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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).

Use this Skill: https://skilld.dev/gh/aws/agent-toolkit-for-aws/waf

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referencesprotecting-against-bots-with-bot-control.md

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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

Level Detects SDK Best for
Common Self-identifying bots (user-agent such as curl, python-requests, declared crawlers) and known-bad IPs Not required Basic filtering of honest bots
Targeted Adds behavioral machine learning, browser interrogation, and token session tracking Strongly required Login, 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
4. Surface the console link

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

Source: SKILL.md on GitHub

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    This skill provides expert guidance for configuring AWS WAF to protect web applications. It incorporates comprehensive security best practices, such as enforcing least-privilege IAM access, using ephemeral credentials, and implementing mandatory header stripping to prevent spoofing. No malicious patterns or suspicious external references were detected.

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