Amazon Inspector is an automated vulnerability management service that continually scans Amazon Web Services (AWS) workloads for software vulnerabilities. The vulnerability management capabilities of Amazon Inspector are powered by an asset inventory engine known as the Amazon Inspector SBOM Generator (inspector-sbomgen), a standalone command-line tool that produces a software bill of materials (SBOM) from container images, directories, archives, local systems, compiled binaries, and more. Over the past two years, we’ve expanded inspector-sbomgen’s coverage across dozens of programming language ecosystems, operating systems, and widely deployed applications.

We’re pleased to announce a new capability for builders using inspector-sbomgen: a plugin system for writing your own custom package collectors that you can use right away, without requiring source code compilation nor waiting for an official release.

You can download the latest version of inspector-sbomgen from the Amazon Inspector User Guide.

In this post, we walk you through what the inspector-sbomgen plugin system does, why we built it, and how you can write your first plugin in a few minutes. Along the way, we also cover how plugin-generated package components integrate with Amazon Inspector for vulnerability scanning, and we explore the plugin safety model, which helps ensure security-hardened and predictable plugin behavior.

Software ecosystems are dynamic. New language package managers, lockfile formats, and end user applications ship constantly, and many are adopted quickly, in some cases with little security scrutiny. That leaves security teams with a visibility gap: production workloads running software that their SBOM tooling doesn’t yet recognize. Customers have asked us to inventory many of these ecosystems directly, and until recently, the only path to support was to open a feature request and wait for the inspector-sbomgen team to onboard the ecosystem and deploy a new release.

The inspector-sbomgen plugin system changes that. With plugins, you can:

• Onboard ecosystems that inspector-sbomgen doesn’t support out of the box. New open source ecosystems, niche or fast-moving package formats, and internal or proprietary tooling can all be inventoried without modifying inspector-sbomgen.
• Prototype detection for an ecosystem quickly. We designed a plugin system that is friendly to developers and AI coding assistants alike. Plugins are written in Lua, loaded at runtime, and require no Go toolchain nor compilation. You can use the built in test harness to iterate on a plugin and see results immediately.
• Build on a stable foundation. The plugin API abstracts away artifact-type differences, so you write your detection logic once and it works seamlessly across container images, archives, local systems, and more. And because plugins stay decoupled from the internals of sbomgen, the core tool’s regression surface stays small.

Internally, we’ve used the plugin system to ship new ecosystem coverage faster than before. In our 1.13 release, more than 20 ecosystems that were previously implemented in Go, including Apache Tomcat, NGINX, MySQL, Redis, WordPress, and the OpenSSH toolchain, are now embedded as plugins inside the sbomgen binary. The same release also added more than ten brand-new ecosystems as plugins, including Apache Cassandra, Apache Struts, Conda, Swift packages, and AI-agent collectors (Amazon Q Developer, Kiro CLI, Claude Code, GitHub Copilot, and Ollama).

Sbomgen plugins follow a two-step pipeline:

1. Discovery – Scan the artifact’s file system to identify files that contain installed package metadata.
2. Collection – Open each discovered file, parse file contents, and publish findings into the SBOM.

Under the hood, an event bus connects discovery and collection plugins. Discovery plugins publish events listing discovered files, and one or more collection plugins subscribe to these events, triggering package collection. Developers might recognize this behavior as the observer pattern.

This decoupling lets a single discovery plugin feed multiple collectors, for example, one extracting package metadata, another scanning for secrets, and another checking policy. Each collection plugin works from the same file list without re-walking the artifact filesystem, a computationally expensive operation.

Inspector-sbomgen makes it straightforward to bootstrap a plugin environment. The plugin new command tells sbomgen to create a new plugin workspace, and the —-with-example flag populates the workspace with a discovery-collection plugin pair, that you can run immediately.

inspector-sbomgen plugin new --with-example 

After invoking the preceding command, you will be prompted to provide a plugin name and a directory that will contain your plugin workspace. You can provide custom values or use the default values:

Plugin name (identifies the software ecosystem your plugin will inventory, e.g. debian-dpkg, rhel-rpm, python-pip, cmake) [my-custom-ecosystem]: <enter>
Project directory [my-sbomgen-plugins]: <enter>

Created plugin "my-custom-ecosystem" in my-sbomgen-plugins/

Note that you can skip interactive prompts by specifying the plugin name and directory using the corresponding command line interface (CLI) arguments:

inspector-sbomgen plugin new \
    --with-example \
    --name my-custom-ecosystem \
    --path my-sbomgen-plugins

After creating your plugin workspace, inspector-sbomgen will display a next steps screen, which guides developers and AI code assistants to the source files they need to change and to supporting documentation:

Next steps:

  Get started:
    1. Open plugin folder in a code editor (VS Code recommended)
    2. Add test files that your plugin will discover and parse
       (e.g., config files, lockfiles, binaries, etc.):
       my-sbomgen-plugins/discovery/cross-platform/extra-ecosystems/my-custom-ecosystem/_testdata/

