跳到正文
cccskills
免费在 GitHub 公开

deploying-cloud-deception-with-decoy-resources

Set up decoy credentials, storage, and secrets across AWS, Azure, and GCP to detect suspicious access. Connect audit logs to security alerts, verify detection, and document permissions, resource inventories, and maintenance plans.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Deploy cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach detection.

查看安装方法

适用场景

  • Detecting use of leaked cloud credentials
  • Monitoring suspicious access to decoy storage and secrets
  • Extending on-premises deception coverage to cloud environments
  • Verifying alerts and maintaining a decoy inventory

包含的文件(5)

  • SKILL.md11.0 KB
  • assets/template.md2.3 KB
  • LICENSE11.0 KB
  • references/standards.md4.1 KB
  • scripts/process.py5.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Deploying Cloud Deception with Decoy Resources

When to Use

  • When cloud accounts (AWS/Azure/GCP) hold crown-jewel data or infrastructure and you need a tripwire that fires the moment an attacker who has gained access starts to operate.
  • When the only deception in place is on-prem honeypots, leaving the cloud control plane uninstrumented.
  • When seeding fake credentials to catch credential theft, accidental code-repo leaks, or secrets exposed in build pipelines.
  • When detecting cloud reconnaissance (enumeration of IAM, storage, or secrets) and lateral movement that legitimate users would never perform.
  • When you want detections that survive into incident response with strong fidelity — a touch on a decoy resource almost always means malicious or unauthorized activity.

This is the cloud counterpart to on-prem honeypot/honeytoken/canary-token deployment skills. For program strategy and how these Activities map to adversary engagement goals, use designing-adversary-engagement-with-mitre-engage.

Prerequisites

  • Cloud admin/IAM permissions to create decoy principals, storage, secrets, and detection wiring, ideally in a dedicated deployment role with least privilege.
  • Cloud audit logging already enabled: AWS CloudTrail (multi-region, with management and relevant data events), Azure Activity log + Microsoft Entra audit/sign-in logs, GCP Cloud Audit Logs (Admin Activity always on; Data Access enabled where needed).
  • A SIEM/alert sink: SNS topic, Microsoft Sentinel workspace, or GCP Pub/Sub + Monitoring, with routing to the SOC.
  • A naming and tagging convention that is plausible to an attacker but unambiguous to defenders internally (e.g., realistic names, plus an internal deception=true tag/label kept out of attacker-visible metadata).
  • Decoy principals must be permission-less (explicit deny-all). The value is the alert, never the access. A decoy that grants real privilege is a liability, not a control.

Workflow

1. Decide what to mimic

Pick decoys that match how your attackers operate: leaked AWS keys (credential theft), an "admin" S3 bucket (data discovery), a prod-db-password secret (secrets harvesting), a privileged-looking service account (cloud lateral movement). Place credential decoys where harvesting tools look: env files, CI variables, code comments, an internal wiki.

2A. AWS — canary access keys on a permission-less user

Create a decoy IAM user with an explicit deny-all policy, then issue an access key to plant:

aws iam create-user --user-name svc-backup-prod --tags Key=deception,Value=true
aws iam put-user-policy --user-name svc-backup-prod \
  --policy-name deny-all \
  --policy-document '{"Version":"2012-10-17","Statement":[{"Effect":"Deny","Action":"*","Resource":"*"}]}'
aws iam create-access-key --user-name svc-backup-prod   # plant the returned AccessKeyId/Secret

Any use of this key appears in CloudTrail (even denied calls, which still log AccessDenied). Wire an EventBridge rule on CloudTrail to alert:

aws events put-rule --name decoy-key-used \
  --event-pattern '{"detail":{"userIdentity":{"userName":["svc-backup-prod"]}}}'
aws events put-targets --rule decoy-key-used \
  --targets "Id"="1","Arn"="arn:aws:sns:us-east-1:111111111111:soc-deception-alerts"

2B. AWS — honey S3 bucket

Create a believable bucket, enable object-level data events, and alert on any read/list:

aws s3api create-bucket --bucket acme-prod-db-backups-2026 --region us-east-1
aws s3api put-bucket-tagging --bucket acme-prod-db-backups-2026 \
  --tagging 'TagSet=[{Key=deception,Value=true}]'
# Ensure CloudTrail captures S3 data events for this bucket, then alert on GetObject/ListBucket
aws events put-rule --name decoy-bucket-access \
  --event-pattern '{"detail":{"eventSource":["s3.amazonaws.com"],"requestParameters":{"bucketName":["acme-prod-db-backups-2026"]}}}'

