Vulnerability Testing for gRPC APIs: What Every Tester Should Know

As an API developer/tester, you need to ensure your API endpoints are secure and protected from vulnerabilities. Failing to properly test API security can have serious consequences – like data breaches, unauthorized access, and service disruptions.

This blog post will provide you with practical guidance on best practices for testing the security of API endpoints, including a step-by-step technical example of how to test gRPC endpoints. It outlines the types of security testing that should be performed and the different types of security vulnerabilities that can be found in API endpoints and provides tips for remediation. By following the guidance in this article, you can build a robust security testing plan for your API endpoints.

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OWASP API Security Checklist: The Types of Tests to Perform

To ensure the security of your API endpoints, you should perform several types of testing, as recommended in the OWASP API Security Checklist. By performing these types of tests regularly, you can gain assurance that your API endpoints are secure and address any vulnerabilities that are identified to protect your APIs and your consumers.

• Penetration testing examines API endpoints for vulnerabilities that could allow unauthorized access or control. This includes testing for injection flaws, broken authentication, sensitive data exposure, XML external entities (XXE), broken access control, security misconfigurations, and insufficient logging and monitoring.
• Fuzz testing, or fuzzing, submits invalid, unexpected, or random data to API endpoints to uncover potential crashes, hangs, or other issues. This can detect memory corruption, denial-of-service, and other security risks.
• Static application security testing (SAST) analyzes API endpoint source code for vulnerabilities. This is useful for finding injection flaws, broken authentication, sensitive data exposure, XXE, and other issues early in the development lifecycle.
• Dynamic application security testing (DAST) tests API endpoints by sending HTTP requests and analyzing the responses. This can uncover issues like injection, broken authentication, access control problems, and security misconfigurations.
• Abuse case testing considers how API endpoints could potentially be misused and abused. The goal is to identify ways that the API could be used for malicious purposes so that appropriate controls and protections can be put in place.

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Common API Vulnerabilities and How to Test for Them

To ensure the security of your API endpoints, you must test for common vulnerabilities. Some of the major issues to check for include:

• SQL injection: This occurs when malicious SQL statements are inserted into API calls. Test for this by entering ' or 1=1;-- into API parameters to see if the database returns an error or additional data.
• Cross-site scripting (XSS): This allows attackers to execute malicious JavaScript in a victim's browser. Try entering into API parameters to check for reflected XSS.
• Broken authentication: This allows unauthorized access to API data and functionality. Test by attempting to access API endpoints with invalid or missing authentication credentials to verify that users are properly authenticated.
• Sensitive data exposure: This occurs when API responses contain personally identifiable information (PII) or other sensitive data. Review API responses to ensure no sensitive data is returned.
• Broken access control: This allows unauthorized access to API resources. Test by attempting to access API endpoints with different user roles or permissions to verify proper access control is in place.

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Testing gRPC Endpoints: A Technical Example

Integration Testing gRPC Endpoints in Python 

To ensure your gRPC API endpoints are secure, you should perform integration testing. This involves sending requests to your API and analyzing the responses to identify any vulnerabilities.

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Step 1 

First, use a tool like Postman, Insomnia, or BloomRPC to send requests to your gRPC server. Test all endpoints and methods in your API.

• Set up a gRPC channel and stub to connect to the server.
• Call the appropriate gRPC methods on the stub to send requests and receive responses.

#import the relevant modules 

import grpc

import your_service_pb2 as your_service

import your_service_pb2_grpc as your_service_grpc

def test_integration():

    # Test all endpoints and methods in your API.

    channel = grpc.insecure_channel('your_grpc_endpoint_address:port')

    stub = your_service_grpc.YourServiceStub(channel)

    test_endpoint_1(stub)

    test_endpoint_2(stub)

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Step 2 

Next, analyze the responses for information disclosure. Make sure that no sensitive data is returned in error messages or stack traces.