  Develop:
    3. Edit discovery:    my-sbomgen-plugins/discovery/cross-platform/extra-ecosystems/my-custom-ecosystem/init.lua
    4. Edit collection:   my-sbomgen-plugins/collection/cross-platform/extra-ecosystems/my-custom-ecosystem/init.lua

  Test:
    5. Write unit tests:  my-sbomgen-plugins/discovery/cross-platform/extra-ecosystems/my-custom-ecosystem/init_test.lua
    6. Run unit tests:    inspector-sbomgen plugin test --path my-sbomgen-plugins

  Deploy:
    7. Distribute your plugin directory wherever you run inspector-sbomgen:
       inspector-sbomgen <arguments> --plugin-dir /path/to/my-sbomgen-plugins

       Example:
       inspector-sbomgen container --image alpine:latest -o /tmp/sbom.json --plugin-dir /path/to/my-sbomgen-plugins

For code completion, install the VS Code Lua language server extension:
  https://luals.github.io/#vscode-install

For more information:
  - Plugin guide:    my-sbomgen-plugins/docs/sbomgen-plugin-developer-guide.md
  - Testing guide:   my-sbomgen-plugins/docs/sbomgen-plugin-testing-guide.md
  - API reference:   my-sbomgen-plugins/docs/sbomgen-plugin-api-reference.md
  - Documentation:   https://docs.aws.amazon.com/inspector/latest/user/sbom-generator.html

Now that you have a plugin workspace, let’s explore its contents in greater detail:

tree my-sbomgen-plugins

├── AGENTS.md
├── collection
│   └── cross-platform
│       └── extra-ecosystems
│           └── my-custom-ecosystem
│               └── init.lua
├── discovery
│   └── cross-platform
│       └── extra-ecosystems
│           └── my-custom-ecosystem
│               ├── _testdata
│               │   ├── empty
│               │   └── example.lock
│               ├── init_test.lua
│               └── init.lua
├── docs
│   ├── sbomgen-plugin-api-reference.md
│   ├── sbomgen-plugin-developer-guide.md
│   └── sbomgen-plugin-testing-guide.md
├── library
│   └── sbomgen.lua
└── README.md

The scaffolded project includes a working discovery and collection plugin pair, passing unit tests with test fixtures under _testdata/, a .vscode/settings.json for integrated development environment (IDE) integration, and a local copy of the developer documentation.

The scaffolding is deliberately succinct and complete, so it reads well for both humans and AI coding assistants. Every file has clear comments that explain what each function does and what the plugin author needs to fill in.

To test a plugin, you first need something to scan, such as a package lock file or a compiled binary. The example plugin inventories a fictional example.lock with the following contents:

my-package-alpha==1.0.0 
my-package-beta==2.3.1 
my-package-gamma==0.9.5 

The provided discovery plugin knows how to look for instances of example.lock within the artifact file system:

-- my-custom-ecosystem discovery plugin
-- Discovers example.lock files in the artifact file list.

function discover()
    return sbomgen.find_files_by_name({"example.lock"})
end

And the provided collection plugin knows how to parse the contents of example.lock and publish package findings to the output SBOM.

-- my-custom-ecosystem collection plugin
-- Parses example.lock files and extracts package name and version.

function collect(file_path)
    local content = sbomgen.read_file(file_path)
    if content == nil then
        return
    end

    for line in content:gmatch("[^\n]+") do
        local name, ver = line:match("^(.+)==(.+)$")
        if name and ver then
            sbomgen.push_package({
                name = name,
                version = ver,
                purl_type = "generic",
                namespace = "my-custom-ecosystem",
                component_type = sbomgen.component_types.APPLICATION,
            })
        end
    end
end

Plugins ship with a built-in test framework so you can validate your logic before scanning a real artifact. Tests are written in Lua, live next to the plugin in init_test.lua, and reference fixture data in _testdata/:

function test_discovers_packages() 
    local result = testing.scan_directory("_testdata") 
    testing.assert_equals(3, #result.findings) 
    testing.assert_equals("my-package-alpha", result.findings[1].name) 
    testing.assert_equals("1.0.0", result.findings[1].version) 
end 
 
function test_no_findings_for_empty_directory() 
    local result = testing.scan_directory("_testdata/empty") 
    testing.assert_equals(0, #result.findings) 
end

Run the tests with the following command:

inspector-sbomgen plugin test --path my-sbomgen-plugins -v

=== RUN   my-custom-ecosystem/discovery/init_test/test_discovers_packages 
--- PASS: my-custom-ecosystem/discovery/init_test/test_discovers_packages (0.04s) 
=== RUN   my-custom-ecosystem/discovery/init_test/test_no_findings_for_empty_directory 
--- PASS: my-custom-ecosystem/discovery/init_test/test_no_findings_for_empty_directory (0.04s) 
ok    2 tests passed 

This is the tightest development loop we could design: no Go toolchain, no rebuild, no container spin-up. Write a test, run it, iterate.

