AI Agent & Chatbot Security Testing
Identify vulnerabilities in your Large Language Model (LLM) implementations, AI agents, and intelligent chatbots. Protect your applications against jailbreaks, prompt injection attacks, and unauthorized data leakage before deployment.
Why Secure AI Agents & LLMs?
Integrating LLMs and autonomous AI agents introduces non-deterministic attack vectors that traditional web security tools fail to detect.
Prevent Jailbreaking & Bypasses
Adversaries manipulate prompt contexts to bypass safety alignment rules, forcing the model to issue unsafe or unauthorized outputs.
Protect RAG & Internal Data
Prevent retrieval-augmented generation (RAG) vector pipelines from leaking confidential corporate documents, system prompts, or API keys.
Stop Agent Execution Exploits
Secure autonomous AI agents from indirect prompt injections that trick the system into executing unauthorized commands, function calls, or file actions.
Vulnerabilities We Uncover
Fully aligned with the OWASP Top 10 for LLM Applications and AI Security standards.
Our AI Security Testing Methodology
A structured framework combining automated adversarial probing with custom manual red teaming.
Threat Modeling & Architecture Mapping
We analyze system prompts, AI agent integrations, RAG databases, function-calling tools, and input/output guardrails to build a targeted attack plan.
Adversarial Prompting & Jailbreaking
Engineers craft multi-turn, multi-language, and obfuscated payload strategies to test safety alignment, system prompt resilience, and content filtering logic.
Agent Tool & RAG Exploitation
We evaluate whether malicious prompts embedded within external documents or web content (Indirect Prompt Injection) can trick AI agents into executing unauthorized actions.
Reporting & Hardening Guidance
You receive detailed proof-of-concept write-ups, vulnerability severity scores, and actionable hardening recommendations for system prompts, guardrails, and pipelines.