LLM Application Security Checklist
28 security checks for LLM-powered applications covering prompt injection, data leakage, output validation, RAG poisoning, API security, and agent framework hardening.
🚨 LLM Application Threat Landscape
LLM-powered applications introduce attack surfaces that traditional security testing misses entirely:
• OWASP Top 10 for LLMs prompt injection is #1 risk (OWASP 2025)
• 77% of companies have no LLM security policy (Gartner)
• 90% of jailbreaks succeed on first attempt against unprotected apps (NVIDIA)
• $4.88M average cost of an AI-related data breach (IBM 2025)
• 56% of LLM apps leak sensitive data through outputs (Lakera)
• 3.2x more attack surface in RAG applications vs traditional LLM apps
CATEGORIES
Direct Prompt Injection Defense
CriticalUser inputs cannot override system prompts or modify LLM behavior directives
Indirect Prompt Injection Resistance
CriticalExternal data sources (web pages, documents, emails) cannot inject instructions into the LLM
Jailbreak Resistance
CriticalApplication resists role-play, encoding, and multi-turn jailbreak techniques
System Prompt Protection
HighSystem prompts cannot be extracted or leaked through conversational techniques
PII Detection & Redaction
CriticalPersonally identifiable information is detected and redacted before reaching the LLM
Training Data Extraction Prevention
CriticalModel cannot be prompted to reveal memorized training data or fine-tuning examples
Conversation History Isolation
HighUser sessions are isolated — no cross-contamination of conversation data between users
Log Sanitization
HighApplication logs do not contain sensitive prompts, user data, or model responses in plaintext
Hallucination Detection
HighCritical outputs are validated against ground truth — hallucinated facts flagged before reaching users
Code Execution Output Sanitization
CriticalLLM-generated code is sandboxed and validated before execution — no arbitrary code runs unsupervised
Structured Output Validation
HighJSON, SQL, and other structured outputs are validated against schemas before downstream use
Toxic Content Filtering
HighModel outputs screened for harmful, biased, or inappropriate content before delivery
RAG Document Poisoning Prevention
CriticalDocuments ingested into the knowledge base are scanned for embedded injection payloads
Retrieval Access Control
HighRAG retrieval respects document-level permissions — users only see content they're authorized to access
Embedding Inversion Protection
HighVector embeddings cannot be reverse-engineered to reconstruct original document content
Source Attribution Integrity
MediumRetrieved sources are accurately cited — no fabricated or manipulated source references
Rate Limiting & Abuse Prevention
HighAPI endpoints enforce per-user rate limits to prevent abuse, cost attacks, and resource exhaustion
Input Length & Complexity Limits
HighMaximum input token counts enforced — no context window exhaustion attacks
Model API Key Security
CriticalLLM provider API keys are never exposed client-side — all calls proxied through backend
Streaming Response Security
MediumSSE/WebSocket streaming responses validated incrementally — no mid-stream injection
Tool Call Authorization
CriticalLLM tool/function calls validated against permission boundaries before execution
Tool Output Sanitization
CriticalData returned from tool calls sanitized before re-entering the LLM context
Autonomous Action Limits
HighAgent loop iterations capped — no unbounded recursive tool calling or infinite loops
Multi-Agent Trust Boundaries
HighIn multi-agent systems, agents cannot escalate privileges or manipulate other agents' instructions
Role-Based Output Filtering
HighModel responses filtered based on user role — admins see full data, regular users see redacted versions
Session Token Security
HighConversation session tokens are cryptographically secure and expire appropriately
Prompt Injection Detection Logging
HighSuspected injection attempts are logged and trigger security alerts
Cost & Usage Anomaly Detection
MediumUnusual spikes in token usage, API calls, or compute costs trigger automated alerts
Need an LLM Application Security Audit?
Zealynx tests LLM applications against real-world attack patterns — prompt injection, data leakage, jailbreaks, and RAG poisoning. We find what automated scanners miss.