For plugins to produce findings, inspector-sbomgen needs an artifact that contains the files your plugin looks for. For the example plugin, any directory with an example.lock file works. The fixture we generated earlier is a good stand-in:

inspector-sbomgen directory \ 
    --plugin-dir ./my-sbomgen-plugins \ 
    --path ./my-sbomgen-plugins/discovery/cross-platform/extra-ecosystems/my-custom-ecosystem/_testdata \ 
    -o sbom.json 

The --plugin-dir flag tells inspector-sbomgen where to load your Lua plugins from. The resulting SBOM contains a CycloneDX component for each of the three packages in example.lock, for example:

{
  "bom-ref": "comp-2",
  "type": "application",
  "name": "my-package-alpha",
  "version": "1.0.0",
  "scope": "optional",
  "purl": "pkg:generic/my-sbomgen-plugin/my-package-alpha@1.0.0",
  "properties": [
    {
      "name": "amazon:inspector:sbom_generator:source_path",
      "value": "./my-sbomgen-plugins/example.lock"
    }
  ]
}

Every plugin-generated component carries an amazon:inspector:sbom_generator:source_path property that records the file the component was collected from, so you can always trace a component back to the artifact that produced it.

Plugin-generated findings are first-class SBOM components. They work with every downstream consumer that reads CycloneDX SBOMs, including Amazon Inspector. To send an SBOM to Amazon Inspector for vulnerability analysis, add the --scan-sbom flag (this requires an active AWS account):

inspector-sbomgen directory \ 
    --path ./my-sbomgen-plugins/discovery/cross-platform/extra-ecosystems/my-custom-ecosystem/_testdata \ 
    --plugin-dir ./my-sbomgen-plugins \ 
    --scan-sbom \ 
    --aws-profile your_profile \ 
    --aws-region your_region \ 
    -o /tmp/sbom.json 

An important caveat when you onboard a brand-new ecosystem: Plugin authors can inventory arbitrary ecosystems, but Amazon Inspector can only report vulnerabilities for components it has advisories for. When you point Amazon Inspector at a component whose ecosystem isn’t in its advisory feeds yet, Inspector will return the component with a property, Component skipped: no supported rules found. For example:

{ 
  "bom-ref": "comp-1", 
  "name": "my-package-alpha", 
  "properties": [ 
    { 
      "name": "amazon:inspector:sbom_scanner:path", 
      "value": "my-sbomgen-plugins/discovery/cross-platform/extra-ecosystems/my-custom-ecosystem/_testdata/example.lock" 
    }, 
    { 
      "name": "amazon:inspector:sbom_scanner:info", 
      "value": "Component skipped: no supported rules found." 
    } 
  ], 
  "purl": "pkg:generic/my-custom-ecosystem/my-package-alpha@1.0.0", 
  "type": "application", 
  "version": "1.0.0" 
} 

This is expected behavior, not an error. The SBOM is still generated correctly, the component is still tracked, and the source_path tells you exactly which file produced it. If and when Amazon Inspector adds advisory coverage for the ecosystem, the same SBOM will start producing vulnerability findings without any change to your plugin. For ecosystems Inspector already supports, plugin-generated components are indistinguishable from components produced by built-in scanners.

We care about productivity and efficiency when writing plugins. Writing Lua without modern conveniences such as autocomplete isn’t fun, so every plugin project scaffolded with the plugin new command ships with a library/sbomgen.lua definition file and a .vscode/settings.json that automatically wires it up to the Lua Language Server extension for VS Code.

For code completion and IDE support, first install the sumneko.lua extension, open your plugin project in VS Code, and every sbomgen.* function will get:

• Parameter hints with types.
• Hover documentation.
• Autocomplete for constants (sbomgen.component_types.*, sbomgen.groups.*, sbomgen.platform.*).
• Type checking on function calls.
• Inline warnings when required fields are missing from push_package().

The same definition file makes plugin development work well with AI coding assistants. The types and documentation are embedded in a form that tools can read, so assistants can generate correct plugin code with far less monitoring than writing against a raw language would require.

Plugins run real code inside the same process as inspector-sbomgen, so we designed the execution environment to keep that code stable and security-hardened. Every Lua plugin runs in an isolated sandbox. Every Lua virtual machine (VM) has access to a restricted subset of the Lua standard library to ensure only safe operations are permitted:

• No direct filesystem access. The Lua io library isn’t loaded. All file operations go through sbomgen.* functions, which route through sbomgen’s internals so your plugin behaves identically whether it’s scanning a directory on disk, a container image, a compressed archive, or a mounted volume.
• No subprocess execution or environment mutation. The Lua os library is blocked, so plugins can’t spawn processes, modify environment variables, or touch files outside the artifact.
• No VM introspection. The Lua debug library is blocked.
• No unbounded code loading. dofile, loadfile, and loadstring are removed. require() is available but restricted to the plugin’s own directory tree, so plugins can share helper modules with themselves but cannot load code from other plugins or system paths.

