AI Pentesting
LLM-specific security testing for AI applications.
Vezraa provides autonomous AI pentesting that actively probes, exploits, and validates security weaknesses. Our LLM testing covers the unique attack surface of AI-powered applications.
OWASP LLM Top 10 coverage
- LLM01 — Prompt injection: direct and indirect injection through user input and retrieved context
- LLM02 — Sensitive information disclosure: extracting system prompts, internal data, and credentials
- LLM03 — Supply chain: compromised models, poisoned training data, vulnerable dependencies
- LLM04 — Data and model poisoning: training data manipulation and model drift detection
- LLM05 — Excessive agency: tool and plugin misuse by the LLM beyond intended scope
Prompt injection testing
- Direct prompt injection — crafting inputs that override system instructions
- Indirect injection — injecting malicious content into retrieved context or documents
- Role-playing attacks — tricking the model into adopting an unrestricted persona
- Multi-language injection — switching languages mid-conversation to bypass guardrails
- Encoding-based bypass — base64, Unicode obfuscation, and other encoding tricks
- Context window overflow — filling context to force the model into vulnerable behavior
Data extraction and leakage testing
- System prompt extraction — attempting to reveal the underlying prompt and instructions
- Training data extraction — probing for memorized PII or sensitive training examples
- Knowledge base leakage — extracting content from RAG data sources and vector databases
- Conversation history disclosure — testing for leakage across different user sessions
- Model fingerprinting — identifying the underlying model and version for targeted attacks
Model denial of service and abuse
- Context window exhaustion — extremely long inputs that consume all available tokens
- Recursive generation loops — crafting prompts that cause infinite or extremely long responses
- Resource exhaustion — high-frequency API calls that drive up inference costs
- Output token bombing — forcing the model to generate massive output payloads
- Tool call abuse — crafting inputs that trigger expensive or infinite tool chains
LLM security best practices
- Implement input sanitization and output filtering specifically for LLM interactions
- Use least-privilege tool access — limit what the LLM can read, write, and execute
- Separate system prompts from user input with strong boundary enforcement
- Monitor for prompt injection attempts in production with real-time detection
- Regularly audit your LLM application against the OWASP LLM Top 10
- Run LLM-specific pentests as part of your pre-release checklist for AI features
Run a free security scan first — LLM pentesting unlocks from your report.
Start Scanning →