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    Repair Prompt Injection, Instruction & Tool Boundary Gaps

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    Repair Prompt Injection, Instruction & Tool Boundary Gaps is a bounded ToolForge Labs workflow.

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    Repair Prompt Injection, Instruction & Tool Boundary Gaps

    Repair Prompt Injection, Instruction & Tool Boundary Gaps

    Example session with this skill installed

    An agent reads customer documents and web pages, summarizes them, and can call internal tools. A test document contained text telling the agent to ignore policy and send retrieved secrets to an external URL. Repair the control boundary so untrusted content cannot grant itself authority or bypass approvals.

    • Read your context and instructions
    • Compiled the repair prompt injection

    The likely issue is a contract mismatch around prompt injection, untrusted instructions, retrieved/web content, tool authorization, system/developer policy separation, data exfiltration paths, output handling, approval gates, and adversarial evaluation. I would first define the invariant: Untrusted content may influence task data but cannot change application authority, tool permissions, approval requirements, secret access, or destination policy; those controls must be enforced outside the model wherever possible. Then I would trace the smallest owning boundary, apply or propose a bounded repair/review step, and add deterministic normal, boundary, failure and replay tests. The workflow preserves existing authorization, safety/security policy, data-access boundaries, user approvals, privacy, and tool-side enforcement. If repository/workspace files are available, the findings can be mapped to exact paths and focused local validation; with partial evidence, the result stays an explicit diagnosis/evidence map with assumptions and no invented execution claims.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Repair Prompt Injection, Instruction & Tool Boundary Gaps is a bounded ToolForge Labs workflow. Diagnose, review, and repair prompt injection, untrusted instructions, retrieved/web content, tool authorization, system/developer policy separation, data exfiltration paths, output handling, approval gates, and adversarial evaluation. Works from full repositories/workspaces or partial evidence. Produces bounded fixes or evidence gates with deterministic validation while preserving existing product/security policy and avoiding invented execution claims. It works from a full repository/workspace or partial evidence such as snippets, logs, configuration, traces, documents, tickets, and architecture notes. The skill separates verified facts from assumptions, avoids invented execution claims, and produces concrete repair/review steps with focused deterministic validation or traceable evidence.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Security.

    Does not guarantee 100% immunity to all future novel attacks. Requires explicit user confirmation for unverified assumptions when full repository access is missing.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

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    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Permissions

    Terminal / Shell
    Read Files
    Write Files

    Use a clean branch or controlled review workspace. Supply repository/workspace files, logs, traces, configuration, documents, or evidence needed for the task. Review patches/findings before merge or operational action; keep production credentials and destructive production actions out of the workflow.

    Listed1 month ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill identifies and remediates critical LLM vulnerabilities, including prompt injection, data exfiltration paths, and unauthorized tool access, by implementing deterministic validation gates and clear instruction boundaries.

    The skill is designed to work with full code repositories or partial evidence like logs, traces, and snippets, making it compatible with any AI agent architecture regardless of the underlying LLM.

    Unlike broad AI suggestions, this skill produces bounded fixes and evidence gates that preserve your existing product policies while avoiding halluncinated or unverified execution claims.

    You receive the complete ToolForge Labs workflow for diagnosing gaps, a suite of adversarial evaluation templates, and a framework for implementing deterministic approval gates within your workspace.

    The workflow includes a dedicated phase for adversarial evaluation, where repairs are tested against known injection techniques to ensure the new boundaries are robust and effective.

    New in Security