Exploiting a Generative AI Chatbot – Prompt Injection to Remote Code Execution (RCE)

Security teams are often sidelined in generative AI projects. Early AI security testing — covering evasion, poisoning, and extraction — protects LLM investments without slowing innovation.

YesWeHack examines three key AI pentesting concerns - scope control, harm avoidance, and traceability - and shows how its Agentic Pentest harness addresses them for safe autonomous testing.

Using HacktronAI's OpenAI Discourse hack disclosure, this article covers sandboxing, runtime protection, and AI-assisted defense to shrink the blast radius of RCE across interconnected apps.

This research provides real-world threat telemetry on how attackers target modern AI deployments and MCP interfaces.

Provides verifiable, transparent evidence of advanced LLMs performing complex, multi-step binary and browser exploitation autonomously under strict ASLR and memory randomization defenses.

Offers security researchers and penetration testers an out-of-the-box, comprehensive environment to practice prompt injection, OWASP LLM Top 10 risks, and agent tool-abuse exploitation against real models.