Every Company Is Now Using AI in One of 3 Key Ways
- Assistant AI – Tools like ChatGPT and Copilot help with writing, coding, customer support, and more. Nearly 65% of companies used generative AI in 2024, and 57% of finance teams employ AI for operations, with another 21% planning implementation.
- Agent AI – Autonomous systems that execute tasks end‑to‑end. Currently, 29% of organizations use agentic AI, and 44% plan deployments within a year. However, only ~2% have scaled deployments fully, even though 93% of leaders see it as critical.
- Tool AI – Purpose-built systems for code analysis, threat detection, data pipelines, etc. Across industries, ~69% of organizations use AI analytics, 47% for NLP, and 46% for LLMs.
AI adoption is expanding rapidly, with nearly half of C-suite and employees already using it heavily. Yet, security is lagging: 36% of firms admit GenAI is outpacing their security capabilities
Why AI/LLM Pentesting is Critical?
AI systems, especially generative and agentic ones, can unintentionally leak sensitive data, execute unauthorized actions, or be manipulated via prompt injection, which cyber standards like NIST and OWASP classify as critical threats . Studies show 32% of LLM pentest vulnerabilities are serious, and only 21% get fixed
Hacker Simulation's
LLM & Gen‑AI Penetration Testing
We cover top 10 OWASP risks for LLM and generative AI, including prompt injection, data leakage, malicious output, model poisoning, and more.
Methodology
Infrastructure Testing
Evaluate the application’s hosting environment: API endpoints, authentication, network exposure, cloud misconfigurations, and container security.
AI vs AI
We deploy autonomous AI agents to attack your models with adversarial prompts, jailbreak attempts, misuse chains, and prompt injections.
Human
Our expert human pentesters step in, identifying logic flaws, ethical issues, hidden failure modes, and security blind spots that current AI models miss.
OWASP TOP 10 RISK FOR LLMs & GenAI Apps
- LLM01:2025 Prompt Injection
- LLM02:2025 Sensitive Information Disclosure
- LLM03:2025 Supply Chain
- LLM04:2025 Data and Model Poisoning
- LLM05:2025 Improper Output Handling
- LLM06:2025 Excessive Agency
- LLM07:2025 System Prompt Leakage
- LLM08:2025 Vector and Embedding Weaknesses
- LLM09:2025 Misinformation
- LLM10:2025 Unbounded Consumption
