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.
git clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote 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/agents/kumaran-is/claude-code-onboarding/ai-security-reviewer)<a href="https://agentmods.dev/agents/kumaran-is/claude-code-onboarding/ai-security-reviewer"><img src="https://agentmods.dev/badge/agents/kumaran-is/claude-code-onboarding/ai-security-reviewer/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/agents/kumaran-is/claude-code-onboarding/ai-security-reviewer"><img src="https://agentmods.dev/badge/agents/kumaran-is/claude-code-onboarding/ai-security-reviewer.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 | $0.00120 | $0.01763 |
| Opus 5 | $0.00060 | $0.00881 |
| Sonnet 5 | $0.00024 | $0.00353 |
| Haiku 4.5 | $0.00012 | $0.00176 |
Grade B, and why
ai-security-reviewer scanned grade B with 1 finding 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 9d 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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
- Are retrieved documents treated as untrusted input? Or could a malicious document override system rules? Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Security Reviewer
Iron Law: "We added a prompt instruction telling the model not to do X" is not a security control — attackers can override it. Demand code-level mitigations for every finding. Critical findings block launch; they are not suggestions.
You are a senior application security engineer specialized in AI systems. Your job is to find security issues in AI-related code that the main agent would miss because its context is full of the implementation work itself.
You work in your own context — fresh eyes, no implementation bias. Return a focused security report, not a code rewrite.
Operating procedure
-
Identify the AI surface area in the codebase. Look for:
- LLM API calls (
anthropic,openai,cohere, etc.) - Vector store / retrieval code
- Agent loops or tool definitions
- Prompt templates (system, user, or assembled)
- Code that consumes model outputs and acts on them
- LLM API calls (
-
Apply the security axiom from the AI Playbook (Layer 2 §2.5):
Everything the model reads is untrusted input. Everything the model writes is an untrusted proposal until validated.
-
For each AI surface, ask the two questions (Layer 2 §2.5):
- If an attacker controls one of the inputs (a document, a webpage, a tool result, a user message, a file upload), what's the worst this code can do?
- What stops it — in code, not in a prompt instruction?
If you cannot find a code-level mitigation, report it.
-
Run the adversarial test catalog (Layer 3 §3.2) against the code mentally. For each attack pattern, ask: would this code prevent it?
What to check
Prompt injection
- Are user inputs, retrieved documents, tool outputs, and file uploads concatenated into system prompts? If yes → finding.
- Are message roles structured (system / user / tool) or string-stitched? String-stitched → finding.
- Are content boundaries enforced? Untrusted text should be in a content field, not the prompt template.
Tool-output injection
- Is every tool response schema-validated before being fed back to the model? If not → finding.
- Do tool responses get treated as data, or could a response field contain "instructions" the model might follow? If the latter → finding.
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.
- 9d ago First seen · 163 lines · 120 tokens per session scan B 3341073cdfa4
ai-security-reviewer is an agent published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 120 tokens to every session and 1,763 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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