disclosure.

Question 3

How is AI red teaming different from traditional red teaming?

Accepted Answer

Traditional red teaming focuses on finding and exploiting vulnerabilities in networks, systems, and applications using known attack techniques. AI red teaming requires a different methodology: adversarial ML knowledge to identify model-specific vulnerabilities, natural language attack crafting for prompt injection and jailbreaking, understanding of LLM architecture and training to identify data leakage risks, and MITRE ATLAS techniques designed specifically for AI/ML systems. Paxanimi's AI red team combines traditional security expertise with AI-specific adversarial testing methodology — not just traditional red teamers applying generic techniques to AI systems.

Question 4

What is the OWASP LLM Top 10?

Accepted Answer

The OWASP LLM Top 10 is a framework published by the Open Web Application Security Project (OWASP) that documents the most critical security risks in LLM applications. The Top 10 includes: 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. Our LLM Security Assessment maps findings to OWASP LLM Top 10 categories, giving your team a standardized framework for risk prioritization and remediation tracking.

Question 5

What are the security risks of Retrieval-Augmented Generation (RAG)?

Accepted Answer

RAG architectures introduce a critical indirect prompt injection attack surface: if external documents, web pages, or database records fetched by the retrieval system contain attacker-controlled content, that content can inject instructions into the LLM's context and manipulate its behavior — without any direct user interaction. Additional RAG security risks include retrieval of sensitive documents from improperly access-controlled knowledge bases, data poisoning of the vector database, and metadata leakage through retrieved content. Our AI security assessments specifically test RAG pipelines for indirect injection, access control enforcement, and data integrity.

Question 6

What is AI governance and what frameworks should we align to?

Accepted Answer

AI governance is the organizational structure, policies, processes, and technical controls that ensure AI systems are developed, deployed, and operated responsibly — managing risks to safety, fairness, transparency, and security. The primary frameworks are NIST AI Risk Management Framework (AI RMF), which provides a voluntary framework for managing AI risks across the AI lifecycle; the EU AI Act, which mandates governance requirements for AI systems deployed in the EU market based on risk classification; and MITRE ATLAS, which provides adversarial ML threat knowledge. Our AI governance engagements establish risk registers, governance policies, and framework alignment appropriate to your AI deployment profile and regulatory exposure.

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