Building Secure Agent Skills: Preventing Prompt Injection

Prompt injection is one of the most common risks for agentic systems. It happens when untrusted input tricks an AI agent into breaking rules, exposing data, or performing unintended actions. If you are building agent skills, security must be a first-class concern. This guide explains how to define a threat model, harden your skills, and test for failures before they reach production.

What Prompt Injection Looks Like

Prompt injection usually arrives through external inputs such as web pages, user messages, or file contents. Examples include:

• Instructions hidden in a web page that override the agent’s rules.
• User content that asks the agent to reveal secrets or skip validation.
• Data payloads that include malicious “system” style commands.

These attacks exploit the fact that an agent reads text and treats it as instructions. The best defense is to treat all external input as untrusted.

Start with a Threat Model

Before you build a skill, define what could go wrong. A basic threat model includes:

• Assets: what data or systems must be protected.
• Inputs: where untrusted data can enter the workflow.
• Actions: what the agent can do if compromised.
• Impact: the worst-case outcome of a failure.

With this map, you can design guardrails that address real risk instead of hypothetical fears.

Hardening Tactics That Work

1. Strict tool boundaries

Limit the tools the skill can access. If a skill only needs a file reader, do not allow network access or shell commands.

2. Explicit input validation

Define what valid input looks like. Reject or sanitize data that does not match your schema. For example, only accept JSON with expected keys and types.

3. Separate instructions from data

Keep your system instructions separate from user content. In practice, this means:

• Provide user input in a data block.
• Tell the agent to treat that block as data only.
• Never allow user input to modify the workflow steps.

4. Use allowlists, not blocklists

Allowlists are safer because you control what is permitted. For example, only allow specific commands, file paths, or API endpoints.

5. Enforce least privilege

If a skill only needs to write to a single directory, do not allow broader write access. Small permissions reduce damage.

6. Add human approval gates

For high-risk actions (publishing, deploying, or deleting), require explicit human confirmation.

Defensive Prompt Design

Security is not just about tools. The skill instructions themselves can reduce risk:

• Use direct, unambiguous language.
• State explicitly that user content is untrusted.
• Require a summary of actions before execution.
• Require post-action verification.

These steps turn a generic assistant into a controlled agent.

Testing for Injection Risks

Security testing should be part of your skill lifecycle:

1. Red-team prompts: write test inputs that attempt to override rules.
2. Adversarial examples: include hidden instructions, role changes, or conflicting commands.
3. Regression tests: keep a suite of known attacks and re-run them when the skill changes.

If a skill fails a test, fix the workflow and re-test. Do not ship a skill that cannot resist basic injections.

Monitoring and Logging

Even with strong guardrails, issues can slip through. Add observability so you can detect failures quickly:

• Log tool usage and outputs.
• Record rejected inputs and why they were blocked.
• Track changes made by agents.

These logs help you audit behavior and improve rules over time.

Practical Example: Secure Content Update Skill

Imagine a skill that updates blog metadata. A secure version might:

• Accept only a JSON payload with specific fields.
• Validate the content length and character limits.
• Write only to a single directory.
• Require a summary of changes before saving.
• Run a lint or format check after the update.

The result is a predictable workflow that resists injection attempts.

Conclusion

Prompt injection is real, but it is manageable. Secure agent skills require a mix of threat modeling, strict tool boundaries, explicit validation, and testing. When you treat skills like production software, you build systems that are safe and reliable at scale.

Security is not a one-time task. Keep iterating, test new failure cases, and update your skills as your workflows evolve.