2C. AWS — decoy secret

aws secretsmanager create-secret --name prod/db/master-password \
  --secret-string '{"username":"dbadmin","password":"DECOY-DO-NOT-USE"}' \
  --tags Key=deception,Value=true
# Alert on GetSecretValue for this secret via EventBridge -> SNS

3A. Azure — honeytoken watchlist + decoy service principal

Microsoft Sentinel natively supports honeytokens via a Watchlist of the HoneyTokens template; tagged decoy accounts/secrets raise analytics alerts on use. Create a permission-less decoy app registration / service principal, then add its identifiers to the HoneyTokens watchlist and enable the related analytics rules. Microsoft Defender for Cloud and Entra ID Protection surface anomalous sign-ins to the decoy identity.

3B. Azure — honey storage + Key Vault decoy secret

Create a decoy Storage account and Key Vault, enable diagnostic logging to the Sentinel workspace, store a decoy secret, and write an analytics rule that fires on any data-plane read of the decoy resources.

4A. GCP — decoy service account + honey GCS bucket

Create a service account with no role bindings (permission-less), generate a key to plant, and alert on its use via Cloud Audit Logs:

gcloud iam service-accounts create svc-billing-export \
  --display-name="billing-export"
gcloud iam service-accounts keys create decoy-key.json \
  --iam-account=svc-billing-export@PROJECT.iam.gserviceaccount.com   # plant this key
gsutil mb -b on gs://acme-finance-exports-2026

Create a log-based metric + alerting policy in Cloud Monitoring that triggers on any audit-log entry where the principal is the decoy service account or the resource is the honey bucket.

5. Centralize and de-duplicate

Route all clouds' decoy alerts to one SOC pipeline. Tag each alert as DECEPTION/high-fidelity so it bypasses normal noise filtering and triggers an IR playbook rather than a triage queue.

6. Validate (red-team the decoys)

Have an authorized tester use each decoy (read the bucket, call with the key, fetch the secret) and confirm an alert lands end-to-end within target latency. A decoy you have not tested is assumed broken.

7. Maintain realism and rotate

Refresh decoy names, secrets, and pocket-litter periodically so they age with the real environment. Track every decoy in an inventory so they are never mistaken for real assets during audits or cleanups.

Key Concepts

ConceptDefinition
Decoy / honey resourceA cloud object created solely to be touched by an attacker; no legitimate user has any reason to use it.
Canary access keyA planted credential whose use generates an audit-log event; carries deny-all permissions.
High-fidelity alertA near-zero-false-positive signal because legitimate workflows never reference the decoy.
Permission-less principalA decoy IAM user/role/service principal/service account with explicit deny-all or no role bindings.
Data eventCloud audit logging of object/data-plane access (e.g., S3 GetObject), required to detect storage decoys.
Pocket litterPlausible supporting artifacts (fake configs, env files, wiki entries) that make a decoy credible.
Decoy inventoryThe authoritative internal record distinguishing decoys from real assets.

Tools & Systems

  • AWS — IAM (decoy users/roles), S3 (honey buckets, data events), Secrets Manager / SSM Parameter Store (decoy secrets), CloudTrail, EventBridge, SNS/Lambda, GuardDuty (correlate anomalous use).
  • Azure — Microsoft Entra ID (decoy app registrations / service principals), Storage / Key Vault decoys, Microsoft Sentinel HoneyTokens watchlist and analytics rules, Microsoft Defender for Cloud, Entra ID Protection.
  • GCP — IAM service accounts (decoys), Cloud Storage (honey buckets), Secret Manager (decoy secrets), Cloud Audit Logs, log-based metrics + Cloud Monitoring alerting, Pub/Sub.
  • Open-source / managed honeytoken systems — Canarytokens (https://canarytokens.org offers AWS API key tokens), Thinkst Canary, SpaceSiren / SpaceCrab (self-hosted AWS honey-token frameworks).
  • SIEM/SOAR — to centralize alerts across clouds and drive an IR playbook on any decoy hit.

