Skip to content
Skillpedia
Agents280k · #6

agent-security-hardening

Makes your agent more secure when handling sensitive information or external tools. It protects against unauthorized access and errors.

What it does

This guide teaches how to secure AI systems that handle untrusted information. It helps create security barriers to prevent dangerous data from causing problems.

When to use it

  • ✓When building an AI system that receives external data.
  • ✓When reviewing code that turns data into actions.
  • ✓When separating different users or tasks within an AI system.

Example of what to ask your AI

“How can I ensure the AI doesn't execute dangerous commands based on data I provided?”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/affaan-m/ECC/blob/main/pi/core/skills/agent-security-hardening/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/affaan-m/ECC/blob/main/pi/core/skills/agent-security-hardening/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The guide suggests mapping where untrusted data enters, rigorously validating everything before any action, and containing access to files and other system parts.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add affaan-m/ECC --skill agent-security-hardening

Or download just the file:

curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/pi/core/skills/agent-security-hardening/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Agent Security Hardening

Harden the boundaries where an agent turns untrusted data into filesystem, network, memory, or tool actions. Produce concrete controls and tests for the framework being reviewed; a checklist without enforcement evidence is incomplete.

Important

  • Treat model output, retrieved content, tool output, memory, and cross-agent messages as untrusted input.
  • Default external integrations to read-only and grant each write capability separately.
  • Validate at the action boundary even when an upstream prompt or schema already validated the value.
  • Fail closed before a side effect when identity, destination, ownership, or containment cannot be proven.
  • Before a delete, publish, credential rotation, or other irreversible action, the real tool entry point must authorize the authenticated principal for that exact operation and resource scope, then require explicit confirmation or a valid scoped pre-approval. Missing or invalid evidence blocks the action.
  • Use hard bounds defined by the governing requirement and stricter values verified from deployment configuration. When neither defines a value, mark it as a required decision and test that the implementation rejects an unbounded configuration; do not invent a plausible value.

When to Activate

  • Building an autonomous agent, tool gateway, memory service, or agent-to-agent protocol
  • Reviewing code that maps model output into commands, paths, URLs, credentials, or API mutations
  • Isolating tenants, projects, sessions, or workspaces in a long-running agent process
  • Hardening temporary files, logs, tool responses, or external-content ingestion

Hardening Workflow

1. Map Trust Boundaries

List every boundary as source -> parser -> validator -> side effect. Include direct user input, model output, retrieved documents, memory, environment variables, tool results, and messages from other agents.

For each side effect, record:

  • the identity and scope authorizing it;
  • the exact validator that runs immediately before it;
  • the maximum input and output size;
  • the audit evidence produced;
  • the recovery path if the operation is interrupted.

This step is complete when every write, command, network mutation, and credential use has one named authorization boundary.

2. Validate Identifiers, Environment Values, and URLs

Use an allowlist for identifiers that become filenames, keys, selectors, or command arguments. Agent IDs have the issue-defined hard maximum of 64 characters; deployment configuration may choose a stricter positive limit but cannot raise that boundary:

import re

SAFE_AGENT_ID_CHARS = re.compile(r"^[A-Za-z0-9_][A-Za-z0-9_-]*$")
HARD_MAX_AGENT_ID_LENGTH = 64

def require_agent_id(value: object, *, max_length: int) -> str:
    if (
        isinstance(max_length, bool)
        or not isinstance(max_length, int)
        or not 1 <= max_length <= HARD_MAX_AGENT_ID_LENGTH
    ):
        raise RuntimeError("configured agent ID limit must be between 1 and 64")
    if (
        not isinstance(value, str)
        or len(value) > max_length
        or not SAFE_AGENT_ID_CHARS.fullmatch(value)
    ):
        raise ValueError("invalid agent identifier")
    return value

When an identifier becomes a CLI argument, pass it through an argv API and place it after the command's end-of-options marker where supported. Character validation is not a substitute for argument separation.

Reject missing and whitespace-only environment values before constructing a client:

def require_env(name: str, environ: dict[str, str]) -> str:
    value = environ.get(name)
    if value is None or not value.strip():
        raise RuntimeError(f"{name} is required")
    return value

Parse URLs, allow only schemes required by the deployment, require a host, reject embedded credentials, and block private-network destinations when the URL is attacker-controlled. For every outbound connection and redirect hop, pin the connection to a validated public address or verify the connected peer address at connection time so DNS rebinding cannot cross the boundary. A credential-bearing request must use HTTPS with certificate verification before credentials are attached. Disable automatic cross-origin credential forwarding; when the scheme or origin changes, strip the credentials and reauthorize the new destination or reject the redirect. Re-run destination, connected-address, and transport validation on every hop.

…

FAQ

What does 'treat model output as untrusted input' mean?

It means that anything the AI generates, even if it seems safe, must be checked before being used, as it might contain something unexpected or dangerous.

Why is it important to validate even if it was validated before?

Because validation at each step, especially before an action that could cause a side effect (like deleting something), ensures nothing dangerous slips through unnoticed.

What is 'fail closed'?

It's a security rule that says: when in doubt, block access or action. If it's not possible to prove something is safe, it's better not to allow it to happen.

Details

Original description by the author

Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials. Use when building or reviewing an agent runtime, autonomous worker, tool gateway, memory service, or multi-tenant agent deployment. Do not use for general web application security or offensive testing.

repo: affaan-m/ECC

Popularity

GitHub stars
280k
Ranking position
#6
License
MIT
Updated
10/15/2026

Is this skill useful?

0

Sign in to vote.

Comments

  • …

More skills from this repository

Similar skills