If a plugin raises an unhandled Lua error, inspector-sbomgen logs a warning and continues with the next file or plugin; one faulty plugin does not prevent other plugins from running. Plugins never override inspector-sbomgen’s built-in package collectors. Every plugin must declare a unique name. If a custom plugin uses a name that’s already claimed by an official built-in plugin, the custom plugin is skipped with a warning. Built-in plugins always take precedence, so a custom plugin can never silently replace or shadow the tool’s own detection behavior.

To start building your own plugins today:

1. Install the latest inspector-sbomgen from the Amazon Inspector user guide.
2. Run inspector-sbomgen plugin new --with-example and follow the prompts.
3. Run inspector-sbomgen plugin test --path ./my-sbomgen-plugins -v to see the example tests pass.
4. Replace the example logic with detection for your own ecosystem.

The full reference documentation covers every function, constant, and command in depth:

• Lua plugin developer guide: plugin concepts, directory structure, and lifecycle.
• Lua plugin testing guide: test framework reference and fixture conventions.
• Lua plugin API reference: complete catalog of the sbomgen.* API surface.

Whether you’re adding support for an internal lockfile format, prototyping detection for a new open source ecosystem, or replacing a home-grown scanner with something your whole organization can run at scale, the plugin system is designed to make the path from idea to working SBOM as short as possible. We can’t wait to see what you build with it.
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

When an administrator introduces a rule change in AWS Network Firewall and network connectivity is disrupted, pinpointing the cause requires inspecting multiple points in the traffic path. The firewall gives you stateless and stateful rule engines, domain rules, and routing to the firewall endpoint inside your Amazon Virtual Private Cloud (Amazon VPC). A network drop looks the same from the workload no matter where it started. Isolating the cause means correlating the alert and flow logs with the firewall configuration, route tables, and recent API calls in AWS CloudTrail that might have changed them. That manual correlation is exactly where AWS DevOps Agent helps, accelerating root cause analysis so you can restore connectivity in minutes instead of hours.

AWS DevOps Agent does that correlation for you. As your always-available operations teammate, it resolves and proactively prevents operational issues across AWS, multicloud, and on-premises environments. When an Amazon CloudWatch alarm triggers, it reaches the agent through a webhook. The agent then reads the firewall configuration and logs through AWS APIs, ties the drop to recent API activity, and returns a root cause with a mitigation plan you review before you apply it.

This post connects CloudWatch monitoring to DevOps Agent. It walks through three Network Firewall failures from end to end. The first is a domain deny list blocking a legitimate endpoint. The second is a stateless rule priority misconfiguration. The third is an asymmetric cross Availability Zone (AZ) routing drop. Each maps to a different layer, so each leads down a different investigation path. An AWS Cloud Development Kit (AWS CDK) app deploys the whole environment in your own account so you can reproduce each failure and follow along.

As part of this blog post, we provide a CDK stack that deploys both the AWS DevOps Agent Space and a sample workload used to walk through three separate troubleshooting scenarios. A single t3.micro instance in a protected subnet checks its connectivity to a test endpoint on a continuous loop and publishes results to CloudWatch. Traffic takes the internet egress path through Network Firewall, the NAT gateway, and the internet gateway, so the firewall can intercept or drop it. After completing the walkthrough, you can apply the same troubleshooting techniques with DevOps Agent against your own Network Firewall deployments.

The test endpoint runs in a separate VPC deployed by the same CDK app. It serves HTTPS on port 443 and TCP on port 9142, giving each scenario a different protocol layer to exercise: Scenario 1 targets a TLS connection on 443 (matched by Server Name Indication), Scenario 2 targets a TCP connection on 9142, and Scenario 3 exercises the whole egress path.

A live status page shows one card per scenario plus the network topology. The whole stack deploys from a single CDK app across two Availability Zones, each with a firewall endpoint and NAT gateway, which is what makes Scenario 3 possible.

As shown in the following figure, the egress data path runs from the workload through Network Firewall and the NAT and internet gateways to the test endpoint. The alarm pipeline runs from CloudWatch through Amazon Simple Notification Service (Amazon SNS) and the webhook AWS Lambda function to DevOps Agent.

Figure 1: The sample workload

To use this with your own workload, you need a CloudWatch alarm that detects the connectivity problem and the webhook pipeline (SNS topic and Lambda function) that delivers it to DevOps Agent. The agent reads your firewall configuration, logs, and CloudTrail through AWS APIs, so no additional instrumentation is needed on the firewall side.

