Jsonapi Rb: Ruby Gem for Building and Consuming JSON API Documents.

jsonapi-rb

Ruby gem for building and consuming JSON API documents.

Status 

Resources

• Chat: gitter
• Twitter: @jsonapirb
• Docs: jsonapi-rb.org

Code

jsonapi-rb is simply a bundle of:

• jsonapi-serializable for serialization, and
• jsonapi-deserializable for deserialization.

For framework integrations, see:

• jsonapi-hanami for Hanami, and
• jsonapi-rails for Ruby on Rails.

Installation

# In Gemfile
gem 'jsonapi-rb'

then

$ bundle

or manually via

$ gem install jsonapi-rb

Usage and documentation

See jsonapi-rb.org/guides.

License

jsonapi-rb is released under the MIT License.

---

Author: jsonapi-rb
Source code: https://github.com/jsonapi-rb/jsonapi-rb
License: MIT license

#ruby #ruby-on-rails 

What is GEEK

Buddha Community

54% of Developers Cite Lack of Documentation as the Top Obstacle to Consuming APIs

Recently, I worked with my team at Postman to field the 2020 State of the API survey and report. We’re insanely grateful to the folks who participated—more than 13,500 developers and other professionals took the survey, helping make this the largest and most comprehensive survey in the industry. (Seriously folks, thank you!) Curious what we learned? Here are a few insights in areas that you might find interesting:

API Reliability

Whether internal, external, or partner, APIs are perceived as reliable—more than half of respondents stated that APIs do not break, stop working, or materially change specification often enough to matter. Respondents choosing the “not often enough to matter” option here came in at 55.8% for internal APIs, 60.4% for external APIs, and 61.2% for partner APIs.

Obstacles to Producing APIs

When asked about the biggest obstacles to producing APIs, lack of time is by far the leading obstacle, with 52.3% of respondents listing it. Lack of knowledge (36.4%) and people (35.1%) were the next highest.

#api #rest-api #apis #api-first-development #api-report #api-documentation #api-reliability #hackernoon-top-story

Phobos: Simplifying Kafka for Ruby Apps

Phobos

Simplifying Kafka for Ruby apps!

Phobos is a micro framework and library for applications dealing with Apache Kafka.

• It wraps common behaviors needed by consumers and producers in an easy and convenient API
• It uses ruby-kafka as its Kafka client and core component
• It provides a CLI for starting and stopping a standalone application ready to be used for production purposes

Why Phobos? Why not ruby-kafka directly? Well, ruby-kafka is just a client. You still need to write a lot of code to manage proper consuming and producing of messages. You need to do proper message routing, error handling, retrying, backing off and maybe logging/instrumenting the message management process. You also need to worry about setting up a platform independent test environment that works on CI as well as any local machine, and even on your deployment pipeline. Finally, you also need to consider how to deploy your app and how to start it.

With Phobos by your side, all this becomes smooth sailing.

Installation

Add this line to your application's Gemfile:

gem 'phobos'

And then execute:

$ bundle

Or install it yourself as:

$ gem install phobos

Usage

Phobos can be used in two ways: as a standalone application or to support Kafka features in your existing project - including Rails apps. It provides a CLI tool to run it.

Standalone apps

Standalone apps have benefits such as individual deploys and smaller code bases. If consuming from Kafka is your version of microservices, Phobos can be of great help.

Setup

To create an application with Phobos you need two things:

• A configuration file (more details in the Configuration file section)
• A phobos_boot.rb (or the name of your choice) to properly load your code into Phobos executor

Use the Phobos CLI command init to bootstrap your application. Example:

# call this command inside your app folder
$ phobos init
    create  config/phobos.yml
    create  phobos_boot.rb

phobos.yml is the configuration file and phobos_boot.rb is the place to load your code.

Consumers (listeners and handlers)

In Phobos apps listeners are configured against Kafka - they are our consumers. A listener requires a handler (a ruby class where you should process incoming messages), a Kafka topic, and a Kafka group_id. Consumer groups are used to coordinate the listeners across machines. We write the handlers and Phobos makes sure to run them for us. An example of a handler is:

class MyHandler
  include Phobos::Handler

  def consume(payload, metadata)
    # payload  - This is the content of your Kafka message, Phobos does not attempt to
    #            parse this content, it is delivered raw to you
    # metadata - A hash with useful information about this event, it contains: The event key,
    #            partition number, offset, retry_count, topic, group_id, and listener_id
  end
end

Writing a handler is all you need to allow Phobos to work - it will take care of execution, retries and concurrency.

