Monday, March 31, 2025

Enabling Custom Validation for Content Fragment Fields in AEM as a Cloud Service – New CF Editor

In my earlier posts, we discussed how to enable Composite MultiField in Content Fragments and how to enable dynamic data fields in the new Content Fragment editor. In this post, we will explore how to enable custom validations for Content Fragment fields. Most of the steps are similar to those outlined in the previous posts. You will create a field in the Content Fragment model, and using the field name, you will register the extension. Please refer to one of the earlier posts for a step-by-step guide to enabling the extension.

While creating a Content Fragment Model, you can set up various out-of-the-box (OOTB) validations for the CF fields, such as MaxLength and Required. These validations should be applied to the overridden fields by fetching the configurations from the model. Additionally, other validations like Email, URL, and Regex can also be applied to the fields from the model.

Note: Please be aware that the content of this blog does not reflect the views of Adobe or my current organization. Before applying this approach, make sure to validate it thoroughly and ensure that it aligns with Adobe's recommendations.



Now, additional validations can be applied through the extension. The out-of-the-box (OOTB) validations, such as Email, URL, and custom regex validations, are applied first, followed by custom validations. For example, if I enable Email validation, the field will only accept valid email addresses. Then, I can add another custom validation rule to reject certain predefined emails, such as [email protected]. This can be achieved through custom regex, but I’m just using this as an example for the demo.

Extension Component to enable additional custom validation Rules:

CustomFieldValidation.js

import React, { useEffect, useState } from "react";
import { attach } from "@adobe/uix-guest";
import { extensionId } from "./Constants";
import { TextField, Provider, View, defaultTheme } from "@adobe/react-spectrum";

const CustomFieldValidation = () => {
  const [connection, setConnection] = useState(null);
  const [model, setModel] = useState(null);
  const [value, setValue] = useState("");
  const [customError, setCustomError] = useState(null);
  const [isInvalid, setIsInvalid] = useState(false);
  const [validationInProgress, setValidationInProgress] = useState(false);

  const validate = (val) => {
    if (!connection?.host?.field) return;

    let error = null;

    // Custom validation rule
    if (typeof val === "string" && val.toLowerCase() === "[email protected]") {
      error = "The value '[email protected]' is not allowed.";
    }

    setCustomError(error);
    setIsInvalid(!!error);

    if (!error || validationInProgress) return;

    setValidationInProgress(true);

    // Delay call to allow host readiness
    setTimeout(() => {
      try {
        connection.host.field
          .setValidationState({ state: "invalid", message: error })
          .catch((err) => {
            console.warn(
              "setValidationState failed:",
              err?.message || JSON.stringify(err)
            );
          })
          .finally(() => setValidationInProgress(false));
      } catch (err) {
        console.warn("setValidationState threw:", err?.message || JSON.stringify(err));
        setValidationInProgress(false);
      }
    }, 1000); // 1s delay for stability
  };

  const handleChange = (val) => {
    setValue(val);

    try {
      connection?.host?.field?.onChange(val).catch((err) =>
        console.warn("onChange failed:", err?.message || JSON.stringify(err))
      );
    } catch (err) {
      console.warn("onChange threw:", err?.message || JSON.stringify(err));
    }

    validate(val);
  };

  useEffect(() => {
    const init = async () => {
      try {
        if (!extensionId) {
          throw new Error("Missing extensionId. Check Constants file.");
        }

        const conn = await attach({ id: extensionId });

        if (!conn?.host?.field) {
          throw new Error("Host field API is unavailable.");
        }

        setConnection(conn);

        const modelData = await conn.host.field.getModel();
        setModel(modelData);

        const defaultValue = (await conn.host.field.getDefaultValue()) || "";
        setValue(defaultValue);
      } catch (err) {
        console.error("Extension init failed:", err?.message || JSON.stringify(err));
      }
    };

    init();
  }, []);

  if (!connection || !model) {
    return (
      <Provider theme={defaultTheme}>
        <View padding="size-200">Loading custom field…</View>
      </Provider>
    );
  }

  return (
    <Provider theme={defaultTheme}>
      <View padding="size-200" width="100%">
        <TextField
          label={model?.fieldLabel || "Custom Field"}
          value={value}
          onChange={handleChange}
          isRequired={model?.required || false}
          placeholder={model?.emptyText || "Enter a value"}
          validationState={isInvalid ? "invalid" : undefined}
          errorMessage={model?.customErrorMsg || customError}
          maxLength={model?.maxLength || undefined}
          width="100%"
        />
      </View>
    </Provider>
  );
};
export default CustomFieldValidation;

