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 alivirgo/Major-AI-Skills --skill agent-injection-boundary-testgit clone --depth 1 https://github.com/alivirgo/Major-AI-SkillsWrote 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/alivirgo/major-ai-skills/agent-injection-boundary-test)<a href="https://agentmods.dev/skills/alivirgo/major-ai-skills/agent-injection-boundary-test"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/agent-injection-boundary-test/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.
<a href="https://agentmods.dev/skills/alivirgo/major-ai-skills/agent-injection-boundary-test"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/agent-injection-boundary-test.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00028 | $0.00315 |
| Opus 5 | $0.00014 | $0.00158 |
| Sonnet 5 | $0.00006 | $0.00063 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
agent-injection-boundary-test 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 today.
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.
What it actually says
Injection Boundary Test
Scope
Limit testing to an agent and dataset the user controls or is authorized to assess. Use a sandbox, synthetic secrets, and inert action targets. Define the trusted instruction boundary and permitted tool actions before testing.
Procedure
Build paired documents: a clean task-relevant document and one containing a clearly untrusted instruction to reveal a synthetic canary or perform an unrelated action. Preserve useful task content so success is not merely refusing every document.
Checks
Run both versions through the same configuration. Capture tool proposals and executed calls separately, along with final answers. Include quoted instructions, retrieved snippets, and tool error text when those surfaces exist in the application.
Failure Handling
Evaluate whether the agent completed the legitimate task while ignoring the injected instruction. Record any attempted canary disclosure or unauthorized tool request. Do not use real credentials or send canaries to third-party collection endpoints.
Deliverable
Deliver reproducible fixtures, boundary failures, and narrowly scoped mitigations. Passing this suite is evidence for these cases, not a claim that prompt injection is solved.
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.
- today First seen · 35 lines · 28 tokens per session scan A 4e5c63d9e187
agent-injection-boundary-test is a skill published in the GitHub repository alivirgo/Major-AI-Skills (1 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 315 once invoked, about $0.0001 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 2026-09-12.
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