To start Phobos the start command is used, example:

$ phobos start
[2016-08-13T17:29:59:218+0200Z] INFO  -- Phobos : <Hash> {:message=>"Phobos configured", :env=>"development"}
______ _           _
| ___ \ |         | |
| |_/ / |__   ___ | |__   ___  ___
|  __/| '_ \ / _ \| '_ \ / _ \/ __|
| |   | | | | (_) | |_) | (_) \__ \
\_|   |_| |_|\___/|_.__/ \___/|___/

phobos_boot.rb - find this file at ~/Projects/example/phobos_boot.rb

[2016-08-13T17:29:59:272+0200Z] INFO  -- Phobos : <Hash> {:message=>"Listener started", :listener_id=>"6d5d2c", :group_id=>"test-1", :topic=>"test"}

By default, the start command will look for the configuration file at config/phobos.yml and it will load the file phobos_boot.rb if it exists. In the example above all example files generated by the init command are used as is. It is possible to change both files, use -c for the configuration file and -b for the boot file. Example:

$ phobos start -c /var/configs/my.yml -b /opt/apps/boot.rb

You may also choose to configure phobos with a hash from within your boot file. In this case, disable loading the config file with the --skip-config option:

$ phobos start -b /opt/apps/boot.rb --skip-config

Consuming messages from Kafka

Messages from Kafka are consumed using handlers. You can use Phobos executors or include it in your own project as a library, but handlers will always be used. To create a handler class, simply include the module Phobos::Handler. This module allows Phobos to manage the life cycle of your handler.

A handler is required to implement the method #consume(payload, metadata).

Instances of your handler will be created for every message, so keep a constructor without arguments. If consume raises an exception, Phobos will retry the message indefinitely, applying the back off configuration presented in the configuration file. The metadata hash will contain a key called retry_count with the current number of retries for this message. To skip a message, simply return from #consume.

The metadata hash will also contain a key called headers with the headers of the consumed message.

When the listener starts, the class method .start will be called with the kafka_client used by the listener. Use this hook as a chance to setup necessary code for your handler. The class method .stop will be called during listener shutdown.

class MyHandler
  include Phobos::Handler

  def self.start(kafka_client)
    # setup handler
  end

  def self.stop
    # teardown
  end

  def consume(payload, metadata)
    # consume or skip message
  end
end

It is also possible to control the execution of #consume with the method #around_consume(payload, metadata). This method receives the payload and metadata, and then invokes #consume method by means of a block; example:

class MyHandler
  include Phobos::Handler

  def around_consume(payload, metadata)
    Phobos.logger.info "consuming..."
    output = yield payload, metadata
    Phobos.logger.info "done, output: #{output}"
  end

  def consume(payload, metadata)
    # consume or skip message
  end
end

Note: around_consume was previously defined as a class method. The current code supports both implementations, giving precendence to the class method, but future versions will no longer support .around_consume.

class MyHandler
  include Phobos::Handler

  def self.around_consume(payload, metadata)
    Phobos.logger.info "consuming..."
    output = yield payload, metadata
    Phobos.logger.info "done, output: #{output}"
  end

  def consume(payload, metadata)
    # consume or skip message
  end
end

Take a look at the examples folder for some ideas.

The hander life cycle can be illustrated as:

.start -> #consume -> .stop

or optionally,

.start -> #around_consume [ #consume ] -> .stop

Batch Consumption

In addition to the regular handler, Phobos provides a BatchHandler. The basic ideas are identical, except that instead of being passed a single message at a time, the BatchHandler is passed a batch of messages. All methods follow the same pattern as the regular handler except that they each end in _batch and are passed an array of Phobos::BatchMessages instead of a single payload.

To enable handling of batches on the consumer side, you must specify a delivery method of inline_batch in phobos.yml, and your handler must include BatchHandler. Using a delivery method of batch assumes that you are still processing the messages one at a time and should use Handler.

