external/anthropic-cybersecurity-skills/skills/implementing-cloud-waf-rules/SKILL.md
This skill covers deploying and tuning Web Application Firewall rules on AWS WAF, Azure WAF, and Cloudflare to protect cloud-hosted applications against OWASP Top 10 attacks. It details configuring managed rule sets, creating custom rules for business logic protection, implementing rate limiting, deploying bot management, and reducing false positives through rule tuning and logging analysis.
npx skillsauth add seikaikyo/dash-skills implementing-cloud-waf-rulesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Do not use for network-level DDoS protection (use AWS Shield or Azure DDoS Protection), for API authentication design (see managing-cloud-identity-with-okta), or for application code-level security fixes (WAF is a compensating control, not a replacement for secure code).
Enable cloud provider managed rule sets that cover OWASP Top 10 vulnerabilities. Start in Count (detection) mode before switching to Block (prevention) mode.
# AWS WAF: Create Web ACL with AWS Managed Rules
aws wafv2 create-web-acl \
--name production-waf \
--scope REGIONAL \
--default-action '{"Allow": {}}' \
--visibility-config '{
"SampledRequestsEnabled": true,
"CloudWatchMetricsEnabled": true,
"MetricName": "production-waf"
}' \
--rules '[
{
"Name": "AWSManagedRulesCommonRuleSet",
"Priority": 1,
"Statement": {
"ManagedRuleGroupStatement": {
"VendorName": "AWS",
"Name": "AWSManagedRulesCommonRuleSet"
}
},
"OverrideAction": {"Count": {}},
"VisibilityConfig": {
"SampledRequestsEnabled": true,
"CloudWatchMetricsEnabled": true,
"MetricName": "CommonRuleSet"
}
},
{
"Name": "AWSManagedRulesSQLiRuleSet",
"Priority": 2,
"Statement": {
"ManagedRuleGroupStatement": {
"VendorName": "AWS",
"Name": "AWSManagedRulesSQLiRuleSet"
}
},
"OverrideAction": {"Count": {}},
"VisibilityConfig": {
"SampledRequestsEnabled": true,
"CloudWatchMetricsEnabled": true,
"MetricName": "SQLiRuleSet"
}
},
{
"Name": "AWSManagedRulesKnownBadInputsRuleSet",
"Priority": 3,
"Statement": {
"ManagedRuleGroupStatement": {
"VendorName": "AWS",
"Name": "AWSManagedRulesKnownBadInputsRuleSet"
}
},
"OverrideAction": {"Count": {}},
"VisibilityConfig": {
"SampledRequestsEnabled": true,
"CloudWatchMetricsEnabled": true,
"MetricName": "KnownBadInputs"
}
}
]'
Deploy rate-based rules to protect login endpoints against brute force and credential stuffing attacks.
# Rate limiting rule for login endpoint (100 requests per 5 minutes per IP)
aws wafv2 update-web-acl \
--name production-waf \
--scope REGIONAL \
--id <web-acl-id> \
--lock-token <lock-token> \
--default-action '{"Allow": {}}' \
--rules '[
{
"Name": "RateLimitLogin",
"Priority": 0,
"Statement": {
"RateBasedStatement": {
"Limit": 100,
"AggregateKeyType": "IP",
"ScopeDownStatement": {
"ByteMatchStatement": {
"FieldToMatch": {"UriPath": {}},
"PositionalConstraint": "STARTS_WITH",
"SearchString": "/api/auth/login",
"TextTransformations": [{"Priority": 0, "Type": "LOWERCASE"}]
}
}
}
},
"Action": {"Block": {"CustomResponse": {"ResponseCode": 429}}},
"VisibilityConfig": {
"SampledRequestsEnabled": true,
"CloudWatchMetricsEnabled": true,
"MetricName": "RateLimitLogin"
}
}
]'
Block traffic from countries where the application has no legitimate users and leverage IP reputation lists to block known malicious sources.
# AWS WAF: Geo-blocking rule
# Block countries not in the allowed list
aws wafv2 create-ip-set \
--name blocked-ips \
--scope REGIONAL \
--ip-address-version IPV4 \
--addresses "198.51.100.0/24" "203.0.113.0/24"
# Add Amazon IP Reputation rule
# AWSManagedRulesAmazonIpReputationList blocks IPs flagged by AWS threat intelligence
Analyze WAF logs in Count mode to identify legitimate requests being flagged. Create rule exceptions for specific URI paths or request patterns.
