llm-app-security

llm-app-security is a skill for Claude Code, Codex from GoldenWing-360/claude-security-skills. It costs 88 tokens per session (2,104 once invoked), scanned A, original, MIT.

An operations and security guide for applications that use language-model APIs, such as the Anthropic API. It maps common AI risks to controls including rate limits, spending limits, privacy filtering, logs, and incident response.

In plain words
What is it for?
Use it when launching, reviewing, or operating an AI feature in production, investigating unusual usage or costs, responding to complaints, or reviewing a provider security notice.
Why use it?
It helps control abuse, unexpected API bills, privacy leaks, unsafe outputs, and problems caused by changing model versions after launch.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when launching, reviewing, or operating an AI feature in production, investigating unusual usage or costs, responding to complaints, or reviewing a provider security notice.

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Install with agentmods
npx agentmods add skills/goldenwing-360/claude-security-skills/llm-app-security
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add GoldenWing-360/claude-security-skills --skill llm-app-security
Clone the repo
git clone --depth 1 https://github.com/GoldenWing-360/claude-security-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for llm-app-security

README.md
[![agentmods](https://agentmods.dev/badge/skills/goldenwing-360/claude-security-skills/llm-app-security/github.svg)](https://agentmods.dev/skills/goldenwing-360/claude-security-skills/llm-app-security)
Your own site
<a href="https://agentmods.dev/skills/goldenwing-360/claude-security-skills/llm-app-security"><img src="https://agentmods.dev/badge/skills/goldenwing-360/claude-security-skills/llm-app-security/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for llm-app-security

Your own site · 80×15
<a href="https://agentmods.dev/skills/goldenwing-360/claude-security-skills/llm-app-security"><img src="https://agentmods.dev/badge/skills/goldenwing-360/claude-security-skills/llm-app-security.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,104 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00088 $0.02104
Opus 5 $0.00044 $0.01052
Sonnet 5 $0.00018 $0.00421
Haiku 4.5 $0.00009 $0.00210

Measured 11d ago against content hash d94759391447, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

llm-app-security scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

llm-app-security/SKILL.md · 162 lines

How it starts

The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LLM App Security

The prompt and the tools are one layer of the story. This skill covers the other layer: the operational side of running an LLM-powered feature in production. The threats are mostly mundane (abuse, cost, leak, compliance), the controls mostly familiar from API security, applied to a substrate that has new failure modes.

Companion to prompt-injection-defense (prompt layer) and ai-agent-guardrails (tool layer).

When to invoke

  • Designing or reviewing an LLM feature before public launch
  • Investigating unusual API spend or 429s
  • Handling an abuse complaint ("your AI told a user to ...", "your AI leaked ...")
  • After a model-provider security advisory
  • Periodic re-review of an existing LLM product surface

The OWASP LLM Top 10 — practical mapping

Walk these against your app. Most issues fall under one of these.

ID Issue Practical control
LLM01 Prompt injection See prompt-injection-defense
LLM02 Insecure output handling Treat model output as untrusted: escape, sanitize, validate before acting
LLM03 Training-data poisoning Mostly upstream; pick reputable providers, version-pin
LLM04 Model DoS Per-user/IP rate limit; cost cap; max-tokens cap; timeout
LLM05 Supply chain Pin SDK versions, audit MCP/plugin packages, scan deps
LLM06 Sensitive information disclosure PII scrubbing pre-context; output review; allowlist what the model can fetch
LLM07 Insecure plugin / tool design See ai-agent-guardrails
LLM08 Excessive agency Narrow tool scope; human-in-loop for high-tier actions
LLM09 Overreliance UI disclosures; show provenance for facts; gate medical/legal/financial advice
LLM10 Model theft / prompt theft Treat the system prompt as a secret (not strong, but reduces casual leakage)

Rate limiting and cost caps

Read the full file on GitHub · 162 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 162 lines · 88 tokens per session scan A d94759391447

Subscribe to this mod's changes

llm-app-security is a skill published in the GitHub repository GoldenWing-360/claude-security-skills (17 stars, last pushed 1mo ago), licensed MIT. It adds 88 tokens to every session and 2,104 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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