When using inline_batch, each instance of Phobos::BatchMessage will contain an instance method headers with the headers for that message.

class MyBatchHandler
  include Phobos::BatchHandler

  def around_consume_batch(payloads, metadata)
    payloads.each do |p|
      p.payload[:timestamp] = Time.zone.now
    end

    yield payloads, metadata
  end

  def consume_batch(payloads, metadata)
    payloads.each do |p|
      logger.info("Got payload #{p.payload}, #{p.partition}, #{p.offset}, #{p.key}, #{p.payload[:timestamp]}")
    end
  end

end

Note that retry logic will happen on the batch level in this case. If you are processing messages individually and an error happens in the middle, Phobos's retry logic will retry the entire batch. If this is not the behavior you want, consider using batch instead of inline_batch.

Producing messages to Kafka

ruby-kafka provides several options for publishing messages, Phobos offers them through the module Phobos::Producer. It is possible to turn any ruby class into a producer (including your handlers), just include the producer module, example:

class MyProducer
  include Phobos::Producer
end

Phobos is designed for multi threading, thus the producer is always bound to the current thread. It is possible to publish messages from objects and classes, pick the option that suits your code better. The producer module doesn't pollute your classes with a thousand methods, it includes a single method the class and in the instance level: producer.

my = MyProducer.new
my.producer.publish(topic: 'topic', payload: 'message-payload', key: 'partition and message key')

# The code above has the same effect of this code:
MyProducer.producer.publish(topic: 'topic', payload: 'message-payload', key: 'partition and message key')

The signature for the publish method is as follows:

def publish(topic: topic, payload: payload, key: nil, partition_key: nil, headers: nil)

When publishing a message with headers, the headers argument must be a hash:

my = MyProducer.new
my.producer.publish(topic: 'topic', payload: 'message-payload', key: 'partition and message key', headers: { header_1: 'value 1' })

It is also possible to publish several messages at once:

MyProducer
  .producer
  .publish_list([
    { topic: 'A', payload: 'message-1', key: '1' },
    { topic: 'B', payload: 'message-2', key: '2' },
    { topic: 'B', payload: 'message-3', key: '3', headers: { header_1: 'value 1', header_2: 'value 2' } }
  ])

There are two flavors of producers: regular producers and async producers.

Regular producers will deliver the messages synchronously and disconnect, it doesn't matter if you use publish or publish_list; by default, after the messages get delivered the producer will disconnect.

Async producers will accept your messages without blocking, use the methods async_publish and async_publish_list to use async producers.

An example of using handlers to publish messages:

class MyHandler
  include Phobos::Handler
  include Phobos::Producer

  PUBLISH_TO = 'topic2'

  def consume(payload, metadata)
    producer.async_publish(topic: PUBLISH_TO, payload: {key: 'value'}.to_json)
  end
end

Note about configuring producers

Since the handler life cycle is managed by the Listener, it will make sure the producer is properly closed before it stops. When calling the producer outside a handler remember, you need to shutdown them manually before you close the application. Use the class method async_producer_shutdown to safely shutdown the producer.

Without configuring the Kafka client, the producers will create a new one when needed (once per thread). To disconnect from kafka call kafka_client.close.

# This method will block until everything is safely closed
MyProducer
  .producer
  .async_producer_shutdown

MyProducer
  .producer
  .kafka_client
  .close

Note about producers with persistent connections

By default, regular producers will automatically disconnect after every publish call. You can change this behavior (which reduces connection overhead, TLS etc - which increases speed significantly) by setting the persistent_connections config in phobos.yml. When set, regular producers behave identically to async producers and will also need to be shutdown manually using the sync_producer_shutdown method.

Since regular producers with persistent connections have open connections, you need to manually disconnect from Kafka when ending your producers' life cycle:

MyProducer
  .producer
  .sync_producer_shutdown

Phobos as a library in an existing project

When running as a standalone service, Phobos sets up a Listener and Executor for you. When you use Phobos as a library in your own project, you need to set these components up yourself.

First, call the method configure with the path of your configuration file or with configuration settings hash.