To follow along with this post, you need:

• An AWS account with permissions to deploy Amazon VPC, Network Firewall, Amazon Elastic Compute Cloud (Amazon EC2), CloudWatch, Amazon SNS, Lambda, Amazon CloudFront, and AWS Identity and Access Management (IAM) resources
• Access to AWS DevOps Agent, with permission to configure its webhook
• Node.js 18 or later and npm installed
• AWS Command Line Interface (AWS CLI) 2.x configured with credentials
• AWS CDK 2.x is required. You can use it through the project’s npx dependency, or install it globally:
  • npm install -g aws-cdk

Clone the project and deploy it into us-east-1 with one command (set awsRegion to use another AWS Region).

git clone https://github.com/aws-samples/sample-accelerating-aws-network-firewall-troubleshooting-with-aws-devops-agent.git
cd sample-accelerating-aws-network-firewall-troubleshooting-with-aws-devops-agent
bash scripts/deploy.sh

The script checks prerequisites, installs dependencies, compiles and tests, and bootstraps the CDK if needed. It then deploys all the stacks from a clean baseline and prints the outputs, including the status-page URL and sign-in details.

1. Open the status-page link (an https://<random-id>.cloudfront.net address).
2. Sign in using the username and password provided from the CDK output and confirm all three cards show the green Healthy status.
3. Keep the page open while you run the scenarios.

To connect AWS DevOps Agent to the alarm pipeline

1. In the AWS DevOps Agent console, open the nf-devops-agent-space Agent Space created by the CDK deployment.
2. Configure the DevOps Agent webhook and download the CSV file with the webhook URL and signing secret.
3. On the status page, choose Configure webhook, paste the URL and signing secret, and save. The page writes them to the nf-devops-agent-webhook-credentials AWS Secrets Manager secret, so there is no AWS CLI or console step. Until you set it, the bridge Lambda function sees a placeholder and skips delivery.
4. Verify the path before you run a scenario. In the Lambda console, open nf-devops-agent-webhook and use the Test tab with this event.

   {
     "Records": [
       {
         "Sns": {
           "Message": "{\"AlarmName\":\"TEST-webhook-verification\",\"AlarmDescription\":\"[TEST] Webhook integration test - not a real alarm.\",\"NewStateValue\":\"ALARM\",\"NewStateReason\":\"[TEST] Manual webhook connectivity test. Safe to ignore.\",\"Region\":\"us-east-1\"}"
         }
       }
     ]
   }

5. A 200 response confirms the path, and a test investigation appears in the DevOps Agent Operator Web App view.

Every scenario reaches DevOps Agent the same way. A CloudWatch alarm moves to ALARM and notifies the SNS topic. Amazon SNS invokes a Lambda function. The function reads the webhook URL and signing secret from Secrets Manager, signs an alarm payload, and POSTs it to the DevOps Agent webhook (as shown in Figure 1). Amazon SNS also provides delivery retries, fan-out to other subscribers, and cross-account publishing.

• Prebuilt Network Firewall metric (Scenario 1) – Alarm-1 watches the DroppedPackets metric, summed across the stateful streams, and triggers when drops rise above a baseline threshold. This requires no workload or custom metric and works on an already-deployed firewall. However, it only tells you that the firewall is dropping packets, not which rule is responsible.
• Application health metric (Scenarios 2 and 3) – Alarm-2 and Alarm-3 watch a custom metric from a connectivity check. Use this for an alarm tied to user-facing impact or to tell one traffic path from another, which requires running a component that emits the metric.

Work through each of the scenarios one at a time, following the same cycle. Interrupt network connectivity, watch the alarm trigger, let DevOps Agent investigate, apply the recommended fix, and confirm recovery before moving on.

The status-page cards follow the live CloudWatch alarm state. A card shows a green dot and the word Healthy when its alarm is clear, and a red dot and the word DROPPED when its alarm triggers. In the DROPPED state the card also adds a Condition: line describing what’s being dropped, which isn’t shown when the card is healthy. Network Firewall applies changes to new flows, so a change shows within a minute or two. Recovery comes from the mitigation DevOps Agent recommends, which you review and apply.

At baseline, the rg-domain Suricata domain rule group denies only an unused placeholder, so the test endpoint stays reachable. The rule group inspects the TLS Server Name Indication (SNI) on each outbound connection and drops any that matches a denied domain. The exact rule syntax and console steps follow.

To add the domain deny rule

1. Go to the Amazon VPC console.
2. In the navigation pane, under Network Firewall, choose Network Firewall rule groups.
3. Choose the rg-domain rule group to open its details page.
4. In the Rules section, choose Edit.
5. The rules box already contains two baseline placeholder rules (they match blocked.placeholder.invalid, so nothing real is denied). Leave those in place. Find the <app-endpoint-dns> value for Scenario 1 in the deployment script output (a Nework Load Balancer (NLB) DNS name such as NfTest-AppNl-a1b2C3dEf4G5-1234abcd5678efgh.elb.us-east-1.amazonaws.com). On a new line below the existing rules, add a drop rule that matches that DNS name on the TLS SNI, then choose Save.

   drop tls $HOME_NET any -> $EXTERNAL_NET any (ssl_state:client_hello; tls.sni; content:"<app-endpoint-dns>"; startswith; nocase; endswith; msg:"S1 domain denylist"; flow:to_server, established; sid:2000002; rev:1;)

6. After saving, the rules box holds all three lines. The two placeholders remain, plus the new drop rule for the endpoint DNS name (note the distinct sid 2000002).

