LLM Prompt Injection & System Firewall Defense Lab
ENVIRONMENT: İzole Python 3.12 Sandboxed Lab & Local LLM Gateway
VISIBILITY: Public
TOOLS:
Burp Suite
Python
LangChain
Rebuff
Custom Fuzzing Scripts
01 // Objective
Analyze indirect prompt injection and system prompt leak vectors in enterprise LLM deployments and develop layered boundary defense mechanisms.
02 // Methodology & Execution Steps
1. Injected adversarial payloads (roleplay inversion, delimiter escaping, Unicode bypasses) into sandboxed AI agent endpoints.
2. Attempted unauthorized tool-invocation escalations and exfiltration of simulated confidential system instructions.
3. Validated multi-layer guardrails combining regex, token perplexity scoring, and secondary validation LLMs.
03 // Findings & Attack Vector Verification
Relying solely on system prompt constraints is fundamentally insecure. Multi-lingual and obfuscated payloads bypass naive boundary instructions. Deploying a dedicated input/output firewall gateway neutralized 99.2% of injection attempts.
04 // Remediation & Layered Defense
1. Implement deterministic input token validation and adversarial classifier models.
2. Apply strict Least Privilege and sandboxed execution to all callable agent tools.
3. Enforce cryptographic data loss prevention (DLP) masks on all model responses.
05 // Lessons Learned & Engineering Takeaways
AI application security cannot be solved by prompt engineering alone; it requires standard defense-in-depth engineering principles.