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# Introduction
2026 is, without a doubt, the year of autonomous AI systems. We are seeing an unprecedented shift from functional chatbots to functional AI agents with reasoning capabilities — often combined with large-scale linguistic models (LLMs) or retrieval generative (RAG) systems. This change creates a cybersecurity situation that crosses a critical point of no return. The reason is simple: AI agents don't just answer questions – they action. They do this because of independent planning and consultation. The execution of actions such as sending mass emails, manipulating databases, and interacting with internal platforms or external applications is no longer something that only humans and engineers do. As a result, the complexity of the security paradigm has reached a new level.
This article provides a visual overview, based on the latest ideas and issues, about the current state of security for AI agents. After analyzing the main issues and risks, we tackle the question mentioned in the article: “Are AI agents your next security dream?”
Let's examine four key issues related to security risks in today's AI threat landscape.
# 1. Managing Excessive Agent Freedom in AI Shadows
Shadow AI is a concept that refers to the unsupervised, uncontrolled, and unauthorized deployment of AI agent-based applications and tools in the real world.
A significant and representative problem related to this idea is its focus OpenClaw (formerly named Moltbot). This is an open-source, self-contained AI agent tool that is gaining momentum and can be used to manage personal or business accounts with little or no restrictions. Not surprisingly, based on early reports of 2026, it has been labeled as “an AI agent's security nightmare.” Incidents have occurred where tens of thousands of OpenClaw instances have been exposed online without security barriers such as authentication, which would easily allow unauthorized, malicious users – or agents, for that matter – to take full control of the host machine.
Part of the pressing issue surrounding shadow AI lies in allowing employees to integrate agent tools into business settings without additional oversight from IT teams.
# 2. Addressing Supply Chain Risks
AI agents rely heavily on third-party ecosystems – specifically the capabilities, plugins, and extensions they use to interact with external tools via APIs. This creates a new software supply chain. According to recent threat reports, malicious tools or plugins are often disguised as legitimate product improvement solutions. Once integrated into an agent environment, these solutions can covertly use their access to perform unintended actions, such as executing remote code, silently extracting sensitive data, or installing malware.
# 3. Identifying New Attack Vectors
I Open the Web Application Security Project (OWASP) The Top 10 report on security risks for AI and LLM says that the threat panorama of 2026 presents new risks, such as “Agent Intent Hijacking”. This type of threat involves an attacker spoofing an agent's primary mission with hidden commands on the web. Another aspect concerns the memory that agents retain across time (commonly referred to as short-term and long-term memory processes). This memory retention scheme can make agents more vulnerable to corruption by incorrect data, thereby altering their behavior and decision-making capabilities. Other risks listed in the report include the two already discussed: excessive agency (LLM06:2025) and supply chain vulnerability (ASI04).
# 4. Using Missing Circuit Breakers
The effectiveness of traditional perimeter security methods is rendered ineffective against an ecosystem of multiple interconnected AI agents. Communication between autonomous systems and operating at the speed of a machine – often an order of magnitude faster than humans – means the risk of being vulnerable to independent damage to the entire network in a matter of milliseconds. Businesses often lack the necessary runtime visibility or “circuit breaker” mechanisms to identify and stop a “rogue agent” during a transaction.
Industry reports suggest that while perimeter security has improved somewhat, appropriate circuit breakers that include automatic service shutdown mechanisms when some level of malicious activity is reported are still sorely lacking between applications and APIs for agent-based systems.
# Wrapping up
There is a strong consensus among security organizations: you can't protect what you can't see. Strategic change is needed to mitigate the risks emerging from modern AI solutions. A good start to end the “security nightmare” in organizations would be to use open source control frameworks aimed at establishing runtime visibility, promoting strong access to the “less necessary”, and, most importantly, treating agents as first-class identities in the network, each labeled with their own trust scores.
Despite the undeniable risks, private agents are not inherently nightmare hunters as long as they are controlled by open but vigilant agencies. If so, they can turn what might look like a huge risk into a highly productive, manageable resource.
Iván Palomares Carrascosa is a leader, author, speaker, and consultant in AI, machine learning, deep learning and LLMs. He trains and guides others in using AI in the real world.