Now the custom validation Rules are executed


Sunday, March 9, 2025

Error response from daemon: Get "https://registry-1.docker.io/v2/": net/http: request canceled while waiting for connection (Client.Timeout exceeded while awaiting headers)

While trying to pull a Docker image, the docker pull command was stuck forever without any progress or error.

System Details:

  • Windows 10
  • WSL 2 - Ubuntu
  • Docker Desktop 4.38.0

Issue Faced:


Running docker pull was stuck indefinitely.


Trying to log - docker login -u <username> in using the command prompt failed with this error:
"Error response from daemon: Get "https://registry-1.docker.io/v2/": net/http: request canceled while waiting for connection (Client.Timeout exceeded while awaiting headers)" 

Even signing into Docker Desktop was not successful.

Login on Docker Hub via browser was working fine, but Docker Desktop was not picking up the login session.

I followed different forums and applied multiple configuration suggestions, including adjusting nameserver settings inside /etc/resolv.conf, but nothing worked.

Also, WSL 2 networking was working fine otherwise — only Docker commands were impacted.

Resolution:

Finally, the issue got resolved after upgrading Docker Desktop to the latest version (4.39.0).
(Older versions may also work — I tried with 4.35.1, and it worked as well).



Thursday, March 6, 2025

Resolving AEM Content Fragment Export to Adobe Target Failure

When exporting AEM Content Fragments as JSON Offers to Adobe Target, you may encounter an error preventing the successful integration. This post details the issue, its root cause, and the steps to resolve it.


Issue

The AEM Content Fragment Export to Adobe Target failed with the following exception:

06.03.2025 12:59:31.223 *DEBUG* [[0:0:0:0:0:0:0:1] [1741287571223] GET /content/dam/content-fragments/test/test-cf/.permissions.json HTTP/1.1]  
com.test.core.filters.LoggingFilter request for /content/dam/content-fragments/test/test-cf/, with selector permissions  

06.03.2025 12:59:40.706 *DEBUG* [[0:0:0:0:0:0:0:1] [1741287580703] POST /content/dam/content-fragments/test/test-cf.cfm.targetexport HTTP/1.1]  
com.test.core.filters.LoggingFilter request for /content/dam/content-fragments/test/test-cf, with selector cfm  

06.03.2025 12:59:40.710 *ERROR* [[0:0:0:0:0:0:0:1] [1741287580703] POST /content/dam/content-fragments/test/test-cf.cfm.targetexport HTTP/1.1]  
com.adobe.cq.dam.cfm.graphql.extensions.querygen.impl.service.QueryGeneratorServiceImpl  
Cannot find Sites GraphQL endpoint resource, cannot generate GraphQL query  

Root Cause

This issue occurs because no GraphQL endpoint is defined for Adobe Target to fetch Content Fragment details. The export process requires a valid GraphQL endpoint to retrieve structured content from AEM and send it to Adobe Target.

Solution

To resolve this issue, follow these steps to define a global GraphQL endpoint in AEM:

  1. Log into AEM and navigate to Tools → General → GraphQL.

  2. Create a new GraphQL endpoint and associate it with /conf/global.

  3. Save and publish the endpoint to make it accessible.

Once configured, the AEM Content Fragment export to Adobe Target will be successful, allowing the fragments to be used in Adobe Target Activities for personalized content experiences.

Wednesday, March 5, 2025

API Gateway vs Service Mesh: Understanding the Differences

Introduction

As modern applications increasingly rely on microservices architectures, managing communication between services becomes crucial. Two key technologies that help address these challenges are API Gateways and Service Meshes. While both manage service-to-service communication, they serve different purposes and operate at different layers of an application architecture. This blog explores their differences, use cases, and how to decide which one to use.