# Enable WAF logging to S3
aws wafv2 put-logging-configuration \
--logging-configuration '{
"ResourceArn": "arn:aws:wafv2:us-east-1:123456789012:regional/webacl/production-waf/id",
"LogDestinationConfigs": ["arn:aws:s3:::waf-logs-bucket"],
"RedactedFields": [{"SingleHeader": {"Name": "authorization"}}]
}'
# Query WAF logs with Athena to find false positives
# Find rules triggered most frequently for legitimate traffic
cat << 'EOF' > waf-analysis.sql
SELECT
terminatingRuleId,
httpRequest.uri,
httpRequest.httpMethod,
COUNT(*) as block_count
FROM waf_logs
WHERE action = 'BLOCK'
AND timestamp > date_add('day', -7, now())
GROUP BY terminatingRuleId, httpRequest.uri, httpRequest.httpMethod
ORDER BY block_count DESC
LIMIT 20
EOF
# Exclude specific rule from managed rule set that causes false positives
# Example: Exclude SizeRestrictions_BODY for file upload endpoint
aws wafv2 update-web-acl \
--name production-waf \
--scope REGIONAL \
--id <web-acl-id> \
--lock-token <lock-token> \
--rules '[{
"Name": "AWSManagedRulesCommonRuleSet",
"Priority": 1,
"Statement": {
"ManagedRuleGroupStatement": {
"VendorName": "AWS",
"Name": "AWSManagedRulesCommonRuleSet",
"ExcludedRules": [{"Name": "SizeRestrictions_BODY"}]
}
},
"OverrideAction": {"None": {}},
"VisibilityConfig": {
"SampledRequestsEnabled": true,
"CloudWatchMetricsEnabled": true,
"MetricName": "CommonRuleSet"
}
}]'
After 7-14 days of Count mode with acceptable false positive rates, switch managed rules to Block mode for active protection.
# Change OverrideAction from Count to None (use rule group's default Block action)
# Update each managed rule group from {"Count": {}} to {"None": {}}
# Monitor CloudWatch metrics for sudden changes in blocked request volume
| Term | Definition | |------|------------| | Web ACL | Web Access Control List defining the set of rules evaluated against every HTTP request to a protected resource | | Managed Rule Group | Pre-configured rule set maintained by the cloud provider or third-party vendor covering common attack patterns | | Rate-Based Rule | WAF rule that tracks request rates per IP address and blocks IPs exceeding the threshold within a time window | | Count Mode | WAF action that logs matching requests without blocking them, used for rule validation before enforcement | | Rule Priority | Numerical ordering determining which rules are evaluated first; lower numbers have higher priority | | Custom Response | WAF capability to return specific HTTP status codes and headers when blocking requests | | Scope-Down Statement | Condition that narrows a rate-based rule to specific URI paths, methods, or headers | | False Positive | Legitimate request incorrectly blocked by a WAF rule, requiring rule tuning or exclusion |
Context: An e-commerce application experiences 50,000 login attempts per hour from a botnet using stolen credential lists. The attacker rotates source IPs every few minutes to evade simple IP-based blocking.
Approach:
Pitfalls: Setting rate limits too aggressively blocks legitimate users behind shared NAT IPs. Blocking by User-Agent alone is easily bypassed by rotating agent strings.
Cloud WAF Configuration Report
================================
Web ACL: production-waf
Scope: Regional (us-east-1)
Protected Resources: ALB (arn:aws:elasticloadbalancing:...)
Report Date: 2025-02-23
RULE CONFIGURATION:
[P0] RateLimitLogin - BLOCK (100 req/5min/IP)
[P1] AWSManagedRulesCommon - BLOCK (1 exclusion: SizeRestrictions_BODY)
[P2] AWSManagedRulesSQLi - BLOCK
[P3] AWSManagedRulesKnownBad - BLOCK
[P4] AWSManagedRulesBotControl - COUNT (evaluation phase)
[P5] GeoBlockRule - BLOCK (12 countries blocked)
TRAFFIC ANALYSIS (Last 7 Days):
Total Requests: 2,847,293
Allowed: 2,791,456 (98.0%)
Blocked: 51,234 (1.8%)
Counted: 4,603 (0.2%)
TOP BLOCKED RULES:
RateLimitLogin: 23,456 blocks (45.8%)
SQLi Detection: 8,234 blocks (16.1%)
CommonRuleSet (XSS): 7,891 blocks (15.4%)
GeoBlockRule: 6,543 blocks (12.8%)
KnownBadInputs: 5,110 blocks (10.0%)
FALSE POSITIVE ANALYSIS:
Reported False Positives: 3
Confirmed False Positives: 1 (SizeRestrictions_BODY for /api/upload)
Action Taken: Rule exclusion applied
development
拋棄式 HTML mockup 比稿:產出 2 到 3 個設計立場不同的變體(密度 / 版式 / 強調軸,不是換色),各附取捨說明,最後給有立場的對比結論。適用:「畫個草圖」「比較 A 版 B 版」「先看方向再做」「給我看幾種做法」。要 production 元件或設計已定案時不適用。
tools
需求不明時的意圖萃取訪談:一次一題、每題附上自己的猜測、聽出「真正想要 vs 覺得應該要」,直到能預測使用者反應(約 95% 信心)才動工。適用:需求缺少對象 / 動機 / 成功標準 / 約束,或使用者點名「訪談我」「先確認一下」「我們確定嗎」。明確自足的指示、純資訊查詢、機械性操作不適用。
development
對非平凡決策啟動新鮮 context 對抗審查(找碴不背書),在修正還便宜的時候抓出錯誤方向。適用:高風險改動(production、資安敏感邏輯、不可逆操作)、不熟的程式碼、要宣稱「這樣是安全的 / 可行的」之前。機械性操作與一行修改不適用。
testing
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