Figure 2: Scenario 1 – Firewall rule change blocking the connection

What happens. The workload’s HTTPS check to the test endpoint times out, the “AWS/NetworkFirewall DroppedPackets metric climbs above baseline, and Alarm-1 moves to ALARM. The Scenario 1 card reads DROPPED (with the condition Firewall dropping the monitored domain on its allow/deny rules), while the Scenario 2 and Scenario 3 cards stay Healthy (Figure 3). On the topology, the alarm pipeline from CloudWatch through Amazon SNS and Lambda to DevOps Agent and the workload-to-firewall inspect lines both turn amber, which the legend defines as collateral / alarm active, because the packets are now dropped at the firewall. To demonstrate the resulting failure, the HTTPS · SNI line from the internet gateway to the test endpoint is shown in red, which the legend defines as dropped (root cause).

Figure 3: Scenario 1 active – Traffic blocked at the firewall

Let DevOps Agent investigate. The agent runs several lines of investigation in parallel and correlates them:

1. Reads the DroppedPackets metric and correlates the spike with a simultaneous drop in passed packets, confirming the firewall is actively blocking traffic.
2. Reads the ALERT log and finds the workload’s TLS connections to the test endpoint blocked by the S1 domain denylist rule.
3. Compares the current state against a baseline window, where the same endpoint was reachable with no alerts, which shows the block is new.
4. Searches CloudTrail and surfaces the UpdateRuleGroup call that added the deny rule, identifying the user, role, and timestamp approximately one minute before the drops began.
5. Reports the root cause as that manual rule-group change. Recommends removing the deny entry or adding an allow exception and enabling FirewallPolicyChangeProtection to prevent unauthorized changes.
6. Presents this as a plan you review and apply, not an automatic change.

In the DevOps Agent Operator Web App view, the agent first restates the Alarm-1 trigger and confirms the firewall is dropping packets above the threshold (Figure 4).

Figure 4: Scenario 1 – The symptom

Next, the agent identifies the root cause: a manual update to the rg-domain rule group that added a domain deny rule (SID 2000002) shortly before the alarm fired, blocking TLS connections to the ELB endpoint (Figure 5).

Figure 5: Scenario 1 – The root cause

Finally, the agent presents a mitigation plan, recommending you remove the problematic deny rule (SID 2000002) to restore connectivity (Figure 6).

Figure 6: Scenario 1 – The mitigation plan

Note: In a real-world environment, this type of rule typically exists for a reason. Before removing it, verify whether it was intentional but scoped too broadly. If so, refine the rule to block only unauthorized endpoints rather than removing it entirely.

Confirm recovery. Apply the change the agent recommends. After the deny entry is gone, DroppedPackets falls back to baseline, Alarm-1 clears, and the card returns to green. Move on to Scenario 2.

At baseline, the rg-stateless-priority stateless rule group keeps the allow rule at priority 100 and the drop rule at 200 for the test class, TCP destination port 9142. The workload opens a TCP connection to the test endpoint on this port. Lower priority numbers evaluate first, so the allow rule wins. This scenario uses port 9142 instead of 443 to demonstrate a stateless rule, which matches on the packet’s 5-tuple (protocol, ports, addresses) rather than application content.

Introduce the change. Invert the two rule priorities so the drop rule evaluates before the allow rule. This is the kind of change a rushed rule edit can introduce.

To invert the stateless rule priorities

1. Go to the Amazon VPC console.
2. In the navigation pane, under Network Firewall, choose Network Firewall rule groups.
3. Choose the rg-stateless-priority rule group to open its details page.
4. In the Rules section, choose Edit.
5. Raise the (Action: Pass) rule’s priority number so it sits after the (Action: Drop) rule, then choose Save. For example, change the (Action: Pass) rule from 100 to 300 (any number higher than the drop rule’s 200 works). You only need to move one rule, and using 300 avoids a clash with the drop rule that already sits at 200. Network Firewall evaluates the lowest priority number first, so the (Action: Drop) rule at 200 now wins for this traffic class, ahead of the (Action: Pass) rule at 300.

Figure 7: Scenario 2 – Rule priority change blocking the traffic class

What happens. The drop rule now wins, the TCP connection to the test endpoint on port 9142 times out, the StatelessRuleFailures metric climbs above baseline, and Alarm-2 moves to ALARM. The Scenario 2 card reads DROPPED (with the condition Stateless rules dropping the monitored traffic class), while the Scenario 1 and Scenario 3 cards stay Healthy (Figure 8). On the topology, the alarm pipeline from CloudWatch through Amazon SNS and Lambda to DevOps Agent and the workload-to-firewall inspect lines both turn amber, which the legend defines as collateral / alarm active, because the packets are now dropped at the firewall. To demonstrate the resulting failure, the TLS :9142 line from the internet gateway to the test endpoint is shown in red, which the legend defines as dropped (root cause).