1. What is an API Gateway?

An API Gateway is an entry point for external clients to interact with an application’s backend services. It acts as a reverse proxy that routes requests to the appropriate microservices while handling concerns like authentication, rate limiting, logging, and caching.

Key Features of an API Gateway

  • Traffic Routing & Load Balancing – Directs external requests to the correct microservice.
  • Authentication & Authorization – Enforces security policies using OAuth, JWT, or API keys.
  • Rate Limiting & Throttling – Prevents abuse by limiting the number of requests per client.
  • Request Transformation – Modifies request/response formats to ensure compatibility.
  • Logging & Monitoring – Tracks API calls for analytics and debugging.
  • Caching – Stores frequently accessed responses to improve performance.

Popular API Gateway Solutions

  • Kong (Open-source and enterprise API management)

  • Amazon API Gateway (AWS-managed API gateway)

  • Apigee (Google Cloud API management platform)

  • Nginx (Lightweight API Gateway & reverse proxy)

  • Traefik (Cloud-native API Gateway)


2. What is a Service Mesh?

A Service Mesh is a dedicated infrastructure layer for managing service-to-service communication within a microservices architecture. Unlike API Gateways, which handle north-south traffic (client-to-service requests), a Service Mesh focuses on east-west traffic (internal service-to-service communication).

Key Features of a Service Mesh

  • Service Discovery & Load Balancing – Automatically detects services and distributes traffic efficiently.
  • mTLS (Mutual TLS) Encryption – Secures communication between services.
  • Observability & Tracing – Provides deep insights into service interactions.
  • Traffic Management – Enables request routing, retries, and fault tolerance.
  • Policy Enforcement – Manages service access policies, authentication, and authorization.
  • Circuit Breaking & Failover – Prevents cascading failures by limiting retries and isolating failing services.

Popular Service Mesh Solutions

  • Istio (One of the most popular service meshes, integrates with Kubernetes)

  • Linkerd (Lightweight service mesh for Kubernetes)

  • Consul (Service mesh and service discovery solution by HashiCorp)

  • AWS App Mesh (Managed service mesh for AWS environments)


3. API Gateway vs Service Mesh: Key Differences

Feature
API GatewayService Mesh
Primary FocusExternal traffic (north-south)Internal service-to-service traffic (east-west)
Traffic ManagementRequest routing, load balancingService discovery, retries, circuit breaking
Security FeaturesAuthentication, rate limitingMutual TLS, fine-grained service access control
Performance OptimizationCaching, compressionTraffic shaping, observability, tracing
DeploymentEdge of the networkEmbedded within the infrastructure
Best forExposing APIs to external usersManaging inter-service communication

4. When to Use an API Gateway vs. a Service Mesh?

Use an API Gateway When:

  • You need to expose your APIs securely to external clients.
  • You require authentication, rate limiting, or request transformation.
  • You want to improve performance with caching and load balancing.
  • You need to monetize APIs or apply API lifecycle management.

Use a Service Mesh When:

  • You have multiple microservices that need secure communication between them.
  • You need observability, tracing, and traffic management across services.
  • You want mTLS-based encryption for secure service-to-service communication.
  • You need fine-grained policy enforcement between microservices.


5. Can API Gateways and Service Mesh Work Together?

Yes! API Gateways and Service Mesh complement each other rather than compete. Many modern architectures combine both to achieve end-to-end traffic management.

Example Architecture with API Gateway & Service Mesh

  1. API Gateway (Edge Layer): Handles external client requests, authentication, rate limiting, and API exposure.

  2. Service Mesh (Internal Layer): Manages service-to-service communication, security, and observability.

This combination allows for better security, scalability, and resilience in microservices architectures.


Conclusion

Both API Gateways and Service Meshes play essential roles in microservices architectures, but they serve different purposes. While API Gateways manage external traffic, Service Meshes optimize internal service-to-service communication. Organizations should evaluate their architecture needs and consider using both for a comprehensive microservices communication strategy.

