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Waf

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Security
  • Apache-2.0
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From the author

Configures AWS WAF to filter web traffic: creating web access control lists (web ACLs) on CloudFront, Application Load Balancers, API Gateway, and AppSync; AWS Managed Rules tuned in Count mode; rate-based rules for HTTP floods; IP set and geographic match rules; Bot Control (Common and Targeted); turning bot labels into a confidence signal; stripping spoofed inbound x-amzn-waf-* headers; recovering the real client IP behind a CDN; Fraud Control (account takeover and account creation fraud prevention); and logging and request sampling. Use when the user wants to protect a web application or API from common exploits, bots, credential stuffing, fake-account creation, or HTTP floods at the application layer (layer 7). Routes to the right per-task procedure in references. Do NOT use for L3/L4 DDoS protection (shieldadvanced skill), multi-account WAF rollout (firewallmanager skill), CloudFront configuration (cloudfront skill), or Route 53 health checks or records (route53 skill).
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as a central router for configuring AWS WAF to secure web applications and APIs. It provides structured procedures for creating web ACLs, managing rule sets, implementing bot and fraud control, and configuring logging. The skill emphasizes a secure-by-design approach, requiring logging to be enabled before tuning and enforcing strict header-stripping to prevent spoofing. It covers the full lifecycle of WAF management, from initial setup and association with resources like CloudFront and ALB to advanced mitigation strategies for volumetric abuse and credential stuffing.

What it does

  • Creates and associates web ACLs with CloudFront, ALB, API Gateway, and AppSync.
  • Configures AWS Managed Rules, rate-based rules, and custom IP or geographic match rules.
  • Implements Bot Control and Fraud Control to prevent account takeover and fake-account creation.
  • Provides workflows for logging, request sampling, and tuning rules in Count mode.
  • Enforces security best practices like header stripping and least-privilege IAM.
  • Routes users to specific, self-contained procedures for complex WAF tasks.

When to use it

  • When you need to protect a web application or API from layer 7 exploits and HTTP floods.
  • When implementing bot detection or fraud prevention for login and signup endpoints.
  • When you need to tune managed rules to reduce false positives without blocking legitimate traffic.
  • When you need to recover the real client IP or secure forwarded headers behind a CDN.
  • When you need to manage AI and LLM crawler traffic.

Advantages

  • Reduces risk of misconfiguration by enforcing a logical, step-by-step tuning workflow.
  • Prevents accidental blocking of legitimate traffic through mandatory Count-mode tuning.
  • Ensures security consistency by mandating header-stripping when forwarding signals.
  • Provides clear guidance on WCU budget management and rule group selection.
  • Centralizes complex WAF procedures into a single, reliable reference source.

Usage examples

“How do I set up AWS WAF to block HTTP floods on my Application Load Balancer?”
The agent will guide you through creating a regional web ACL, associating it with your ALB, and configuring a rate-based rule to throttle requests based on IP or session token.
“I am seeing false positives from the Core Rule Set. How do I fix this?”
The agent will instruct you to check your WAF logs to identify the specific rule ID triggering the block, then use the RuleActionOverrides feature to set that specific rule to Count mode.
“How can I protect my login page from credential stuffing?”
The agent will guide you through enabling Fraud Control (Account Takeover Prevention) and configuring the necessary logging and signal-forwarding rules.
“I need to allow traffic only from specific countries. What is the procedure?”
The agent will provide the steps to create a geographic match rule and configure the web ACL's default action to block traffic outside of your allowed list.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/aws/waf/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip waf.zip -d .agents/skills

Documentation by AWS

AWS WAF

Overview

Domain expertise for configuring AWS WAF, the web application firewall that filters HTTP and HTTPS traffic to CloudFront distributions, Application Load Balancers, API Gateway REST APIs, and AppSync GraphQL APIs. Covers web ACL creation and association, AWS Managed Rules, rate-based rules, match rules (IP set and geographic), Bot Control and the signal-forwarding workflows built on top of it, Fraud Control for logins and signups, AI and LLM crawler management, and the logging that every tuning workflow depends on.

