Skill profile · Updated 2026-05-03
Prompt Injection Defense
Stop attackers from hijacking your LLM features through user inputs and retrieved content.
What is it?
Prompt injection is the OWASP-#1 risk for LLM applications: an attacker plants instructions in user input or in content the model later retrieves, causing the model to ignore its system prompt and follow attacker intent instead. Indirect injection — instructions hidden in a webpage the agent fetches, an email it summarizes, or a doc it cites — is the meaner variant because it bypasses the user trust model entirely. The defense is layered: input boundary checks, output schema validation, structured tool-call interfaces (not free-form actions), least-privilege tool scopes, isolated execution contexts for retrieved content, and continuous red-teaming against your own surface.
Source: OWASP — Top 10 for Large Language Model Applications
Who needs it?
Roles where this skill is explicitly weighted by hiring managers.
AI Security Specialist
You own the threat model, the red-team cadence, and the boundary policies. This is your primary domain.
Applied GenAI Engineer
Every user-facing LLM feature you ship is a potential injection surface. Defensive posture is part of the design, not an afterthought.
AI Solutions Architect
Enterprise customers will ask about injection risk. Concrete mitigation patterns and a written threat-model are part of every scoping call.
MLOps Engineer
Tool-call audit logs, sandboxing, and rollback for compromised prompts sit in your operational layer.
Time to proficiency
Realistic benchmarks assuming 8–10 focused hours per week. Adjust for your starting point.
You can explain direct vs indirect injection with examples. You know that 'just instruct the model not to follow malicious instructions' does not work.
You implement input-side checks (PII redaction, suspicious-pattern detection), output-side schema validation, and you separate user content from system instructions cleanly. You have a small adversarial test set.
You design with structured tool interfaces (JSON schemas, not natural-language actions), least-privilege tool scopes, sandboxed execution for retrieved content, and continuous red-team eval that runs in CI.
You operate red-team-as-a-discipline: rotating attack libraries, post-incident review, threat modeling for new surfaces, and organizational policies that engineering teams hold themselves to. You contribute mitigations to the OWASP LLM top-10 community.
Prove it with a cert
Complete the Prompt Engineering, then take the AI Security Fundamentals practice exam on CertQuests to validate your knowledge and add a shareable credential to your profile.
Go to CertQuests