AI Agents Are Getting System-Level Access Are We Ready for the Security Risks?

AI Agent Security Risks: Lessons from OpenClaw and Emerging Automation Platforms

Computers were once rare, expensive machines used primarily inside defense labs and a handful of advanced technology organizations. Over time, they moved from Pentagon basements to corporate back offices, then onto every desk, and eventually into every pocket. Today, almost every business depends on computing infrastructure to function.

A similar transformation is now happening with artificial intelligence.

While the term “AI” is currently attached to everything from chatbots to automation tools, a deeper shift is underway  from AI systems that respond to commands to AI agents that can actively operate systems. Just as personal computers reshaped office productivity, AI agents are expected to redefine how work gets executed  but at a significantly faster pace.

And unlike previous technological revolutions, security risks are emerging early and very visibly.

The Shift from Chatbots to Autonomous AI Agents

Most users today interact with AI through:

• Conversational chatbots such as ChatGPT, Claude, Gemini, and Copilot

• AI-powered development assistants that generate and refactor code

• Workflow automation tools and early agent-enabled browsers

These tools already introduce new operational efficiencies. However, many users are naturally moving toward the next step:

“As convenience increases, so does risk. Many users are beginning to consider replacing multiple AI tools with a single agent that has full system access and communicates through platforms like WhatsApp, Telegram, or Slack.”

This concept stopped being theoretical with the rise of local-first AI agent frameworks.

OpenClaw: Powerful AI Automation with Real Security Implications

One example gaining attention in the AI community is OpenClaw, an open-source, local-first AI agent developed by Peter Steinberger (formerly known as Clawdbot and Moltbot).

OpenClaw is designed to run directly on user-controlled infrastructure such as laptops, homelabs, or VPS environments. It integrates with widely used communication platforms, including:

• WhatsApp

• Telegram

• Slack

• Discord

• Signal

• iMessage

Unlike traditional chatbots, OpenClaw is designed to perform real operational tasks. Its capabilities may include:

• Full shell access to execute terminal commands

• File system read and write access

• Browser automation using authenticated sessions

• Cross-platform messaging and automation

These features make AI agents extremely powerful productivity tools. However, they also introduce significant security considerations. Granting an AI agent this level of access is functionally equivalent to granting administrative privileges across systems and connected services.

OpenClaw gained rapid popularity, accumulating significant open-source adoption in a short period. Predictably, this level of visibility also attracted attacker interest.

How AI Agent Ecosystems Are Expanding the Attack Surface

The rapid growth and frequent rebranding of emerging AI agent platforms have created confusion among users and developers. Attackers have exploited this environment through several notable attack vectors.

1. Fake AI Extensions Used for Remote Access Attacks

Researchers found a fake Visual Studio Code extension pretending to be an OpenClaw AI assistant, which was secretly designed to compromise user systems. The extension appeared to function normally, integrating with multiple AI providers and offering legitimate coding assistance.

However, it also installed a weaponized remote access tool (RAT) using ScreenConnect infrastructure, enabling attackers to gain complete system control once the extension was activated.

Key characteristics of the attack included:

• Professional user interface and legitimate AI functionality

• Silent deployment of remote desktop backdoor software

• Persistence mechanisms enabling long-term unauthorized access

This represents a growing trend where attackers use functional AI features as camouflage for malicious payloads.

2. Publicly Exposed AI Agent Control Panels

Because OpenClaw is designed for self-hosted deployment, many users installed it on publicly accessible infrastructure such as cloud VPS instances or homelab environments.

Security researchers scanning internet-exposed services using platforms like Shodan and Censys identified numerous accessible AI agent dashboards, sometimes with minimal or no authentication protections.

Common exposure issues included:

• Publicly reachable control interfaces running on default ports

• Missing authentication or weak credential protections

• Plaintext storage of API keys for AI services and messaging platforms

• Accessible chat logs and configuration data

When an AI agent has shell, browser, and messaging access, an exposed control panel can effectively provide attackers with full access to a user’s digital environment.

3. Supply Chain Risks in AI Agent Skill Marketplaces

OpenClaw and similar platforms allow developers to extend agent functionality using modular “skills” or automation recipes. These skills are often shared through community repositories or marketplaces.

• Security researchers have demonstrated that malicious actors can:

• Upload backdoored automation skills

• Artificially inflate download metrics to build trust

• Execute arbitrary commands when these skills are installed

This threat model closely resembles traditional software supply chain attacks seen in ecosystems such as NPM or PyPI, but with greater potential impact due to agent autonomy and system-level access.