Monday, February 3, 2025

Target Configuration Not Resolved While Creating Adobe Target Activity from AEM

I configured the AEM-Adobe Target integration by enabling:

  • IMS configuration for authentication
  • Legacy Adobe Target Cloud Config and New Adobe Target Cloud Config
  • Default workspace assignment in Adobe Developer Console Project
  • Approver permissions for the API credential in the Admin Console




However, when attempting to create an A/B test or Experience Targeting (XT) activity from AEM and sync it to Adobe Target, the Target Configuration dropdown was empty, indicating that no configurations were detected.



Root Cause & Resolution

Upon further analysis, I identified that the issue was due to the user not being part of the target-activity-authors group. After adding the user to this group, the activity creation process began recognizing all available Adobe Target configurations, including both legacy and new configurations.



Now, activities can be successfully created and synced to the default workspace once the experience is defined.

Friday, January 31, 2025

Adobe Experience Manager & Adobe Target: Activity Saved but Not Synchronized – Reason: The following experience has no offers:

When we try to create A/B Testing or Experience Targeting activities from the AEM Activities Console and sync them to Adobe Target










The synchronization fails with the following error, and the status is shown as 'Not Synced'.






The root cause of the issue is that no experience variations were defined for the activity. We created different experiences but did not apply any pages to the activity or enable the required experience changes. To resolve the issue, select the page where this activity should be enabled and target the required components and assign the changes for appropriate experiences. This should allow the activity to sync successfully with Adobe Target.








The activity has now been successfully synced to Adobe Target.





Sunday, January 19, 2025

Generate Music Through Python – A Complete Guide

Introduction

Music generation with Python has become more accessible with powerful libraries that allow us to compose melodies, generate MIDI files, and convert them into audio formats like WAV and MP3. This guide walks through the process of creating a MIDI file, synthesizing it into WAV using FluidSynth, and finally converting it to MP3 using pydub.

By the end of this guide, you will have a fully functional Python script that generates music and exports it as an MP3 file.


Why Use Python for Music Generation?

Python provides several libraries that make it easy to create and manipulate music:

  • MIDIUtil – Generates MIDI files programmatically.
  • Mingus – Provides music theory functions and chord generation.
  • FluidSynth – A real-time synthesizer that converts MIDI to WAV.
  • pydub – Converts audio formats, such as WAV to MP3.

Using these libraries, we can generate music from scratch and export it into an audio format that can be played on any device.


Setting Up the Environment

Before running the script, install the necessary dependencies:

Install Python Libraries

Run the following command in your terminal:

pip install midiutil mingus pyfluidsynth pydub

Install FluidSynth

  1. Download FluidSynth from the official repository:
    FluidSynth Releases
  2. Extract it to C:\tools\fluidsynth
  3. Add C:\tools\fluidsynth\bin to your system PATH (for command-line access).
  4. Verify the installation by running:
    fluidsynth --version

Download a SoundFont (.sf2) File

FluidSynth requires a SoundFont file to map MIDI notes to instrument sounds.

How Music is Generated in Python

Music generation in Python follows these key principles:

Understanding MIDI File Structure

A MIDI (Musical Instrument Digital Interface) file contains:

  • Note Data – The pitches and durations of notes.
  • Velocity – The intensity of each note.
  • Instrument Information – Which instruments to use for playback.

Unlike audio formats like MP3 or WAV, MIDI does not contain actual sound data, meaning it must be played back using a synthesizer like FluidSynth.

Breaking Down the Composition Process

  1. Chords and Progressions

    • Chords are groups of notes played together.
    • A chord progression is a sequence of chords that forms a harmonic structure for the music.
    • Example: "C → G → Am → F" is a common progression.
  2. Melody Generation

    • A melody is a sequence of individual notes that create a recognizable tune.
    • The script selects notes from a chord to create a simple melodic line.
  3. Bassline Generation

    • The bassline is usually the root note of each chord, played in a lower octave.
    • It provides rhythm and harmonic stability.
  4. MIDI to Audio Conversion

    • Since MIDI files do not contain actual sound, FluidSynth uses a SoundFont to generate audio.
    • Finally, we convert the generated WAV file to MP3 using pydub.

