AbuseIPDB API reference. IP reputation and abuse report lookups.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you need to classify an IP as internet scanner noise vs. targeted activity. Returns noise/riot flags, classification (benign/malicious/unknown), actor name if known. IP-only. Commonly invoked by /ip-investigation to filter out mass-scanning noise. Retrieval only.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Queries GreyNoise to classify whether an IP is internet background noise (opportunistic scanners, search engines, etc.) or targeted. IP-only. Retrieval only.
Two CLIs are provided. Pick by capability needed:
node tools/clis/greynoise.js ip <ip>
Hits the /v3/community/{ip} endpoint (free, 50/day). Best for fast retrieval inside an investigation chain.
python3 tools/clis/greynoise.py community <ip> # FREE (50/day)
python3 tools/clis/greynoise.py context <ip> # Enterprise
python3 tools/clis/greynoise.py riot <ip> # Enterprise
python3 tools/clis/greynoise.py quick <ip,ip,...> # Enterprise (bulk)
python3 tools/clis/greynoise.py similarity <ip> # Enterprise
python3 tools/clis/greynoise.py timeline <ip> [--days N] # Enterprise
python3 tools/clis/greynoise.py query "<gnql>" # Enterprise
python3 tools/clis/greynoise.py stats "<gnql>" # Enterprise
python3 tools/clis/greynoise.py metadata
Self-bootstraps a private venv at tools/clis/.venv-greynoise/ on first run.
Capabilities the Node CLI doesn't have (Enterprise tier required for most):
query) — find every IP matching a query: classification:malicious tags:Mirai metadata.country:RU. The killer pivot — discover scanner clusters by characteristic.GNQL examples:
# All malicious scanners hitting port 22 from Russia in the last day
python3 tools/clis/greynoise.py query 'classification:malicious raw_data.scan.port:22 metadata.country:RU last_seen:1d'
# Stats: country distribution of Mirai-tagged IPs
python3 tools/clis/greynoise.py stats 'tags:"Mirai" last_seen:30d'
# Find IPs similar to a known scanner
python3 tools/clis/greynoise.py similarity 185.220.101.45 --limit 50
Both CLIs accept --dry-run.
/v3/community/{ip}) is public and unauthenticated. Both CLIs work without $GREYNOISE_API_KEY for the community subcommand. Rate-limited per source IP (a few per minute is fine; sustained bulk lookups will throttle).community.source: greynoise
indicator: <IP>
query_time: <ISO8601>
noise: <boolean> # seen as internet background noise
riot: <boolean> # rule-it-out: known-benign common service
classification: benign | malicious | unknown
name: <actor name or unknown>
last_seen: <date>
message: <summary>
Community endpoint: 50 req/day free. Enterprise: much higher.
Default rating for downstream /score-source: B2 (usually reliable, probably true). Empirical signal. A noise: true result is strong evidence the IP is not targeted.
riot: true is a strong signal the IP is a benign common service (CDN, search engine, etc.) — downgrade severity of any detection against it.classification: malicious + noise: true = mass-scanning threat actor infrastructure, not a targeted campaign./lookup-abuseipdb — abuse reports for the same IP/lookup-virustotal, /lookup-shodan — additional IP context/ip-investigation — uses GreyNoise to filter noise early in the pipelinetools/integrations/greynoise.mdtools/clis/greynoise.jstools/clis/greynoise.pyまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
AbuseIPDB API reference. IP reputation and abuse report lookups.
日本語の概要は準備中です。原文の説明を表示しています。
Analysis of Competing Hypotheses — structured technique for evaluating multiple explanations against evidence. Use when facing ambiguous attribution or multiple plausible scenarios. Prefers graded evidence items from /quality-of-information-check and runs that skill first when handed raw URLs or text. Runs on ungraded evidence when asked, labelled as such, unweighted, and capped at Moderate confidence.
日本語の概要は準備中です。原文の説明を表示しています。
Use when documenting a named campaign across time and victims, the user asks to start or update a campaign record, or another skill identified a multi-incident cluster that warrants formal tracking. Provides the template (timeline, attribution, victimology, attack chain, Diamond Model mapping, IOC clusters) and the lifecycle from active to historical.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks about carding, BIN attacks, payment-card breach markets, fullz/CVV2 trade, autoshops (BidenCash, Brian's Club, Russianmarket, B1ack's Stash), or financial-fraud TTPs. Self-updating knowledge cell.
日本語の概要は準備中です。原文の説明を表示しています。
Censys API v2 reference. Host reconnaissance and certificate data.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks about Chinese state-sponsored cyber operations or specific PRC-aligned actors (APT41, Volt Typhoon, Mustang Panda, APT10, APT31, Salt Typhoon, etc.), MSS/PLA-attributed campaigns, or PRC sector targeting. Self-updating knowledge cell.
日本語の概要は準備中です。原文の説明を表示しています。