This skill is a router. Each customer task maps to a procedure file under references/. Read the matching reference in full before acting, then follow its constraints and steps. The reference files are self-contained: each carries its own decision tables, constraints, procedure, and troubleshooting.

Execute commands using the AWS MCP server when connected (sandboxed execution, audit logging, observability). Fall back to the AWS CLI otherwise. A web ACL's scope is fixed at creation: a CloudFront web ACL must be created in us-east-1 with CLOUDFRONT scope, while a regional web ACL (Application Load Balancer, API Gateway, AppSync) is created in the resource's Region with REGIONAL scope.

Which WAF task do you need?

GoalReference
Create a web ACL and attach it to a resourcecreating a web ACL and associating it with a resource
Set up logging and sampling before tuning anythingsetting up logging and request sampling
Add AWS Managed Rules and tune false positivesadding managed rules and tuning with count mode
Throttle HTTP floods and brute forceadding rate-based rules
Allow or block by IP range or countryusing ip sets and geographic match rules
Detect and control bots (the on-ramp)protecting against bots with bot control
Collapse bot labels into one confidence signalturning bot control labels into a confidence signal
Forward all signals to the origin with one ruleforwarding signals with dynamic label interpolation
Decide what the app does with the forwarded signaladaptive mitigation playbook for forwarded signals
Stop attackers from spoofing forwarded headersstripping inbound waf headers before trusting them
Recover the real client IP behind a CDNrecovering the real client ip behind a cdn
Protect logins and signups from fraudprotecting logins and signups with fraud control
See and manage AI and LLM crawler trafficseeing and managing ai crawler traffic

Routing notes

  • Logging comes before tuning. Every Count-mode tuning workflow assumes logging and request sampling are on. If the customer has not set up logging, run that reference first; otherwise Count-mode tuning has nothing to read.

  • Web ACL scope is fixed at creation. A CloudFront web ACL is CLOUDFRONT scope in us-east-1; a regional resource needs a REGIONAL web ACL in its own Region. Scope cannot be changed later, so the creating reference settles it before anything is built.

  • Bot Control is a chain, not one task. Protecting against bots is the on-ramp (turn on, choose Common vs Targeted, observe). Turning labels into a confidence signal, forwarding that signal, and deciding what the application does with it are three separate references that build on it in that order. The header-stripping reference is the mandatory safety companion whenever a signal is forwarded to the origin.

  • Common vs Targeted is not a soft choice. Common only catches self-identifying bots and known-bad IPs. For login, checkout, or any high-value endpoint facing evasive bots, Targeted with the application integration SDK is required. The bots reference pushes Targeted for real bot threats rather than presenting it as optional.

  • Rate limiting vs Fraud Control. Rate-based rules blunt volumetric HTTP floods. Credential stuffing and fake-account creation are account-based abuse that rate limiting misses; those go to the Fraud Control reference (ATP and ACFP), not the rate-based reference.

  • Forwarded headers need the strip rule. Any time the customer forwards a signal or the client IP to the origin in x-amzn-waf-* headers, the inbound-header-stripping reference is required to prevent spoofing. The confidence-signal, interpolation, and client-IP references all point at it.

  • What lives in other skills. L3/L4 DDoS protection and Shield cost-protection credits are the shieldadvanced skill. Multi-account WAF rollout is the firewallmanager skill. CloudFront and Application Load Balancer configuration are their own skills. This skill builds the WAF rules; it does not configure the resources it protects.

Security Considerations

AWS WAF is itself a security control, so misconfiguration directly weakens an application's defenses. Apply these across every reference:

  • Least-privilege IAM. You MUST grant only the specific wafv2: actions a task needs (for example wafv2:CreateWebACL, wafv2:GetWebACL, wafv2:UpdateWebACL, wafv2:AssociateWebACL, wafv2:PutLoggingConfiguration) rather than wafv2:* or the AWSWAFFullAccess managed policy.

