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.
npx skills add Kaademos/secure-sdlc-agents --skill ai-securitygit clone --depth 1 https://github.com/Kaademos/secure-sdlc-agentsWrote 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.
[](https://agentmods.dev/skills/kaademos/secure-sdlc-agents/ai-security)<a href="https://agentmods.dev/skills/kaademos/secure-sdlc-agents/ai-security"><img src="https://agentmods.dev/badge/skills/kaademos/secure-sdlc-agents/ai-security.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.01807 |
| Opus 5 | $0.00025 | $0.00903 |
| Sonnet 5 | $0.00010 | $0.00361 |
| Haiku 4.5 | $0.00005 | $0.00181 |
Grade A, and why
ai-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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Security
Overview
This skill applies structured security analysis to AI and LLM-powered features. The threat categories here — prompt injection, excessive agency, output misuse, supply chain — did not exist before 2023 and are still being misunderstood by most developers shipping AI features today.
Working assumption: every model is a trust boundary, not a trusted component. Model outputs must be treated as untrusted user input to every downstream system.
Reference framework: OWASP Top 10 for LLMs 2025 (LLM01–LLM10).
When to Use
- Any code that calls an LLM API (OpenAI, Anthropic, Google, Mistral, self-hosted)
- Any feature that sends user-supplied content to a model
- RAG systems, embeddings, vector databases, or retrieval pipelines
- AI agents with tool access (file system, HTTP requests, database writes, email)
- Features where model output is rendered in UI, executed as code, or used in queries
- Selecting or integrating a third-party model, fine-tune, or embedding
Process
Step 1 — Map the attack surface
Before finding vulnerabilities, enumerate:
| Question | Why it matters |
|---|---|
| Who sends input to the model? | Determines direct injection risk |
| What external sources feed the prompt context? | Determines indirect injection risk |
| What tools / functions can the model invoke? | Determines excessive agency blast radius |
| What happens to the model's output? | Determines output handling risk |
| Is user PII sent to a third-party API? | Determines data leakage and legal risk |
| Where does the model or its weights come from? | Determines supply chain risk |
Step 2 — Assess prompt injection risk (LLM01, LLM07)
Input trust classification:
| Input Source | Trust Level | Injection Risk |
|---|---|---|
| Authenticated user (UI) | LOW | Direct prompt injection |
| Public / unauthenticated user | UNTRUSTED | Direct + jailbreak attempts |
| Retrieved document (RAG) | UNTRUSTED | Indirect prompt injection |
| Tool / function call result | MEDIUM | Injection via external API response |
| Database query result | MEDIUM | Injection via poisoned records |
| Web scraping / search | UNTRUSTED | Indirect injection |
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.
- 8d ago First seen · 164 lines · 50 tokens per session scan A 853dc5cd5473
ai-security is a skill published in the GitHub repository Kaademos/secure-sdlc-agents (13 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 1,807 once invoked, about $0.0003 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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