Tego's Skills Security Index: Assessing the Hidden Risks in AI Agent Skills

The rapid proliferation of AI agents has created a complex ecosystem where modular skills serve as the building blocks for functionality. However, this modularity introduces a significant security challenge that has largely flown under the radar: the security risks embedded within skill definitions themselves. Tego has addressed this gap with its Skills Security Index, a comprehensive assessment platform that analyzes and scores AI skills based on their potential security implications.

The Hidden Attack Surface in AI Skills

AI skills represent specialized capabilities that agents can utilize to perform specific tasks. What makes them particularly interesting from a security perspective is that they're essentially sets of instructions that guide AI behavior. These instructions can range from simple data processing to complex system interactions, and they often include permissions to access various resources.

The problem lies in how these skills are developed and deployed. Unlike traditional software components, AI skills often lack formal security vetting. They're frequently created by third-party developers, shared through registries, and integrated into AI systems with minimal security assessment. This creates a perfect storm where potentially risky capabilities can be introduced into AI environments without proper oversight.

"As AI agents increasingly rely on modular skills to perform tasks, the instructions used to define these skills become a critical attack surface," explains Tego in their documentation. "This index helps security engineers and developers understand the potential 'blast radius' of any given skill before deployment."

How the Skills Security Index Works

The Tego Skills Security Index analyzes over 2,500 skills from major platform registries, performing deep scans of each skill's identity, instructions, and associated code. The assessment follows a standardized security schema with a particular focus on instructional risk—identifying when skills might encourage an agent to bypass guardrails or perform sensitive operations without proper oversight.

The evaluation process examines several dimensions:

1. Instructional Analysis: Reviewing the prompts and directions within each skill to identify potentially risky behaviors.
2. Capability Assessment: Determining what modalities the skill encourages the agent to use.
3. Permission Validation: Evaluating whether resource requests are justified by the skill's stated purpose.

The platform then categorizes skills across several capability buckets: Tools, Code Execution, Web Access, File System, Data Access, Authentication, Network, and System. Each capability is marked as "detected" if the skill explicitly encourages the agent to utilize that modality.

The Risk Ranking Framework

Tego has developed a five-tier risk ranking system that helps developers quickly understand the potential security implications of each skill:

• Pass: No significant risks detected in instructions or tools
• Low: Minor capability risk with appropriate scoping context
• Medium: Potentially risky tool use or instructions that lack clear restrictions
• High: Direct instructions for sensitive operations (e.g., broad file system write or unencrypted network use)
• Critical: Encouragement of malicious actions, data exfiltration, or explicit bypasses

This framework assigns each skill the highest severity level detected across all assessed dimensions. For example, a skill might have low risk in most areas but be classified as "High" risk if it includes instructions for unencrypted network communication.

Real-World Examples of Skill Risk Levels

The index provides concrete examples of skills across different risk categories, offering valuable insights into the types of vulnerabilities that exist in the wild:

Medium Risk: 3d-web-experience

This skill helps build 3D experiences for the web using technologies like Three.js and React Three Fiber. It's classified as Medium risk primarily due to its System capabilities, which could potentially be abused for unauthorized system operations if not properly sandboxed.

Low Risk: ab-test-setup

This skill guides users through planning and setting up A/B tests. It receives a Low risk rating because, while it handles data, it doesn't encourage access to sensitive resources or perform risky operations.

High Risk: active-directory-attacks

This skill provides techniques for attacking Microsoft Active Directory environments. It's classified as High risk due to its extensive permissions, including high-risk capabilities in Authentication, Network, and System areas.

Critical Risk: aflpp

A fork of the AFL fuzzer with enhanced performance, this skill is marked as Critical due to its extensive permissions and potential for malicious use. The combination of High Code Execution, Web Access, Network, and System capabilities creates a significant security risk profile.

The Broader Implications for AI Security

The Skills Security Index addresses a critical need in the AI security landscape. As organizations increasingly deploy AI agents with access to sensitive systems and data, understanding the security implications of each component becomes essential.

The platform's approach of focusing on instructional risk rather than just technical permissions represents a more nuanced understanding of AI security. Traditional security assessments might focus on what a skill can technically do, but Tego's methodology also evaluates what the skill instructs the AI to do—capturing the subtleties of how AI behavior can be manipulated through prompt engineering.

For developers and security teams, the index provides several key benefits:

1. Risk Assessment: Quick identification of potentially risky skills before integration
2. Blast Radius Analysis: Understanding how a compromised skill might affect the broader system
3. Comparative Analysis: Ability to evaluate alternative skills with different risk profiles
4. Security Best Practices: Insights into how to design safer skills from the outset

The Future of AI Skill Security

As the AI ecosystem continues to evolve, the security of modular components will become increasingly important. The Skills Security Index represents an early step toward addressing this challenge, but much work remains to be done.

Future developments might include:

• Integration with automated deployment pipelines to block high-risk skills
• Real-time monitoring of skill usage to detect abnormal behavior
• Collaborative security assessments with community contributions
• Standardized security certifications for AI skills

The Tego Skills Security Index serves as both a practical tool and a call to action for the AI community. It highlights the need for greater attention to security in the development and deployment of AI components, helping to create a more secure foundation for the next generation of AI applications.

For organizations building AI agents, the message is clear: vet your skills with the same rigor you apply to any other software component. The Skills Security Index provides one valuable resource for this process, but it represents just the beginning of what will need to be a comprehensive approach to AI security.

To explore the Skills Security Index yourself, you can visit the official Tego website or check out their documentation for more details about their assessment methodology.