LLM Security & Prompt Engineering

• Design system prompts and context boundaries that resist injection
• Implement input and output validation for LLM features
• Constrain tool calling and agent permissions
• Build a repeatable adversarial test suite
• Monitor and log LLM interactions safely

1. 1

   LLM Application Anatomy

   Every place untrusted text enters.

   • Prompt layers
   • Context assembly
   • Tool and function calling

   Open module
2. 2

   Prompt Engineering for Robustness

   Clear instructions are a control.

   • Instruction design
   • Delimiting untrusted content
   • Structured output
   • Refusal behaviour

   Open module
3. 3

   Injection Attacks in Depth

   Direct, indirect and multi-turn.

   • Direct injection
   • Indirect injection
   • Multi-turn manipulation
   • Prompt leakage

   Open module
4. 4

   Guardrails & Validation

   Never trust model output blindly.

   • Input filtering
   • Output schema validation
   • Content classifiers
   • Sandboxed execution

   Open module
5. 5

   Tooling, Agents & Least Privilege

   Limit what a compromised prompt can do.

   • Tool permission scoping
   • Approval gates
   • Rate and cost limits

   Open module
6. 6

   Testing & Monitoring

   Keep it secure after launch.

   • Adversarial test suites
   • Regression testing
   • Interaction logging and privacy
   • Abuse detection

   Open module

PrimeSec does not hand you a pre-built machine. You get professional lab guides and build the environment yourself — that is where the skill comes from.

• Deploy a vulnerable LLM chat application
• Leak the system prompt, then prevent it
• Execute an indirect injection via a retrieved document
• Add output schema validation and break it
• Scope tool permissions and verify enforcement
• Build an automated adversarial test suite

• A hardened LLM application with documented controls
• An adversarial test suite with results before and after hardening
• An LLM security design guide for engineering teams