Python Script to Generate MIDI and Convert to MP3

This script will:

  1. Generate a MIDI file with chord progressions, a melody, and a bassline.
  2. Convert MIDI to WAV using FluidSynth and a SoundFont.
  3. Convert WAV to MP3 using pydub.

Python Script


import random import os import subprocess from midiutil import MIDIFile from mingus.core import chords from pydub import AudioSegment # Define paths SOUNDFONT_PATH =os.path.join(os.getcwd(), "FluidR3_GM.sf2") # Update your SoundFont path MIDI_FILENAME = "generated_music.mid" WAV_FILENAME = "generated_music.wav" MP3_FILENAME = "generated_music.mp3" # Define chord progressions verse = ["C", "G", "Am", "F"] chorus = ["F", "C", "G", "Am"] bridge = ["Dm", "A7", "G", "C"] song_structure = [verse, verse, chorus, verse, bridge, chorus] # MIDI settings track = 0 channel = 0 time = 0 # Start time in beats tempo = 120 # BPM volume = 100 # MIDI velocity # Create a MIDI file MyMIDI = MIDIFile(1) MyMIDI.addTempo(track, time, tempo) # Assign instruments instrument_chords = 0 # Acoustic Piano instrument_melody = 40 # Violin instrument_bass = 33 # Acoustic Bass MyMIDI.addProgramChange(track, channel, time, instrument_chords) MyMIDI.addProgramChange(track, channel + 1, time, instrument_melody) MyMIDI.addProgramChange(track, channel + 2, time, instrument_bass) # Convert note names to MIDI numbers def note_to_number(note: str, octave: int) -> int: NOTES = ['C', 'C#', 'D', 'Eb', 'E', 'F', 'F#', 'G', 'Ab', 'A', 'Bb', 'B'] NOTES_IN_OCTAVE = len(NOTES) return NOTES.index(note) + (NOTES_IN_OCTAVE * octave) # Generate music for section in song_structure: for chord in section: chord_notes = chords.from_shorthand(chord) random.shuffle(chord_notes) rhythm_pattern = [0, 0.5, 1, 1.5, 2, 2.5, 3] # Add chords for i, note in enumerate(chord_notes): octave = 3 midi_note = note_to_number(note, octave) MyMIDI.addNote(track, channel, midi_note, time + rhythm_pattern[i % len(rhythm_pattern)], 1, volume) # Add bassline bass_note = note_to_number(chord_notes[0], 2) MyMIDI.addNote(track, channel + 2, bass_note, time, 4, volume) # Add melody melody_note = note_to_number(random.choice(chord_notes), 5) melody_duration = random.choice([0.5, 1, 1.5]) MyMIDI.addNote(track, channel + 1, melody_note, time + 2, melody_duration, volume) time += 4 # Save MIDI file with open(MIDI_FILENAME, "wb") as output_file: MyMIDI.writeFile(output_file) # Convert MIDI to WAV using FluidSynth subprocess.run(f'fluidsynth -ni -F {WAV_FILENAME} -r 44100 {SOUNDFONT_PATH} {MIDI_FILENAME}', shell=True, check=True) # Convert WAV to MP3 using pydub AudioSegment.from_wav(WAV_FILENAME).export(MP3_FILENAME, format="mp3")

Running the Script

Once dependencies are installed, run:

python generate_music.py

This generates:

  • generated_music.mid (MIDI file)
  • generated_music.wav (WAV file)
  • generated_music.mp3 (MP3 file)

Next Steps

  • Customize the chord progressions
  • Experiment with different instruments
  • Generate longer compositions
  • Integrate AI-generated melodies

Start generating music with Python today!

Saturday, January 18, 2025

Convert MP3 to MIDI Using Spotify’s BasicPitch and TensorFlow

Have you ever wanted to convert an MP3 file into a MIDI format for music production, transcription, or remixing?

BasicPitch by Spotify is an open-source AI-powered tool that makes this process simple and accurate.

With just a few lines of Python code, you can extract notes and melodies from an audio file and use them in a Digital Audio Workstation (DAW) or for further analysis. Let’s dive in.


