An internal user enters a client credit card number into an internal generative machine learning...

Basic Concept: Prompt injection occurs when malicious content embedded in user input manipulates an LLM ' s behavior, causing it to leak sensitive data, bypass restrictions, or execute unintended actions. Preventing such attacks requires mechanisms that inspect and filter content at the prompt level. CompTIA SecAI+ covers LLM-specific security controls extensively.

Why A is Correct: Guardrails are purpose-built controls that inspect, filter, and constrain both input prompts and output responses in LLM systems. They can detect sensitive data patterns such as credit card numbers, block prompt injection payloads, enforce content policies, and prevent the model from processing or outputting restricted information. Guardrails are the primary LLM-native defense against prompt injection as cited in the CompTIA SecAI+ Study Guide.

Why B is Wrong: Antivirus software detects known malware signatures in files and executables. It does not inspect or understand the semantic content of LLM prompts and cannot detect or block prompt injection attacks.

Why C is Wrong: A WAF operates at the HTTP layer inspecting web requests and responses against rule sets. While it can block some patterns, it lacks the contextual intelligence to understand LLM prompt semantics and cannot prevent sophisticated injection attacks.

Why D is Wrong: Role-based access control manages who can access which resources. It controls authorization but does not inspect the content of prompts to prevent injection attacks once a user has legitimate access.