  • Ephemeral credentials. You MUST use IAM roles with temporary credentials (such as an EC2 instance profile, SSO session, or aws sts assume-role) rather than long-lived IAM user access keys when running these WAF CLI commands.

  • Monitor configuration changes. You SHOULD enable AWS CloudTrail on wafv2 management events and set CloudWatch alarms on critical web ACL configuration changes (such as DeleteWebACL and UpdateWebACL rule removals) and on the web ACL's BlockedRequests and CountedRequests metrics, so rule changes and sudden spikes in blocked or counted traffic are detected.

  • Misconfiguration opens access. A web ACL that is created but never associated, or one whose default action is left at Allow with no enforcing rules, filters nothing. You MUST confirm the web ACL is associated and that its posture matches the intended default (block vs allow) before reporting setup complete.

  • Protect log destinations. Logs can capture credentials and session data. You MUST redact sensitive fields (such as the authorization header and cookie) and MUST enable encryption at rest on the log destination (CloudWatch Logs, Amazon S3, or Amazon Data Firehose).

  • Header-spoofing risk. Any x-amzn-waf-* signal forwarded to the origin can be forged inbound. You MUST add the inbound-header-stripping rule whenever a signal or client IP is forwarded (see stripping-inbound-waf-headers-before-trusting-them).

Additional Resources

Files

references/seeing-and-managing-ai-crawler-traffic.md

Seeing and Managing AI and LLM Crawler Traffic

Overview

Domain expertise for deciding, per AI scraper, whether to allow or block it in AWS WAF. Covers the AI activity visibility surface, the AI and large language model (LLM) bot labels Bot Control applies, category-based handling, and composing the AI labels into the existing bot confidence signal.

Does not cover turning Bot Control on (the protecting-against-bots reference) or the confidence signal chain in depth (its own references). This reference depends on Bot Control being enabled.

Execute commands using the AWS MCP server when connected (sandboxed execution, audit logging, observability). Fall back to the AWS CLI otherwise.

Table of Contents

  • Overview

  • Workflow

  • See the AI traffic first

  • Handle AI traffic by category

  • Compose into the confidence signal

  • Troubleshooting

  • Procedure

  • Security Considerations

  • Additional Resources

Workflow

To see and manage AI crawler traffic end to end, follow the procedure exactly. See the Procedure section below.

The procedure covers:

  • Using the AI activity surface to see which AI and LLM crawlers are arriving

  • Reading the AI and LLM bot labels

  • Handling that traffic by category (allow wanted crawlers, block or challenge unwanted ones)

  • Optionally composing the AI labels into the existing confidence signal

See the AI traffic first

Customers want to allow some AI crawlers and block others but cannot see the AI traffic, so they cannot make the decision.

Constraints:

  • You MUST surface the AI activity view and the AI and LLM bot labels so the customer can see which crawlers arrive before deciding on each

  • You SHOULD confirm Bot Control is enabled, since the AI labels come from it

Handle AI traffic by category

Customers treat all AI crawler traffic as one block-or-allow decision when they want different handling per crawler.

Constraints:

  • You MUST handle AI traffic by category using the bot labels, so the customer can allow wanted crawlers and block or challenge unwanted ones, rather than a single blanket decision

Compose into the confidence signal

A customer already forwarding a bot confidence signal can feed AI labels into the same chain rather than building a separate path.

Constraints:

  • You SHOULD compose the AI labels into the existing confidence signal (see turning-bot-control-labels-into-a-confidence-signal) rather than building a separate AI path

  • You MUST NOT duplicate the forwarding mechanism for AI traffic when a confidence signal already exists

Troubleshooting

The customer cannot see which AI crawlers are arriving

The AI activity surface or AI labels are not in view, or Bot Control is not enabled. Enable Bot Control and use the AI activity view (See the AI traffic first).

AI handling is all-or-nothing

The customer is making one blanket decision. Handle by category using the labels (Handle AI traffic by category).

Procedure

Overview

This procedure surfaces AI crawler traffic, handles it by category, optionally composes it into the confidence signal, and surfaces the console link.