• In the test_endpoint_1 function, send a request to Endpoint 1.
• Handle any exceptions that occur during the request and analyze the error message or status code for potential information disclosure.

def test_endpoint_1(stub):

  

    try:

        request = your_service.Endpoint1Request(param1='value1', param2='value2')

        response = stub.Endpoint1Method(request)

        print("Endpoint 1 response:", response)

    except grpc.RpcError as e:

        print("Error occurred in Endpoint 1:", e.details())

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Step 3

Then, test for broken authentication by sending requests without authentication credentials. The API should return a “401 Unauthorized” status code.

• In the test_endpoint_2 function, send a request to Endpoint 2 without providing authentication credentials.

Catch any grpc.RpcError exceptions that occur and check the error code to ensure that it is “401 Unauthorized.”

def test_endpoint_2(stub):

    ```

Test for broken authentication.

    Send requests without authentication credentials.

    The API should return a 401 Unauthorized status code.

```

    try:

        request = your_service.Endpoint2Request(param1='value1', param2='value2')

        response = stub.Endpoint2Method(request)

        print("Endpoint 2 response:", response)

    except grpc.RpcError as e:

        print("Error occurred in Endpoint 2:", e.code())

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Step 4

Finally, execute the tests. Call the test methods you have defined in your script to execute the integration tests:

if __name__ == '__main__': 

test_integration()

It is important to note that gRPC API endpoint testing has many variations depending on the programming language and technologies you are using. For the example given above, there are many extension possibilities. 

For instance, you can further expand the code and add more test methods for other steps such as testing access control, handling malformed requests, checking TLS encryption, and reviewing API documentation for any discrepancies with the actual implementation.

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Ongoing API Endpoint Security Testing Best Practices

To ensure that API endpoints remain secure over time, ongoing security testing is essential. Schedule regular vulnerability scans and penetration tests to identify any weaknesses that could be exploited.

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Conduct Regular Vulnerability Scans

Run automated vulnerability scans on API endpoints at least monthly. Scan for issues like:

• SQL injection
• Cross-site scripting
• Broken authentication
• Sensitive data exposure

Remediate any critical or high-severity findings immediately. Develop a plan to address medium- and low-severity issues within 30-90 days.

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Perform Penetration Tests

Have an independent third party conduct penetration tests on API endpoints every 6-12 months. Penetration tests go deeper than vulnerability scans to simulate real-world attacks. Testers will attempt to access sensitive data or take control of the API. Address any issues found to strengthen endpoint security.

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Monitor for Anomalous Behavior

Continuously monitor API endpoints for abnormal behavior that could indicate compromise or abuse. Look for things like:

• Sudden spikes in traffic
• Requests from unknown or suspicious IP addresses
• Invalid requests or requests attempting to access unauthorized resources

Investigate anything unusual immediately to determine if remediation is needed. Monitoring is key to quickly detecting and responding to security events.

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Review Access Controls

Review API endpoint access controls regularly to ensure that only authorized users and applications can access data and resources. Remove any unused, outdated, or unnecessary permissions to limit exposure. Access controls are a critical line of defense, so keeping them up-to-date is important for security.

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Conclusion

In conclusion, testing the security of API endpoints should be an ongoing process to protect systems and data. By following best practices for identifying vulnerabilities through various types of security testing, you can remediate issues and strengthen endpoint security over time. 

While the examples above focused on gRPC endpoints, the overall guidelines apply to any API. Regularly testing API endpoints is key to avoiding breaches and ensuring the integrity of your infrastructure. Make security testing a priority and keep your endpoints protected.

🚀 How to Stop Kafka Lag: Root Causes, Best Practices, and Prevention Strategies

 

Why Kafka Lag Matters

