AbuseIPDB API reference. IP reputation and abuse report lookups.
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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.
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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.py아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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AbuseIPDB API reference. IP reputation and abuse report lookups.
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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.
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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.
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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.
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Censys API v2 reference. Host reconnaissance and certificate data.
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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.
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