Phobos.configure('config/phobos.yml')

or

Phobos.configure(kafka: { client_id: 'phobos' }, logger: { file: 'log/phobos.log' })

Listener connects to Kafka and acts as your consumer. To create a listener you need a handler class, a topic, and a group id.

listener = Phobos::Listener.new(
  handler: Phobos::EchoHandler,
  group_id: 'group1',
  topic: 'test'
)

# start method blocks
Thread.new { listener.start }

listener.id # 6d5d2c (all listeners have an id)
listener.stop # stop doesn't block

This is all you need to consume from Kafka with back off retries.

An executor is the supervisor of all listeners. It loads all listeners configured in phobos.yml. The executor keeps the listeners running and restarts them when needed.

executor = Phobos::Executor.new

# start doesn't block
executor.start

# stop will block until all listers are properly stopped
executor.stop

When using Phobos executors you don't care about how listeners are created, just provide the configuration under the listeners section in the configuration file and you are good to go.

Configuration file

The configuration file is organized in 6 sections. Take a look at the example file, config/phobos.yml.example.

The file will be parsed through ERB so ERB syntax/file extension is supported beside the YML format.

logger configures the logger for all Phobos components. It automatically outputs to STDOUT and it saves the log in the configured file.

kafka provides configurations for every Kafka::Client created over the application. All options supported by ruby-kafka can be provided.

producer provides configurations for all producers created over the application, the options are the same for regular and async producers. All options supported by ruby-kafka can be provided. If the kafka key is present under producer, it is merged into the top-level kafka, allowing different connection configuration for producers.

consumer provides configurations for all consumer groups created over the application. All options supported by ruby-kafka can be provided. If the kafka key is present under consumer, it is merged into the top-level kafka, allowing different connection configuration for consumers.

backoff Phobos provides automatic retries for your handlers. If an exception is raised, the listener will retry following the back off configured here. Backoff can also be configured per listener.

listeners is the list of listeners configured. Each listener represents a consumer group.

Additional listener configuration

In some cases it's useful to share most of the configuration between multiple phobos processes, but have each process run different listeners. In that case, a separate yaml file can be created and loaded with the -l flag. Example:

$ phobos start -c /var/configs/my.yml -l /var/configs/additional_listeners.yml

Note that the config file must still specify a listeners section, though it can be empty.

Custom configuration/logging

Phobos can be configured using a hash rather than the config file directly. This can be useful if you want to do some pre-processing before sending the file to Phobos. One particularly useful aspect is the ability to provide Phobos with a custom logger, e.g. by reusing the Rails logger:

Phobos.configure(
  custom_logger: Rails.logger,
  custom_kafka_logger: Rails.logger
)

If these keys are given, they will override the logger keys in the Phobos config file.

Instrumentation

Some operations are instrumented using Active Support Notifications.

In order to receive notifications you can use the module Phobos::Instrumentation, example:

Phobos::Instrumentation.subscribe('listener.start') do |event|
  puts(event.payload)
end

Phobos::Instrumentation is a convenience module around ActiveSupport::Notifications, feel free to use it or not. All Phobos events are in the phobos namespace. Phobos::Instrumentation will always look at phobos. events.

Executor notifications

• executor.retry_listener_error is sent when the listener crashes and the executor wait for a restart. It includes the following payload:
  • listener_id
  • retry_count
  • waiting_time
  • exception_class
  • exception_message
  • backtrace
• executor.stop is sent when executor stops