Apache Kafka is the backbone for many high-scale systems — powering payments, order tracking, fraud detection, and event-driven microservices.

But when Kafka lag creeps in, your real-time system becomes near-real-time, which can lead to:

• Delayed payments or settlement

• Missed SLA agreements

• Data processing backlogs

• Increased infrastructure cost from retries

In financial or mission-critical domains, lag is not just a performance issue — it’s a business risk.

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What is Kafka Lag?

Kafka lag is the difference between the latest message offset in a partition and the last committed offset by a consumer group.

Example:

• Partition offset head: 1000

• Last committed offset: 800

• Lag = 200 messages

If your lag keeps growing instead of shrinking, you’re in trouble.

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Root Causes of Kafka Lag

Through real-world experience with large-scale, payment-heavy systems, I’ve seen the same lag patterns appear:

1. Slow Consumer Processing

   • Heavy DB calls or synchronous API calls inside the consumer loop.

2. Insufficient Parallelism

   • Too few consumers for the number of partitions.

3. Hot Partitions

   • Poor key distribution causing one partition to carry most traffic.

4. Broker Bottlenecks

   • Disk or network saturation on Kafka brokers.

5. Large Message Sizes

   • Serialization/deserialization overhead impacting poll rates.

6. Consumer Group Rebalancing

   • Frequent membership changes causing pauses in consumption.

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Best Practices to Prevent Kafka Lag

1. Optimize Consumer Throughput

• Keep business logic light — push heavy processing to async workers.

• Batch process records with max.poll.records.

• Commit offsets frequently to avoid replay storms.

2. Scale Consumers Effectively

• Number of consumers should match or be less than partition count.

• Use consumer group scaling during traffic peaks.

3. Fix Partition Skew

• Review key hashing logic.

• If hot partitions exist, consider re-keying or adding partitions.

4. Tune Consumer Configurations

Key configs to watch:

max.poll.records=500

max.poll.interval.ms=300000

fetch.min.bytes=50000

fetch.max.wait.ms=500

• Tune based on throughput vs. latency trade-offs.

5. Monitor Lag Proactively

• Use Prometheus JMX Exporter or Burrow for lag metrics.

• Alert when lag exceeds business-defined thresholds.

6. Handle Third-Party Dependencies

• For load tests, use mock gateways to avoid hitting real partner APIs.

• Apply circuit breakers to isolate external failures.

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Case Study: Reducing Lag at Scale

While working at Rapido, our trip location tracking service faced ~2M message lag during evening peak hours.

Root cause: Consumers were enriching each message with DB lookups.

Solution:

• Offloaded enrichment to a downstream async process.

• Increased partitions from 6 → 18.

• Tuned max.poll.records from 50 → 500.

  Result: Lag dropped from 2M to under 5K during peak.

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Checklist for Kafka Lag Prevention

• Keep consumer logic lightweight

• Scale consumer groups with partitions

• Fix partition key distribution

• Tune consumer configurations

• Batch process where possible

• Monitor lag continuously

• Mock external dependencies during load

• Test with production-like data in staging

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Final Thoughts

Kafka lag is inevitable under certain conditions — but chronic lag is a design flaw.

By combining good partition strategy, optimized consumers, and proactive monitoring, you can maintain near-real-time processing even at massive scale.

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If you’re building a Kafka-heavy system, remember:

Lag prevention is a design decision, not a firefight.

Happy Learning :) 

Getting Started with Mobile Security Framework (MobSF) for Mobile App Security Testing

Mobile application security is no longer optional—it’s essential. Whether you’re an Android, iOS, or Windows mobile developer, integrating automated security assessments into your CI/CD pipeline can drastically improve your app’s resilience against attacks. Enter MobSF (Mobile Security Framework)—an all-in-one toolkit for performing static and dynamic analysis of mobile apps.

In this guide, we’ll walk through setting up MobSF using Docker on macOS with Colima and demonstrate how to conduct both static and dynamic analysis.

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🚀 What is MobSF?

Mobile Security Framework (MobSF) is an open-source, automated mobile application pentesting, malware analysis, and security assessment tool. It supports:

• Static & dynamic analysis

• Mobile binaries: .apk, .ipa, .appx, .xapk

• Source code (zipped)

• REST APIs for CI/CD or DevSecOps integration

