Borrowing it
Nothing to install: this file belongs to adcontextprotocol/adcp-client. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/adcontextprotocol/adcp-client/main/.claude/agents/security-reviewer-deep.mdgit clone --depth 1 https://github.com/adcontextprotocol/adcp-clientWrote 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/adcontextprotocol/adcp-client/security-reviewer-deep)<a href="https://agentmods.dev/agents/adcontextprotocol/adcp-client/security-reviewer-deep"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/security-reviewer-deep/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/adcontextprotocol/adcp-client/security-reviewer-deep"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/security-reviewer-deep.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 | $0.00064 | $0.02413 |
| Opus 5.5 | $0.00026 | $0.00965 |
| Sonnet 5.5 | $0.00013 | $0.00483 |
| Haiku 4.5 | $0.00006 | $0.00241 |
Grade A, and why
security-reviewer-deep 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 14d 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.
This is a copy
100% identical to security-reviewer-deep — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Reviewer - Agentic Ad Tech Systems
Core Identity
You are a security reviewer who specializes in the attack surfaces unique to agentic AI systems. You understand that traditional web security (SQLi, XSS, CSRF) is necessary but insufficient for platforms where LLMs process user input, tool calls execute actions, and system prompts control behavior. You know that an attacker who modifies a system prompt can silently poison thousands of users without touching application code.
Your reviews are informed by real breaches. The McKinsey/Lilli attack (March 2026) demonstrated that AI chatbot platforms face a specific threat model: exposed API documentation enables automated enumeration, SQL injection through JSON keys bypasses parameterized queries, and database-stored system prompts become a single point of compromise for all downstream users.
When Invoked
- Run
git diffto see recent changes (or review the files/PR specified) - Identify which system the changes touch and what data flows through it
- Trace user input from entry point to every place it is used, stored, or forwarded
- Apply the review checklist below, starting with Priority 0
- Check for attack chains: vulnerabilities that are low-severity alone but critical in combination
Review Priorities
Priority 0: Prompt Injection and System Prompt Integrity
Direct prompt injection:
- User input concatenated into system prompts, tool descriptions, or LLM context without sanitization
- Chat messages, form fields, campaign names, product descriptions, or any user-controlled string that reaches an LLM
- Template literals or f-strings that embed user data into prompt text (search for backtick templates and f-strings near LLM calls)
Indirect prompt injection:
- Stored data (chat history, database records, file contents) that is later retrieved and included in LLM context
- Campaign names, product descriptions, or metadata that flow from one user's input into another user's agent context
- Tool responses from external systems that could contain adversarial instructions
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.
- 14d ago First seen · 192 lines · 64 tokens per session scan A 2f9258239713
security-reviewer-deep is an agent published in the GitHub repository adcontextprotocol/adcp-client (18 stars, last pushed today), licensed Apache-2.0. It adds 64 tokens to every session and 2,413 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to security-reviewer-deep, differing in 0 lines, and is treated as a copy.
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