When AI Moves from “Speaking” to “Acting”: New Security Challenges
The global cybersecurity community has taken a crucial step this week. As reported by PR Newswire, the OWASP GenAI Security Project has officially released the “Top 10 for Agentic Applications.” This document is the result of over a year of research with collaboration from bodies such as NIST and the European Commission.
The distinction is vital: while the LLM Top 10 focused on text response manipulation, this new list addresses the risks of Autonomous Agents (Agentic AI). These systems do not just generate content; they make decisions, use tools, and execute actions within enterprise infrastructure. Risks such as “Agent Behavior Hijacking” or “Tool Misuse” transform AI into an active attack vector within the corporate perimeter.
Key Threats Highlighted by OWASP
| Threat | Description | Infrastructure Impact |
|---|---|---|
| Agent Behavior Hijacking | Attackers alter the agent’s goal or logic. | Execution of unauthorized processes on servers. |
| Tool Misuse | The agent uses legitimate tools for malicious purposes. | Improper access to internal APIs or databases. |
| Identity & Privilege Abuse | The agent assumes excessive permissions for tasks. | Privilege escalation and lateral movement in Cloud. |
The TeraLevel Perspective: Securing the Runtime Environment
At TeraLevel, we interpret this news as a call to reinforce DevSecOps discipline. When a company deploys AI agents that can interact with AWS, Google Cloud, or Kubernetes APIs, securing the model’s code is not enough; we need to shield the environment where that agent “lives” and “acts.”
The risk is no longer just the AI “saying” something inappropriate, but “doing” something destructive, such as deleting a production cluster or exfiltrating sensitive data using legitimate credentials.
Our Value Proposition for the Agentic Era
To mitigate the risks identified by OWASP without stalling innovation, TeraLevel proposes a secure infrastructure approach:
- Principle of Least Privilege (IAM): We review and harden the IAM roles used by your agents. An AI agent should never have full administrative permissions; we scope its access strictly to what is necessary via granular policies.
- Isolation via Containerization: We deploy agents in isolated environments (Sandboxing) using Kubernetes and Docker, ensuring that if an agent is compromised (Hijacked), the attacker cannot pivot to the rest of the corporate network.
- 24/7 Behavior Monitoring: Our observability systems look beyond server status to monitor anomalous resource usage patterns and API calls, detecting deviations in agent behavior in real-time.
Autonomous AI is the future of automation, but it requires a solid foundation. Shall we audit your AI agents’ permissions and environments today?