Parameters
  • web_acl_name, web_acl_id, scope (required): Identify the web ACL.

  • category_handling (required): Which AI or LLM categories to allow, block, or challenge.

Constraints for parameter acquisition:

  • You MUST ask for all required parameters upfront in a single prompt

  • You MUST confirm Bot Control is enabled and producing AI labels

Steps
1. Verify dependencies

Constraints:

  • You MUST confirm credentials with aws sts get-caller-identity

  • You MUST confirm Bot Control is enabled and AI labels are present

2. Review AI traffic and add category handling

Constraints:

  • You MUST review the AI activity and labels before deciding

  • You MUST add label-match rules handling the AI categories per the customer's decision, fetching the current LockToken before update-web-acl and passing the full rule set

  • You MUST preserve the web ACL's existing DefaultAction from the get-web-acl response and pass it back as {default_action}; do not assume Allow={}, since that would silently open all unmatched traffic on a web ACL whose default action is Block. For example, a Block rule on an unwanted AI crawler label:

    aws wafv2 get-web-acl --name {web_acl_name} --scope {scope} --id {web_acl_id} --region {region}
    aws wafv2 update-web-acl --name {web_acl_name} --scope {scope} --id {web_acl_id} \
      --lock-token {lock_token} --default-action {default_action} \
      --rules '[{"Name":"BlockUnwantedAICrawler","Priority":1,"Action":{"Block":{}},"Statement":{"LabelMatchStatement":{"Scope":"LABEL","Key":"awswaf:managed:aws:bot-control:bot:category:ai"}},"VisibilityConfig":{"SampledRequestsEnabled":true,"CloudWatchMetricsEnabled":true,"MetricName":"BlockUnwantedAICrawler"}}]' \
      --visibility-config SampledRequestsEnabled=true,CloudWatchMetricsEnabled=true,MetricName={web_acl_name} \
      --region {region}
3. Optionally compose into the confidence signal and surface the console link

Constraints:

  • You SHOULD feed the AI labels into the existing confidence signal rather than a separate path

  • You MUST present the web ACL console link and tell the customer to confirm the AI handling rules:

    https://us-east-1.console.aws.amazon.com/wafv2/homev2/web-acls?region={region}
Example
Example input
{
  "web_acl_name": "example-webacl",
  "web_acl_id": "abc",
  "scope": "REGIONAL",
  "category_handling": {"allow": ["wanted search AI"], "block": ["unwanted scraper AI"]}
}
Example output
Reviewed AI crawler labels in the AI activity view.
Allowed the wanted crawler category, blocked the unwanted one, and fed the labels into the existing confidence signal.
Open the web ACL and confirm the AI handling rules:
https://us-east-1.console.aws.amazon.com/wafv2/homev2/web-acls?region=us-east-1
Troubleshooting
Cannot see AI crawlers

Enable Bot Control and use the AI activity view (Step 1).

Handling is all-or-nothing

Add per-category label-match rules (Step 2).

Security Considerations

This procedure modifies a security control, so misconfiguration directly weakens the application's defenses.

  • Least-privilege IAM. You MUST grant only the specific wafv2: actions a task needs (for example wafv2:GetWebACL and wafv2:UpdateWebACL) rather than wafv2:* or the AWSWAFFullAccess managed policy.

  • Ephemeral credentials. You MUST use IAM roles with temporary credentials (such as an EC2 instance profile, SSO session, or aws sts assume-role) rather than long-lived IAM user access keys when running these WAF CLI commands.

  • Monitor configuration changes. You SHOULD enable AWS CloudTrail on wafv2 management events and set CloudWatch alarms on critical web ACL configuration changes (such as DeleteWebACL and UpdateWebACL rule removals) and on the web ACL's BlockedRequests and CountedRequests metrics, so rule changes and sudden spikes in blocked or counted traffic are detected.

Additional Resources

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

Security

No issues found by our static scan· No issues found

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Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · skills/specialized-skills/networking-and-content-delivery-skills/waf · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download waf.zip