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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/protecting-against-bots-with-bot-control.md

Protecting Against Bots with Bot Control

Overview

Domain expertise for the AWS WAF Bot Control on-ramp: adding the Bot Control managed rule group, choosing Common versus Targeted, and observing in Count mode before enforcing. Covers the sharp difference between Common and Targeted, the application integration SDK as a precondition for Targeted, the machine learning warm-up, the verified-bot Count-override gotcha, and the added cost.

Does not cover what to do with the labels Bot Control produces; turning labels into a confidence signal, forwarding that signal, and the application's response are three separate references that build on this one.

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

  • Decision: Common vs Targeted

  • The SDK is a precondition for Targeted

  • Machine learning warm-up

  • Verified-bot Count-override gotcha

  • Added cost

  • Troubleshooting

  • Procedure

  • Security Considerations

  • Additional Resources

Workflow

To turn on Bot Control end to end, follow the procedure exactly. See the Procedure section below.

The procedure covers:

  • Adding the Bot Control rule group and choosing the inspection level

  • Running in Count mode to see how traffic is labeled

  • Keeping verified bots passing while tuning

  • Deciding which categories to block, challenge, or allow, then enforcing

This reference is the on-ramp. Turning the labels into an application decision is the turning-bot-control-labels-into-a-confidence-signal reference and the ones after it.

Decision: Common vs Targeted

LevelDetectsSDKBest for
CommonSelf-identifying bots (user-agent such as curl, python-requests, declared crawlers) and known-bad IPsNot requiredBasic filtering of honest bots
TargetedAdds behavioral machine learning, browser interrogation, and token session trackingStrongly requiredLogin, checkout, any high-value endpoint facing evasive bots

Constraints:

  • You MUST push Targeted for any real or evasive bot threat; Common alone is not meaningful protection against bots that impersonate a real browser (headless Chrome, Puppeteer, Selenium, residential proxies)

  • You MUST NOT present Targeted as an optional upgrade when the customer faces credential stuffing or inventory hoarding on a high-value endpoint

The SDK is a precondition for Targeted

Targeted's behavioral machine learning, browser interrogation, and token session tracking are largely blind without the application integration SDK or its JavaScript token.

Constraints:

  • You MUST treat the application integration SDK as a precondition for Targeted, not an optional add-on

  • You SHOULD confirm the SDK is integrated before relying on Targeted detection

Machine learning warm-up

The TGT_ML_* rules need up to roughly 24 hours to establish a traffic baseline. Customers enable Targeted, see nothing fire immediately, and assume it is broken.

Constraints:

  • You MUST set the expectation that TGT_ML_* rules need up to roughly 24 hours of warm-up before they act

  • You SHOULD advise against disabling Targeted during the warm-up window

Verified-bot Count-override gotcha

Bot Control does not block verified bots; it labels them. Overriding the whole rule group to Count while tuning also overrides the implicit Allow for verified bots, so they fall through to the customer's other rules.

Constraints:

  • You MUST add an explicit Allow rule on the awswaf:managed:aws:bot-control:bot:verified label when tuning the group in Count, so verified bots keep passing

  • You MUST place that Allow rule at a higher priority number than the Bot Control group, so the verified-bot label exists when the Allow rule runs

  • You SHOULD rely on the verified-bot labeling rather than blanket-blocking all automated traffic

Added cost

Bot Control incurs additional fees beyond the basic AWS WAF charges.

Constraints:

  • You MUST state the additional cost before the customer adds the rule group, not after it appears on the invoice

Troubleshooting

Evasive bots still get through on Common

Common only catches self-identifying bots and known-bad IPs. Move to Targeted with the SDK (Decision: Common vs Targeted).

Targeted fires nothing right after enabling

The TGT_ML_* rules are still warming up. Wait up to roughly 24 hours (Machine learning warm-up).

Verified bots get blocked while tuning

Overriding the whole group to Count canceled the verified-bot Allow. Add an explicit Allow on the verified label (Verified-bot Count-override gotcha).

Procedure

Overview

This procedure adds Bot Control at the chosen level in Count mode, keeps verified bots passing, then enforces, and surfaces the console link.

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

  • inspection_level (required): COMMON or TARGETED.

Constraints for parameter acquisition:

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

  • You MUST confirm logging is enabled and, for Targeted, that the application integration SDK is integrated

Steps
1. Verify dependencies

Constraints:

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

  • You MUST confirm logging and sampling are on

  • For Targeted, you MUST confirm the application integration SDK is in place

2. Add Bot Control in Count mode

Constraints:

  • You MUST add the Bot Control rule group at the chosen inspection level with the group in Count while observing

  • You MUST add an explicit Allow rule on awswaf:managed:aws:bot-control:bot:verified so verified bots keep passing during tuning

  • You MUST fetch the current LockToken before update-web-acl and pass 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, the Bot Control group at the chosen inspection level plus the verified-bot Allow rule:

    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":"AWS-BotControl","Priority":1,"Statement":{"ManagedRuleGroupStatement":{"VendorName":"AWS","Name":"AWSManagedRulesBotControlRuleSet","ManagedRuleGroupConfigs":[{"AWSManagedRulesBotControlRuleSet":{"InspectionLevel":"{inspection_level}"}}]}},"OverrideAction":{"Count":{}},"VisibilityConfig":{"SampledRequestsEnabled":true,"CloudWatchMetricsEnabled":true,"MetricName":"AWS-BotControl"}},{"Name":"AllowVerifiedBots","Priority":2,"Action":{"Allow":{}},"Statement":{"LabelMatchStatement":{"Scope":"LABEL","Key":"awswaf:managed:aws:bot-control:bot:verified"}},"VisibilityConfig":{"SampledRequestsEnabled":true,"CloudWatchMetricsEnabled":true,"MetricName":"AllowVerifiedBots"}}]' \
      --visibility-config SampledRequestsEnabled=true,CloudWatchMetricsEnabled=true,MetricName={web_acl_name} \
      --region {region}
3. Review and enforce

Constraints:

  • You MUST review the labeled traffic (allowing for the Targeted warm-up) before enforcing

  • You MUST decide per category whether to block, challenge, or allow, then enforce

Constraints:

  • You MUST present the web ACL console link and tell the customer to open the Bot Control rule and confirm its level and actions:

    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",
  "inspection_level": "TARGETED"
}
Example output
Confirmed the SDK is integrated. Added Bot Control TARGETED in Count, with an explicit Allow on the verified-bot label.
TGT_ML rules need up to ~24h to warm up before acting.
Open the web ACL and confirm the Bot Control level and actions:
https://us-east-1.console.aws.amazon.com/wafv2/homev2/web-acls?region=us-east-1
Troubleshooting
Evasive bots get through on Common

Move to Targeted with the SDK (Step 1).

Nothing fires right after enabling Targeted

The machine learning is warming up; wait up to roughly 24 hours (Step 3).

Verified bots blocked while tuning

Add the explicit Allow on the verified label (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

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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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