Common Scenarios

  • Credential-theft / code-leak detection. Plant a canary AWS key in CI variables, an env file, and a private repo. Any external use (even from a leaked public push) fires within minutes.
  • Crown-jewel data store. Stand up a honey "backups" bucket next to the real one; attackers enumerating storage hit the decoy first and reveal themselves.
  • Cloud lateral movement. A permission-less decoy service principal that "looks" privileged catches adversaries assuming roles during pivoting.
  • Secrets harvesting. Decoy entries in Secrets Manager / Key Vault / Secret Manager detect tools scraping the secrets store.
  • Migrating from on-prem-only deception. Mirror the existing on-prem decoy strategy into the cloud control plane so coverage follows workloads.

Output Format

Produce a Cloud Deception Deployment Record using assets/template.md, containing:

  1. Decoy inventory — per decoy: cloud, type, plausible name, real placement location of any planted credential, internal deception tag/label, owner.
  2. Detection wiring — per decoy: audit-log source → rule/pattern → alert sink → IR playbook reference, with the target alert latency.
  3. Least-privilege proof — evidence each decoy principal is deny-all / no-role-binding.
  4. Validation results — date tested, who tested, end-to-end latency observed, pass/fail.
  5. Maintenance plan — rotation cadence and review owner.

Use scripts/process.py to render the deployment record and a per-decoy detection checklist from a decoy-inventory JSON, and to flag decoys missing detection wiring or validation.

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

Assess exposure of Windows secrets protected by DPAPI, the operating system’s data protection service, during authorized security tests. Guide examination and decryption of saved credentials and browser data using available user sessions, credentials, or backup keys.

中文简介正在准备中。当前显示英语简介。

  • Assessing saved credential exposure in authorized tests
  • Examining collected DPAPI files offline
  • Reviewing protected browser and RDP credentials
mukul975/Anthropic-Cybersecurity-Skills3.5万2026年8月31日 更新

Assess Shadow Credentials account takeover during authorized Active Directory testing. Examine permissions that allow public key registration, validate authentication without a password reset, and document credential cleanup, detection opportunities, and access control changes needed for remediation.

中文简介正在准备中。当前显示英语简介。

  • Assessing risky permissions identified in BloodHound
  • Validating account takeover without resetting a password
  • Generating directory change records during security exercises
mukul975/Anthropic-Cybersecurity-Skills3.5万2026年8月31日 更新

Prepare for CMMC Level 2, a cybersecurity certification for defense contractors. Define the systems handling controlled information, review security requirements, calculate the SPRS assessment score, and organize remediation plans and evidence into a readiness report.

中文简介正在准备中。当前显示英语简介。

  • Define the assessment scope for systems handling controlled information
  • Calculate an SPRS score and prioritize unmet security requirements
  • Prepare security plans, remediation milestones, and assessment evidence
mukul975/Anthropic-Cybersecurity-Skills3.5万2026年8月31日 更新

Create disk images for investigations using dd or dcfldd on Linux. The workflow covers write protection, bit-for-bit copying, hash checks to verify integrity, and documentation of the acquisition process.

中文简介正在准备中。当前显示英语简介。

  • Preserving a suspect drive before investigation
  • Creating evidence copies of USB devices or memory cards
  • Verifying that a disk image matches its source
mukul975/Anthropic-Cybersecurity-Skills3.5万2026年8月31日 更新

Review Active Directory permissions with ldap3, a Python library for directory queries. Identify excessive access granted to non-administrative accounts, explain potential privilege escalation paths, and produce a JSON report with affected objects and remediation recommendations.

中文简介正在准备中。当前显示英语简介。

  • Auditing excessive Active Directory permissions
  • Investigating privilege escalation paths from ordinary accounts
  • Reporting affected objects and permission remediation recommendations
mukul975/Anthropic-Cybersecurity-Skills3.5万2026年8月31日 更新

Inspect suspicious Android APK files without running them. Analyze permissions, app components, suspicious code patterns, and network indicators, then produce a JSON risk report to support malware triage and detection rule development.

中文简介正在准备中。当前显示英语简介。

  • Triage suspicious APK files without running them
  • Inspect dangerous permission combinations and suspicious API calls
  • Extract network indicators for malware detection rules
mukul975/Anthropic-Cybersecurity-Skills3.5万2026年8月31日 更新

查看 mukul975 的全部技能

报告此技能的问题