Whether you’re running tests during development or before release, MobSF provides valuable insights into the security posture of your app.

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🛠 Prerequisites

Before starting, ensure the following tools are installed on your macOS system:

• Colima – Docker Desktop alternative for macOS

• Docker

Installation Commands:

brew install colima

brew install docker

colima start

🧪 Running MobSF with Docker

Once Colima and Docker are set up, launch MobSF using the following command:

docker run -it --rm -p 8000:8000 opensecurity/mobile-security-framework-mobsf:latest

🌐 Accessing the MobSF Dashboard

After launching MobSF, the terminal logs will include a line like:

Listening at: http://127.0.0.1:8000

Copy this URL into your browser to open the MobSF dashboard.

Uploading an App:

Simply drag and drop your APK/IPA file into the dashboard to begin static analysis.

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🧾 Static Analysis

Once uploaded, MobSF automatically scans the app and generates a security report. Monitor the Docker logs to verify successful completion or identify potential issues during analysis.

Sample Reports:

• AppSec Scorecard for Prod Build (v2.9.8)

• Full Static Analysis Report

✅ These reports help developers and security teams identify code-level vulnerabilities, permission misuses, and more.

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🔍 Dynamic Analysis

MobSF’s dynamic analysis enables runtime behavior assessment, allowing you to detect malicious operations or insecure runtime behaviors.

🔧 Requirements:

• Emulator without Google Play Store

• API level ≤ 28 (Android 9)

Step 1: Start Emulator

Navigate to your SDK tools directory and run:

cd ~/Library/Android/sdk/tools

./emulator -avd Pixel_5_API_28 -writable-system -no-snapshot

To list available AVDs:

emulator -list-avds

Step 2: Launch MobSF with Emulator Identifier

Find your emulator’s ID using:

adb devices

Then start MobSF with the emulator bound:

docker run -e MOBSF_ANALYZER_IDENTIFIER="emulator-5554" -it --rm -p 8000:8000 opensecurity/mobile-security-framework-mobsf:latest

Step 3: Start Dynamic Analysis

In the MobSF UI:

• Go to Dynamic Analyzer

• Click Start Dynamic Analysis

MobSF will initiate an interactive test session connected to your emulator.

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✅ Final Thoughts

MobSF is a powerful and developer-friendly framework for mobile app security. With minimal setup, it provides:

• Actionable security insights

• Seamless CI/CD integration

• Both static and dynamic testing capabilities

By integrating MobSF into your development lifecycle, you ensure your mobile applications are secure, compliant, and robust.

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📎 Useful Links

• MobSF GitHub Repo

• MobSF Docker Image

• Colima Documentation

🔍 Tools and Technologies I Use for Digital Forensics Investigations

Digital forensics plays a critical role in modern cybersecurity — whether it’s responding to a data breach, investigating insider threats, or performing incident analysis after suspicious behavior. In my work as a security-minded engineer and DevSecOps practitioner, I’ve frequently had to identify, collect, and analyze digital evidence across endpoints, servers, and cloud environments.

In this blog post, I’ll walk you through the tools and technologies I rely on to conduct effective digital forensics investigations — categorized by use case.

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🧠 What Is Digital Forensics?

At its core, digital forensics is about identifying, preserving, analyzing, and reporting on digital data in a way that’s legally sound and technically accurate. The goal is to reconstruct events, identify malicious activity, and support security incident response.

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🧰 My Go-To Tools for Digital Forensics Investigations

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🗂️ Disk & File System Analysis

These tools help examine hard drives, deleted files, system metadata, and more:

• Autopsy (The Sleuth Kit) – A GUI-based forensic suite for analyzing disk images, file recovery, and timelines.

• FTK Imager – For creating and previewing forensic images without altering the original evidence.

• dd / dc3dd – Command-line tools to create low-level forensic disk images in Linux environments.

• EnCase (Basic familiarity) – A commercial powerhouse in forensic investigations, used primarily for legal-grade evidence analysis.

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🧬 Memory Forensics

Memory (RAM) often holds short-lived but critical evidence, like injected malware, live sessions, or loaded processes.

• Volatility Framework – Extracts details like running processes, DLLs, command history, network activity, and more from memory dumps.