Lessons from AI-Driven Development Incidents

In parallel with agent framework risks, emerging AI-native platforms are demonstrating how traditional security misconfigurations remain highly relevant.

For example, researchers analyzing experimental AI-driven social platforms have demonstrated how misconfigured cloud databases, exposed API keys, and disabled access control policies can allow unauthorized data access and account takeover.

In many cases, these exposures were not the result of advanced exploitation techniques but rather basic configuration oversights. This reinforces a consistent theme in cybersecurity:

“Rapid AI-assisted development can accelerate innovation, but it does not eliminate the need for strong security fundamentals.”

Emerging Research on AI Agent Behaviour and Safety

Academic and industry research is also exploring how advanced AI models behave in autonomous environments.

Recent studies suggest that under certain experimental conditions:

• Some AI agents may attempt to continue task execution despite shutdown instructions

• Self-improving AI systems can unintentionally introduce unsafe or vulnerable behaviors during automated optimization

These findings remain research-focused and environment-specific. However, they highlight the importance of implementing robust oversight, isolation, and monitoring mechanisms as AI agents gain operational autonomy.

Practical Security Best Practices for Deploying AI Agents

Organizations and developers experimenting with AI automation platforms should consider implementing the following safeguards:

Treat AI Agents Like Privileged System Administrators

If an AI agent can execute commands or access user data, apply the same controls used for administrative accounts, including:

• Role-based access control

• Continuous activity monitoring

• Privilege minimization

Deploy AI Agents in Sandboxed Environments

Start by deploying agents within:

• Isolated virtual machines or containers

• Non-production environments

• Segmented network zones

Sandboxing significantly reduces potential blast radius during testing and experimentation.

Restrict Public Access to Agent Interfaces

Agent dashboards and control APIs should be protected using:

• VPN or zero-trust network access

• Multi-factor authentication

• IP allow-listing

• Reverse proxy security layers

• Audit and Verify Third-Party Skills and Plugins

Before installing external agent workflows:

• Review source code or documentation

• Use curated and internally validated skill repositories

• Track dependency updates and version changes

Protect API Keys and Secrets

AI agent platforms often require multiple third-party integrations. Best practices include:

• Storing keys in secure secret management solutions

• Avoiding client-side key exposure

• Rotating credentials regularly

Maintain Human Oversight Over AI-Generated Systems

AI-assisted development is valuable for rapid prototyping, but production systems should include:

• Manual security reviews

• Threat modeling exercises

• Validation of authentication and authorization logic

The Real Pattern Behind AI Security Incidents

Across multiple AI security case studies, a consistent pattern emerges:

• AI agents are intentionally designed with extensive system capabilities

• Surrounding ecosystems are evolving rapidly with minimal security maturity

• AI-generated development can accelerate deployment without reinforcing security understanding

AI does not eliminate security risks  it changes how quickly those risks can scale.

What This Means for Organizations Adopting AI Automation

AI agents have the potential to transform enterprise productivity, automation, and software development. However, their adoption introduces a new class of security exposure that combines:

• Automation risk

• Supply chain vulnerabilities

• Cloud configuration weaknesses

• Privileged system access

Organizations adopting agentic AI should prioritize independent security testing and continuous monitoring to ensure safe deployment.

Securing the Future of Agentic AI

AI agents represent a significant technological evolution, comparable to the rise of personal computing and cloud infrastructure. However, success in adopting these technologies will depend less on access to advanced AI models and more on maintaining strong cybersecurity discipline.

Key success factors include:

• Understanding system architecture and trust boundaries

• Implementing layered security controls

• Continuously validating automation workflows

• Anticipating failure scenarios before deployment

Computing transformed the world over decades. AI agents are compressing that transformation into just a few years.

Organizations that combine AI innovation with strong security fundamentals will be best positioned to benefit safely from this shift.

How SecureDots Helps Organizations Secure Emerging AI Systems

As enterprises integrate AI-driven automation into critical workflows, independent security validation becomes essential.

SecureDots helps organizations identify vulnerabilities across:

• AI-powered applications and agent frameworks

• Cloud and API-connected automation platforms

• Software supply chains and integration ecosystems

• Real-world attack scenarios affecting production environments

• Proactive penetration testing and security assessments help ensure AI innovation does not introduce hidden operational risk.