Why AI Companies Trust Vaeto LLM Pentesting
Prompt Injection & Jailbreaking
Executing multi-turn jailbreaks, payload splitting, and virtual persona prompts to bypass model safety filters.
RAG & Vector Database Security
Probing vector search retrieval engines to verify metadata filters and prevent cross-tenant document leaks.
AI Agent Function Call Auditing
Auditing LangChain and AutoGen agents to prevent unauthorized SQL execution, email sending, or shell escapes.
30-Day Re-Testing SLA
Deploy new system prompts and guardrails with confidence. We re-test all remediated AI vulnerabilities at zero extra charge.
OWASP Top 10 for LLM Applications Matrix
Direct & Indirect Prompt Injection
Crafting adversarial prompts to bypass system guardrails, extract hidden instructions, or trick LLM agents into executing unsafe actions.
Insecure Output Handling & Remote Code Execution
Unsanitized LLM responses fed directly into backend shells, SQL queries, or WebViews leading to RCE or XSS.
Training Data Poisoning & Model Manipulation
Manipulating fine-tuning datasets or RAG vector databases to introduce malicious backdoors or biased model responses.
Model Denial of Service (Excessive Token Consumption)
Crafting heavy recursive prompts that exhaust LLM API rate limits, GPU compute resources, and cloud budgets.
Supply Chain Vulnerabilities & Poisoned HuggingFace Models
Using compromised open-source model weights, vulnerable PyTorch/LangChain packages, or malicious pickle files.
Sensitive Information Disclosure & System Prompt Leaks
Extracting system prompts, PII data embedded in training sets, or proprietary RAG vector embeddings.
Insecure Plugin & AI Agent Function Calling
AI agents with autonomous tool execution (e.g. database writing, email sending) abused via untrusted user inputs.
Excessive Agency & Unrestricted Autonomous Execution
Granting AI models unnecessary permissions (file system access, shell execution) without human-in-the-loop controls.
Overreliance & Unchecked Hallucination Abuse
Tricking AI applications into providing dangerous code snippets, flawed legal advice, or unauthorized credentials.
Model Theft & Intellectual Property Extraction
Extracting proprietary model weights or system prompts via high-volume API query inversion attacks.