跳到正文
cccskills
免费在 GitHub 公开

intelligence-sharing

Use when the user asks about ISAC participation, TAXII feeds, MISP communities, FIRST, STIX-based sharing, or how to publish intelligence externally. Covers sharing models, standards, communities, and TLP-governed dissemination.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(1)

  • SKILL.md3.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Intelligence Sharing

Why Share?

Intelligence sharing creates a network effect — threats identified by one organisation protect the entire community. However, sharing must be governed, reciprocal, and risk-managed.

Sharing Models

1. ISACs (Information Sharing and Analysis Centers)

Sector-specific sharing communities.

ISACSectorURL
FS-ISACFinancial Servicesfs-isac.com
H-ISACHealthcareh-isac.org
IT-ISACTechnologyit-isac.org
E-ISACEnergyeisac.com
A-ISACAviationa-isac.com
Auto-ISACAutomotiveautomotiveisac.com

Benefits: Sector-relevant intelligence, trusted community, pre-vetted members, established TLP norms.

2. FIRST (Forum of Incident Response and Security Teams)

Global incident response community. Membership-based, includes national CERTs and enterprise security teams.

Sharing via: FIRST mailing lists, TF-CSIRT community, CTI SIG.

3. MISP (Malware Information Sharing Platform)

Open-source threat intelligence platform for sharing IOCs and threat data.

How to share via MISP:

  1. Create an event with appropriate distribution
  2. Add attributes (IOCs) with correct types
  3. Tag with TLP, ATT&CK techniques, and galaxies
  4. Set distribution: Organisation only / Community / Connected communities / All
  5. Publish event

4. STIX/TAXII (Automated Sharing)

Standards for structured intelligence exchange.

  • STIX 2.1: Format for representing intelligence (see stix-bundle skill)
  • TAXII 2.1: Transport protocol for exchanging STIX bundles

TAXII channels:

  • Collections: Server-hosted repositories of STIX objects
  • Channels: Push-based distribution
  • API roots: Discovery endpoints

5. Bilateral/Multilateral Sharing

Direct sharing with trusted partners under agreed terms.

Requirements:

  • Sharing agreement (legal framework)
  • TLP adherence by both parties
  • Secure communication channel
  • Reciprocity expectation

TLP and Sharing

TLPSharing ScopeAutomated Sharing?
REDNamed recipients onlyNo
AMBER+STRICTOwn organisationNo
AMBEROrganisation + need-to-knowRestricted TAXII
GREENCommunityISAC portals, MISP community
CLEARUnrestrictedPublic TAXII feeds, blogs

What to Share

Do Share

  • IOCs from confirmed incidents (with context)
  • TTPs observed in your environment
  • Detection rules that work
  • Vulnerability intelligence relevant to sector
  • Anonymised incident lessons learned

Don't Share

  • Source-identifying information without source consent
  • Victim-identifying information without victim consent
  • Raw internal telemetry or logs
  • Information that reveals your security posture
  • Speculation presented as intelligence

Sharing Quality

Shared intelligence should meet the same quality standards as internal products:

  • Source assessment (Admiralty Scale)
  • Confidence levels
  • MITRE ATT&CK mapping
  • Proper TLP marking
  • Context (not just raw IOCs — explain what they mean)

The number one complaint about shared intelligence: "Just IOCs with no context." Always share the WHY alongside the WHAT.

Legal Considerations

  • Comply with data protection regulations (GDPR, sector-specific rules)
  • Ensure sharing agreements cover liability
  • TLP is a trust agreement, not a legal framework — complement with contracts
  • Be aware of export control regulations for certain technical intelligence
  • Document all sharing decisions for audit trail

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

AbuseIPDB API reference. IP reputation and abuse report lookups.

中文简介正在准备中。当前显示原文说明。

Liberty91LTD/cti-skills272026年9月30日 更新

ach

免费

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.

中文简介正在准备中。当前显示原文说明。

Liberty91LTD/cti-skills272026年9月30日 更新

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.

中文简介正在准备中。当前显示原文说明。

Liberty91LTD/cti-skills272026年9月30日 更新

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.

中文简介正在准备中。当前显示原文说明。

Liberty91LTD/cti-skills272026年9月30日 更新

Censys API v2 reference. Host reconnaissance and certificate data.

中文简介正在准备中。当前显示原文说明。

Liberty91LTD/cti-skills272026年9月30日 更新

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.

中文简介正在准备中。当前显示原文说明。

Liberty91LTD/cti-skills272026年9月30日 更新

查看 Liberty91LTD 的全部技能

报告此技能的问题