Beyond the Prompt: Navigating the Threats to AI Agents

AI agents, programs designed to autonomously collect data and take actions toward specific objectives using LLMs and external tools, are rapidly becoming widespread in applications from customer service to finance. While built on LLMs, they introduce new risks by integrating tools like APIs and databases, significantly expanding their attack surface to include classic software vulnerabilities like SQL injection, remote code execution, and broken access control, in addition to inherent LLM risks like prompt injection. Our sources demonstrate that these vulnerabilities are largely framework-agnostic, stemming from insecure designs and misconfigurations rather than flaws in frameworks like CrewAI or AutoGen. Given the autonomous nature and expanded capabilities of agents, the potential impact of compromises escalates from data leakage to infrastructure takeover. This episode dives into the complex threats targeting AI agents and highlights why a layered, defense-in-depth strategy is essential, combining safeguards like Prompt Hardening, Content Filtering, Tool Input Sanitization, Tool Vulnerability Scanning, and Code Executor Sandboxing, because no single mitigation is sufficient to address the diverse attack vectors.