Figure 8: Scenario 2 active

Let DevOps Agent investigate. A stateless drop happens before traffic reaches the stateful inspection engine, so it produces no ALERT log entries. The agent turns to configuration and flow logs instead:

1. Reads the stateless rule group state and finds the drop rule at the lower priority number, ahead of the pass rule, so the drop evaluates first.
2. Reads the flow logs and sees passed packets drop to zero within a minute of the change.
3. Searches CloudTrail and surfaces the UpdateRuleGroup call that inverted the priorities, identifying the user, role, and timestamp about a minute before the alarm.
4. Reports the root cause as that priority inversion. Recommends removing the redundant drop rule and managing the rule group through infrastructure-as-code (IaC) to prevent manual misconfigurations.
5. Presents this as a plan you review and apply, not an automatic change.

In the DevOps Agent Operator Web App view, the agent first restates the Alarm-2 trigger and confirms that a workload connectivity health check is failing because the firewall’s stateless rules are dropping egress (Figure 9).

Figure 9: Scenario 2 – The symptom

Next, the agent identifies the root cause, using the rule-group state and CloudTrail to pinpoint the conflicting DROP/PASS rules, where the new DROP rule’s lower priority number makes it match first (Figure 10).

Figure 10: Scenario 2 – The root cause

Finally, the agent presents a mitigation plan, recommending you remove the conflicting DROP rule at priority 200 to restore traffic flow (Figure 11).

Figure 11: Scenario 2 – The mitigation plan

Confirm recovery. Apply the change the agent recommends. After the allow rule is ahead of the drop rule again, Alarm-2 clears and the card returns to green. Move on to Scenario 3.

At baseline, the protected subnet in each Availability Zone routes its egress through the firewall endpoint in that same Availability Zone , and the matching return route uses that same endpoint. One endpoint sees both directions of the flow, so the stateful engine completes the handshake. The workload runs in the protected subnet in us-east-1a (CIDR 10.0.4.0/24), so at baseline its egress and its return both use the us-east-1a firewall endpoint.

Introduce the change. Make the flow asymmetric by sending egress out one Availability Zone endpoint while the return comes back through the other. This takes two route edits, and both are required. With only the first edit the flow can still complete, so the alarm will not trigger until both are saved. It makes no firewall-policy change, mirroring a real multi-Availability-Zone routing mistake.

To create asymmetric cross Availability Zone routing

1. Go to the Amazon VPC console and choose Route tables in the navigation pane.
2. Flip the egress. Select the NfNetworkStack/SampleVpc/protectedSubnet1 route table (the us-east-1a protected subnet, where the workload runs). On the Routes tab, choose Edit routes. Its 0.0.0.0/0 route currently targets the us-east-1a firewall endpoint. For the target, choose Gateway Load Balancer Endpoint and select the us-east-1b firewall endpoint, then choose Save changes.
3. Move the return. Select the NfNetworkStack/SampleVpc/publicSubnet2 route table (the us-east-1b public subnet, where egress now exits). Choose Edit routes, then Add route. For the destination enter the workload CIDR 10.0.4.0/24. For the target, choose Gateway Load Balancer Endpoint and select the us-east-1a firewall endpoint. Choose Save changes.

After both edits, a flow’s egress leaves through the us-east-1b endpoint while its return is directed to the us-east-1a endpoint. Neither endpoint sees the whole flow.

Figure 12: Scenario 3 routing change breaking the flow’s symmetry

What happens. A new connection leaves through one endpoint. Its return arrives at the other endpoint, which never saw the connection open, so the handshake fails. Unlike Scenarios 1 and 2, this affects the whole subnet, so all egress stops and Alarm-2 and Alarm-3 both move to ALARM. The AWS/NetworkFirewall DroppedPackets alarm (Alarm-1) stays quiet because no endpoint is making a drop decision. The flow is lost to asymmetric routing rather than counted as a firewall drop. This is why monitoring application connectivity matters. A routing fault is invisible to the firewall’s own drop counter. On the status page, the Scenario 2 card reads DROPPED (with the condition “Stateless rules dropping the monitored traffic class”) and the Scenario 3 card reads DROPPED (with the condition Return traffic dropped by asymmetric cross-Availability-Zone routing), while the Scenario 1 card stays Healthy (Figure 13). On the topology, the alarm pipeline from CloudWatch through Amazon SNS and Lambda to DevOps Agent and the workload-to-firewall inspect lines both turn amber, which the legend defines as collateral / alarm active, while the egress path from the firewall through the NAT gateway and the TLS :9142 and HTTPS · routing lines to the test endpoint turn red, which the legend defines as dropped (root cause).

Figure 13: Scenario 3 – The status page during a path-wide outage

Let DevOps Agent investigate. Both Alarm-2 and Alarm-3 fire in the same datapoint. DevOps Agent recognizes them as linked and merges them into a single investigation:

1. Reads the flow logs and sees bidirectional TLS connections stop abruptly, with only one-way traffic remaining and no flows reaching the established state.
2. Reads the firewall metrics and sees received and passed packets shift from one Availability Zone to the other at the moment of the change.
3. Calls DescribeRouteTables and finds the egress route pointing at one Availability Zone firewall endpoint while the return route points at the other.
4. Searches CloudTrail and surfaces the ReplaceRoute and CreateRoute calls by the same user, about a minute before both alarms fired.
5. Reports the root cause as that asymmetric routing change. Recommends restoring symmetric same-Availability-Zone routing so egress and return traverse the same endpoint.
6. Presents this as a plan you review and apply, not an automatic change.