Listener notifications

• listener.start_handler is sent when invoking handler.start(kafka_client). It includes the following payload:
  • listener_id
  • group_id
  • topic
  • handler
• listener.start is sent when listener starts. It includes the following payload:
  • listener_id
  • group_id
  • topic
  • handler
• listener.process_batch is sent after process a batch. It includes the following payload:
  • listener_id
  • group_id
  • topic
  • handler
  • batch_size
  • partition
  • offset_lag
  • highwater_mark_offset
• listener.process_message is sent after processing a message. It includes the following payload:
  • listener_id
  • group_id
  • topic
  • handler
  • key
  • partition
  • offset
  • retry_count
• listener.process_batch_inline is sent after processing a batch with batch_inline mode. It includes the following payload:
  • listener_id
  • group_id
  • topic
  • handler
  • batch_size
  • partition
  • offset_lag
  • retry_count
• listener.retry_handler_error is sent after waiting for handler#consume retry. It includes the following payload:
  • listener_id
  • group_id
  • topic
  • handler
  • key
  • partition
  • offset
  • retry_count
  • waiting_time
  • exception_class
  • exception_message
  • backtrace
• listener.retry_handler_error_batch is sent after waiting for handler#consume_batch retry. It includes the following payload:
  • listener_id
  • group_id
  • topic
  • handler
  • batch_size
  • partition
  • offset_lag
  • retry_count
  • waiting_time
  • exception_class
  • exception_message
  • backtrace
• listener.retry_aborted is sent after waiting for a retry but the listener was stopped before the retry happened. It includes the following payload:
  • listener_id
  • group_id
  • topic
  • handler
• listener.stopping is sent when the listener receives signal to stop.
  • listener_id
  • group_id
  • topic
  • handler
• listener.stop_handler is sent after stopping the handler.
  • listener_id
  • group_id
  • topic
  • handler
• listener.stop is send after stopping the listener.
  • listener_id
  • group_id
  • topic
  • handler

Plugins

List of gems that enhance Phobos:

Phobos DB Checkpoint is drop in replacement to Phobos::Handler, extending it with the following features:

• Persists your Kafka events to an active record compatible database
• Ensures that your handler will consume messages only once
• Allows your system to quickly reprocess events in case of failures

Phobos Checkpoint UI gives your Phobos DB Checkpoint powered app a web gui with the features below. Maintaining a Kafka consumer app has never been smoother:

• Search events and inspect payload
• See failures and retry / delete them

Phobos Prometheus adds prometheus metrics to your phobos consumer.

• Measures total messages and batches processed
• Measures total duration needed to process each message (and batch)
• Adds /metrics endpoit to scrape data

Development

After checking out the repo:

• make sure docker is installed and running (for windows and mac this also includes docker-compose).
• Linux: make sure docker-compose is installed and running.
• run bin/setup to install dependencies
• run docker-compose up -d --force-recreate kafka zookeeper to start the required kafka containers
• run tests to confirm no environmental issues
  • wait a few seconds for kafka broker to get set up - sleep 30
  • run docker-compose run --rm test
  • make sure it reports X examples, 0 failures

You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

Test

Phobos exports a spec helper that can help you test your consumer. The Phobos lifecycle will conveniently be activated for you with minimal setup required.

• process_message(handler:, payload:, metadata: {}, encoding: nil) - Invokes your handler with payload and metadata, using a dummy listener (encoding and metadata are optional).

### spec_helper.rb
require 'phobos/test/helper'
RSpec.configure do |config|
  config.include Phobos::Test::Helper
  config.before(:each) do
    Phobos.configure(path_to_my_config_file)
  end
end 

### Spec file
describe MyConsumer do
  let(:payload) { 'foo' }
  let(:metadata) { Hash(foo: 'bar') }

  it 'consumes my message' do
    expect_any_instance_of(described_class).to receive(:around_consume).with(payload, metadata).once.and_call_original
    expect_any_instance_of(described_class).to receive(:consume).with(payload, metadata).once.and_call_original

    process_message(handler: described_class, payload: payload, metadata: metadata)
  end
end

Upgrade Notes

Version 2.0 removes deprecated ways of defining producers and consumers:

• The before_consume method has been removed. You can have this behavior in the first part of an around_consume method.
• around_consume is now only available as an instance method, and it must yield the values to pass to the consume method.
• publish and async_publish now only accept keyword arguments, not positional arguments.

Example pre-2.0:

class MyHandler
  include Phobos::Handler

  def before_consume(payload, metadata)
    payload[:id] = 1
  end

  def self.around_consume(payload, metadata)
    metadata[:key] = 5
    yield
  end
end

In 2.0:

class MyHandler
  include Phobos::Handler

  def around_consume(payload, metadata)
    new_payload = payload.dup
    new_metadata = metadata.dup
    new_payload[:id] = 1
    new_metadata[:key] = 5
    yield new_payload, new_metadata
  end
end

Producer, 1.9:

  producer.publish('my-topic', { payload_value: 1}, 5, 3, {header_val: 5})

Producer 2.0:

  producer.publish(topic: 'my-topic', payload: { payload_value: 1}, key: 5, 
     partition_key: 3, headers: { header_val: 5})

Version 1.8.2 introduced a new persistent_connections setting for regular producers. This reduces the number of connections used to produce messages and you should consider setting it to true. This does require a manual shutdown call - please see Producers with persistent connections.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/klarna/phobos.