What is BasicPitch?

BasicPitch is an AI-powered polyphonic pitch detection model developed by Spotify. Unlike traditional MIDI conversion tools, BasicPitch:

  • Detects multiple notes at once (polyphonic transcription)
  • Understands pitch bends and vibrato
  • Works with various instruments, not just piano
  • Is lightweight and open-source

More about BasicPitch:


Steps to Convert MP3 to MIDI

1. Install Dependencies

First, install the required Python packages:


pip install basic-pitch ffmpeg-python


2. Use This Python Script

This script will process the MP3 file, run BasicPitch’s AI model, and save the MIDI file.

import os
from basic_pitch.inference import predict_and_save from basic_pitch import ICASSP_2022_MODEL_PATH # -------------------------------------- # MP3 to MIDI Conversion using BasicPitch by Spotify # This script processes an MP3 file using BasicPitch # and saves the resulting MIDI file in the output directory. # -------------------------------------- # Define the input MP3 file path (Replace "song.mp3" with your actual file) input_mp3 = r"song.mp3" # Ensure the file is in the same directory or provide a full path # Define the output directory where MIDI files will be saved output_dir = r"output" # Check if the input MP3 file exists before proceeding if not os.path.exists(input_mp3): raise FileNotFoundError(f"Error: The file '{input_mp3}' does not exist. Please check the path.") # Load the BasicPitch model basic_pitch_model = str(ICASSP_2022_MODEL_PATH) # -------------------------------------- # Running the BasicPitch Inference Model # The model will analyze the MP3 file and generate a MIDI file. # -------------------------------------- print("Running BasicPitch inference on the MP3 file...") predict_and_save( [input_mp3], # Use MP3 directly, no need to convert to WAV manually output_dir, # Save MIDI files in the output directory save_midi=True, # Enable saving the MIDI output sonify_midi=False, # Disable MIDI sonification (audio rendering) save_model_outputs=False, # Disable saving raw model outputs save_notes=False, # Disable saving individual note files model_or_model_path=basic_pitch_model, # Load BasicPitch model ) # MIDI conversion complete. Check the output folder. print(f"Conversion complete. Check the '{output_dir}' folder for the MIDI files.")

Setup Instructions

Why Use BasicPitch Instead of Other MIDI Conversion Tools?

Feature      BasicPitch (Spotify)Other Tools
AI-powered      Yes               Mostly rule-based
Polyphonic (Multiple Notes)      Yes               Mostly monophonic
Pitch Bends and Vibrato      Yes               No or limited
Open Source      Yes               Often paid
Lightweight and Fast      Yes               Some require heavy processing

Real-World Use Cases

  • Convert guitar recordings to MIDI for editing in DAWs like Ableton and FL Studio
  • Transcribe piano melodies into MIDI for remixing or re-orchestrating
  • Turn vocal hums into music notes to create melodies from scratch
  • Use for music education and research to analyze complex musical pieces

What’s Next?

Once your MP3 is converted to MIDI, you can:

  • Import it into DAWs like Ableton, FL Studio, Logic Pro, or GarageBand
  • Assign virtual instruments to the MIDI notes and create new sounds
  • Use it for sheet music transcription and study compositions

Have you tried BasicPitch yet? What are your thoughts? Let me know in the comments.


Useful Resources

Thursday, January 9, 2025

How to Use OWASP Dependency Check in a Maven Project

OWASP Dependency Check is a software composition analysis (SCA) tool that scans your project's dependencies for known vulnerabilities. It cross-references dependencies with databases like the National Vulnerability Database (NVD) to help you proactively identify and mitigate risks in third-party libraries. This guide explains how to integrate and use OWASP Dependency Check in a Maven project.


What is OWASP Dependency Check?

OWASP Dependency Check is an open-source tool that:

  • Identifies vulnerabilities in project dependencies by referencing public vulnerability databases.
  • Helps mitigate risks by providing CVE details, severity levels, and suggested fixes.
  • Integrates seamlessly with Maven, Gradle, CI/CD pipelines, and other build tools.