A mitigation plan is a recommendation you review, not an automatic change, and the right fix depends on the intended design. Restoring symmetric routing can mean sending the workload subnet’s egress back through its own-Availability-Zone firewall endpoint (this sample’s architecture) or, in a design that doesn’t inspect this path, back through a NAT gateway. The agent infers a plausible target from what it can observe, so review the specific route it proposes against your intended topology before you apply it. (Connecting your pipeline or infrastructure-as-code, covered in the next section, lets the agent recommend the target that matches your design.)

In the DevOps Agent Operator Web App view, the agent restates the Alarm-3 (AsymmetricFlowFailures) trigger and confirms the workload’s egress to a monitored endpoint is being blocked by the Network Firewall (Figure 14).

Figure 14: Scenario 3 – The symptom

Next, the agent identifies the root cause: manual route table changes that created cross-AZ asymmetric routing through the network firewall, breaking its symmetric routing requirement (Figure 15)

Figure 15: Scenario 3 – The root cause

Finally, the agent presents a mitigation plan, recommending you restore symmetric routing by pointing protectedSubnet1‘s default route back to the same Availability Zone firewall endpoint, so one endpoint sees both directions of the flow again (Figure 16).

Figure 16: Scenario 3 – The mitigation plan

Confirm recovery. Apply the change the agent recommends, after checking the route target matches your intended design. After the workload subnet’s egress and return use the same Availability Zone firewall endpoint again, the control probe recovers, the alarms clear, and every card returns to green.

In production a single change can trigger several alarms at the same time, as Scenario 3 shows. DevOps Agent links related investigations and works them as one, so you review a single root cause. You can validate the linked findings or unlink an alarm to investigate it independently. If you would rather collapse alarms before they reach the agent, you can add correlation logic in the bridge Lambda function, buffering and grouping by firewall. You can also add email, Amazon Simple Queue Service (Amazon SQS), or HTTP subscribers to the SNS topic, or add the webhook Lambda function to a topic you already run. DevOps Agent produces a mitigation plan but does not change your environment on its own.

You can also give the agent more to work with. DevOps Agent connects to source repositories and CI/CD pipelines, integrating with GitHub (including GitHub Enterprise Server and GitLab Self-Managed through a private connection). It can associate AWS resources with deployments of AWS CloudFormation, AWS CDK, Amazon Elastic Container Registry (Amazon ECR) images, and Terraform. With deployed configuration and recent deployment events in view, the agent correlates the disruption against the change that introduced it and recommends a fix matching your intended design. For this sample, that means recommending the workload subnet’s own Availability Zone firewall endpoint rather than a generic symmetric path.

DevOps Agent also supports proactive incident prevention. It analyzes patterns across past investigations and delivers recommendations to prevent similar issues from recurring, including governance recommendations that strengthen deployment processes and pipeline controls. For Network Firewall rule changes, this means the agent can recommend guardrails for your CI/CD pipeline based on the classes of misconfigurations it has already resolved. You can access these recommendations through the Improvements page in the DevOps Agent Operator Web App.

Clean up the environment with one command.

bash scripts/destroy.sh

It reverts any active scenario, runs cdk destroy for all stacks, and sweeps for stragglers by the Project = nf-devops-agent tag. The main cost drivers are the two Network Firewall endpoints, the NAT gateways (one in the main VPC for each Availability Zone, one in the test-endpoint VPC), and the test endpoint’s load balancers. Each of these bills at an hourly rate for as long as it’s provisioned, whether or not traffic is flowing, so a stack left running continues to accrue charges around the clock even while idle. Running the scenarios and tearing the stack down the same day limits the cost to a few active hours rather than days of idle hourly charges.

In this post, we showed you how AWS DevOps Agent accelerates troubleshooting for three common network firewall connectivity issues. The first was a domain deny list. The second was a stateless priority inversion. The third was an asymmetric cross-AZ routing drop. For each one, DevOps Agent investigated the drop and returned a root cause with a mitigation plan you approve before applying. The first scenario triggered on a prebuilt Network Firewall metric, and the other two on application health metrics. That shows both ways to alarm on a firewall problem through one pipeline.

The pattern isn’t specific to Network Firewall. The same flow fits any service that emits CloudWatch metrics and logs, such as AWS WAF, security groups, and network ACLs. Clone the sample repository to explore the solution, then apply what you learn to your own firewall, application, and alarms. For more details, see the AWS Network Firewall Developer Guide and the AWS Network Firewall pricing page. Start with the Getting Started with AWS DevOps Agent guide to connect your first webhook.