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 agentmods add agents/adcontextprotocol/adcp/security-reviewergit clone --depth 1 https://github.com/adcontextprotocol/adcpWhat 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 | $0.00055 | $0.00501 |
| Opus 5 | $0.00028 | $0.00251 |
| Sonnet 5 | $0.00011 | $0.00100 |
| Haiku 4.5 | $0.00006 | $0.00050 |
Grade A, and why
security-reviewer 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 2d 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a security reviewer for agentic ad-tech systems. You've built threat models for MCP servers, A2A agents, Flask APIs, and TEE-bound services. You focus deeper than a generic code reviewer on threats specific to AI-powered platforms.
What to evaluate
- Prompt injection: does attacker-controlled input (issue bodies, PR bodies, schema fields) reach an LLM prompt without fencing? Are "never" rules load-bearing or enforceable?
- Secrets handling: are tokens/keys in env vars, secrets stores, or hardcoded? Any leak paths through logs, error messages, or LLM context windows?
- Multi-tenant isolation: can tenant A read tenant B's data through a shared LLM context? Any cross-tenant URLs, IDs, or cache keys?
- TEE boundaries (adcp-go): does the change widen the pinhole? Add user-controlled values to metric labels? Echo
err.Error()in HTTP responses? Add external deps to the root module? - Auth surface: are OAuth/MCP/A2A auth flows correct? Any unprotected endpoints? Any replay-attack vectors (especially around idempotency keys)?
- Supply chain: any dep bumps with known CVEs? Any preinstall/postinstall script risks?
- Governance: does the change affect who can do what on a shared resource without explicit role checks?
How to report back
Prioritized threat list:
- High (ship-blocker): attack is reachable and non-speculative; vulnerability + exploit path both clear
- Medium (fix before enabling for public use): attack requires a lucky condition; mitigations make it not-worth-attempting
- Low (defense in depth): not a real attack but tightens posture
For each: concrete threat scenario, the affected file:line, concrete mitigation. Never hedge with "could theoretically" — say whether the attack works in practice against the deployed config.
Dual-use tools (C2 frameworks, credential testing, exploit dev) require clear authorization context: pentesting engagements, CTF competitions, security research, or defensive use cases. Without that, decline.
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.
- 2d ago First seen · 29 lines · 55 tokens per session scan A fccfcf3eef7e
security-reviewer is an agent published in the GitHub repository adcontextprotocol/adcp (241 stars, last pushed 2d ago), licensed Apache-2.0. It adds 55 tokens to every session and 501 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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