• Rekall – An alternative memory analysis framework focused on automation and deep system state inspection.

✅ I’ve used Volatility to trace injected PowerShell payloads and enumerate hidden processes in live incident simulations.

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🌐 Network Forensics

Capturing and analyzing network traffic is essential for spotting data exfiltration, command-and-control activity, or lateral movement.

• Wireshark – Industry standard for packet analysis and protocol dissection.

• tcpdump – Lightweight CLI tool to capture traffic in headless environments or remote systems.

• NetworkMiner – Parses PCAP files to extract files, sessions, and credentials automatically.

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📊 Log & Timeline Analysis

Understanding what happened — and when — is key to reconstructing incidents.

• Timesketch – A timeline analysis tool for visualizing and collaborating on event data.

• Log2Timeline (Plaso) – Converts log files, browser histories, and system events into structured timelines.

• Sysinternals Suite – Includes gems like Procmon, PsExec, and Autoruns for Windows incident response.

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🧪 Malware Analysis (Static & Dynamic)

Understanding what a file does — before or while it runs — helps detect advanced threats and APT tools.

• Ghidra – Powerful open-source reverse engineering tool from the NSA for analyzing executables.

• x64dbg / OllyDbg – Popular debuggers for inspecting Windows executables.

• Hybrid Analysis / VirusTotal – Cloud-based tools to scan files and observe sandbox behavior.

• Cuckoo Sandbox – An open-source automated sandbox for observing malware behavior in a VM.

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☁️ Cloud & Endpoint Forensics

Modern investigations often span cloud platforms and remote endpoints:

• AWS CloudTrail, GuardDuty – Audit user and API activity in cloud environments.

• Microsoft Azure Defender – For cloud-native threat detection and log correlation.

• CrowdStrike Falcon / SentinelOne – Endpoint Detection and Response (EDR) tools for retrieving artifacts, hunting threats, and isolating compromised machines.

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🧰 Scripting & Automation

Scripting accelerates collection, triage, and analysis — especially in large-scale environments.

• Python – I use it to build custom Volatility plugins, PCAP parsers, or automate alert triage.

• Bash / PowerShell – For live memory dumps, log gathering, process inspection, and rapid automation.

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🧩 MITRE ATT&CK & DFIR Methodology

I map artifacts and behaviors to MITRE ATT&CK techniques (e.g., T1055 – Process Injection) to align with industry standards and communicate findings effectively.

I also follow established methodologies like:

• SANS DFIR process

• NIST 800-61 Incident Handling Guide

• Custom playbooks for containment, eradication, and recovery

✅ Summary: Digital Forensics Tools I Use

🔹 Disk & File System Analysis

• Autopsy (Sleuth Kit) – GUI-based forensic suite

• FTK Imager – Create and inspect forensic images

• dd / dc3dd – Low-level disk imaging on Linux

• EnCase – Commercial tool for deep disk investigations (basic familiarity)

🔹 Memory Forensics

• Volatility – Extract processes, DLLs, and sessions from RAM dumps

• Rekall – Advanced volatile memory analysis

🔹 Network Forensics

• Wireshark – Protocol and packet analysis

• tcpdump – Command-line traffic capture

• NetworkMiner – Extracts files and sessions from PCAP files

🔹 Log & Timeline Analysis

• Timesketch – Timeline visualization and correlation

• Plaso (log2timeline) – Converts raw logs into a forensic timeline

• Sysinternals Suite – Live system inspection (Procmon, PsExec, Autoruns)

🔹 Malware Analysis

• Ghidra – Static reverse engineering

• x64dbg / OllyDbg – Debuggers for binary inspection

• Hybrid Analysis / VirusTotal – Behavioral analysis and threat intel

• Cuckoo Sandbox – Automated dynamic malware analysis

🔹 Cloud & Endpoint Forensics

• AWS CloudTrail / GuardDuty – Monitor API and security activity

• Microsoft Defender / Azure Logs – Cloud-native alerting and forensics

• CrowdStrike Falcon / SentinelOne – EDR tools for endpoint activity and IOC collection

🔹 Scripting & Automation

• Python – For custom plugins, log parsers, automation

• Bash / PowerShell – For system triage, memory dumps, and log collection

🔹 Methodology

• Align findings with MITRE ATT&CK

• Follow structured DFIR frameworks like SANS, NIST 800-61, and custom playbooks

🎯 Final Thoughts

Digital forensics isn’t just for breach responders — it’s a key skill for DevSecOps, SDETs, and any security-conscious engineer. Whether you’re building incident response workflows, simulating attacks, or validating your EDR, knowing how to collect and interpret evidence makes you far more effective.