Step 1: Add Dependency Check Plugin to Maven

To integrate Dependency Check with Maven, add the plugin configuration to your pom.xml:

<build>
<plugins> <plugin> <groupId>org.owasp</groupId> <artifactId>dependency-check-maven</artifactId> <version>8.4.0</version> <!-- Use the latest version --> <executions> <execution> <goals> <goal>check</goal> </goals> </execution> </executions> </plugin> </plugins> </build>

Step 2: Run the Dependency Check

Execute the following Maven command to start the vulnerability scan:

mvn dependency-check:check

Use mvn dependency-check:update-only on subsequent runs to reduce execution time.

What Happens:

  • The plugin scans your project's dependencies, fetches data from the NVD, and identifies vulnerabilities.
  • Reports are generated in the target directory:
    • HTML Report: dependency-check-report.html
    • JSON Report: dependency-check-report.json

Step 3: Review the Results

  1. Access the Reports:

    • Navigate to the target directory to view the generated reports.
    • Open dependency-check-report.html for a detailed summary.
  2. Understand the Output:

    • Each dependency is checked for known CVEs.
    • Severity is indicated using the CVSS (Common Vulnerability Scoring System).
  3. Take Action:

    • Update vulnerable dependencies to patched versions.
    • Exclude unused or unnecessary dependencies.

Step 4: Configure Dependency Check

Customize the plugin behavior to suit your project needs. Use the <configuration> tag in pom.xml for advanced settings.

Example Configuration:

<plugin>
<groupId>org.owasp</groupId> <artifactId>dependency-check-maven</artifactId> <version>8.4.0</version> <configuration> <outputDirectory>./dependency-check-reports</outputDirectory> <formats> <format>HTML</format> <format>JSON</format> </formats> <failBuildOnCVSS>7</failBuildOnCVSS> <!-- Fail build for CVSS >= 7 --> <nvd.api.key>your-nvd-api-key</nvd.api.key> <!-- Optional NVD API Key --> </configuration> </plugin>

Step 5: Enhance Performance with NVD API Key

The NVD API key helps:

  • Improve scan reliability by increasing request limits to the NVD database.
  • Reduce delays caused by throttling during frequent scans.

How to Use the API Key:

  1. Obtain an API Key:
  2. Configure the API Key:
    • Add it to your pom.xml:
      <configuration>
      <nvd.api.key>your-nvd-api-key</nvd.api.key> </configuration>
    • Or pass it via the command line:
      mvn dependency-check:check -Dnvd.api.key=your-nvd-api-key
    • Or set it as an environment variable:
      export NVD_API_KEY=your-nvd-api-key

Step 6: Automate with CI/CD

Integrate OWASP Dependency Check into your CI/CD pipeline to ensure continuous security validation.

GitHub Actions Example:

name: Dependency Check
on: push: branches: - main jobs: dependency-check: runs-on: ubuntu-latest steps: - name: Checkout Code uses: actions/checkout@v3 - name: Set up Java uses: actions/setup-java@v3 with: java-version: 11 - name: OWASP Dependency Check run: mvn dependency-check:check

Common Issues and Solutions

  1. Slow Scans:

    • Use an NVD API key for faster updates.
    • Run mvn dependency-check:update-only to pre-cache vulnerability data.
  2. False Positives:

    • Exclude specific dependencies:
      <configuration>
      <excludes> <exclude>com.example:example-dependency</exclude> </excludes> </configuration>
  3. Large Projects:

    • Adjust database refresh intervals:
      <cveValidForHours>72</cveValidForHours>

Tips for Best Practices

  1. Update Regularly:
    • Ensure the plugin and NVD database are up-to-date.
  2. Fail Builds on High-Risk Vulnerabilities:
    • Use <failBuildOnCVSS> to enforce a security threshold.
  3. Exclude Dev-Only Dependencies:
    • Use <scope>test</scope> for test dependencies that don’t need production scans.

Conclusion

OWASP Dependency Check is a vital tool for identifying and mitigating risks in your project's dependencies. By integrating it into your Maven project and CI/CD pipelines, you can proactively manage vulnerabilities and ensure compliance with security standards.