Linting

Phobos projects Rubocop to lint the code, and in addition all projects use Rubocop Rules to maintain a shared rubocop configuration. Updates to the shared configurations are done in phobos/shared repo, where you can also find instructions on how to apply the new settings to the Phobos projects.

Acknowledgements

Thanks to Sebastian Norde for the awesome logo!

Author: Phobos
Source Code: https://github.com/phobos/phobos 
License: Apache-2.0 license

#ruby #kafka 

Top 10 API Security Threats Every API Team Should Know

As more and more data is exposed via APIs either as API-first companies or for the explosion of single page apps/JAMStack, API security can no longer be an afterthought. The hard part about APIs is that it provides direct access to large amounts of data while bypassing browser precautions. Instead of worrying about SQL injection and XSS issues, you should be concerned about the bad actor who was able to paginate through all your customer records and their data.

Typical prevention mechanisms like Captchas and browser fingerprinting won’t work since APIs by design need to handle a very large number of API accesses even by a single customer. So where do you start? The first thing is to put yourself in the shoes of a hacker and then instrument your APIs to detect and block common attacks along with unknown unknowns for zero-day exploits. Some of these are on the OWASP Security API list, but not all.

Insecure pagination and resource limits

Most APIs provide access to resources that are lists of entities such as /users or /widgets. A client such as a browser would typically filter and paginate through this list to limit the number items returned to a client like so:

First Call: GET /items?skip=0&take=10 
Second Call: GET /items?skip=10&take=10

However, if that entity has any PII or other information, then a hacker could scrape that endpoint to get a dump of all entities in your database. This could be most dangerous if those entities accidently exposed PII or other sensitive information, but could also be dangerous in providing competitors or others with adoption and usage stats for your business or provide scammers with a way to get large email lists. See how Venmo data was scraped

A naive protection mechanism would be to check the take count and throw an error if greater than 100 or 1000. The problem with this is two-fold:

1. For data APIs, legitimate customers may need to fetch and sync a large number of records such as via cron jobs. Artificially small pagination limits can force your API to be very chatty decreasing overall throughput. Max limits are to ensure memory and scalability requirements are met (and prevent certain DDoS attacks), not to guarantee security.
2. This offers zero protection to a hacker that writes a simple script that sleeps a random delay between repeated accesses.

skip = 0
while True:    response = requests.post('https://api.acmeinc.com/widgets?take=10&skip=' + skip),                      headers={'Authorization': 'Bearer' + ' ' + sys.argv[1]})    print("Fetched 10 items")    sleep(randint(100,1000))    skip += 10

How to secure against pagination attacks

To secure against pagination attacks, you should track how many items of a single resource are accessed within a certain time period for each user or API key rather than just at the request level. By tracking API resource access at the user level, you can block a user or API key once they hit a threshold such as “touched 1,000,000 items in a one hour period”. This is dependent on your API use case and can even be dependent on their subscription with you. Like a Captcha, this can slow down the speed that a hacker can exploit your API, like a Captcha if they have to create a new user account manually to create a new API key.

Insecure API key generation

Most APIs are protected by some sort of API key or JWT (JSON Web Token). This provides a natural way to track and protect your API as API security tools can detect abnormal API behavior and block access to an API key automatically. However, hackers will want to outsmart these mechanisms by generating and using a large pool of API keys from a large number of users just like a web hacker would use a large pool of IP addresses to circumvent DDoS protection.

How to secure against API key pools

The easiest way to secure against these types of attacks is by requiring a human to sign up for your service and generate API keys. Bot traffic can be prevented with things like Captcha and 2-Factor Authentication. Unless there is a legitimate business case, new users who sign up for your service should not have the ability to generate API keys programmatically. Instead, only trusted customers should have the ability to generate API keys programmatically. Go one step further and ensure any anomaly detection for abnormal behavior is done at the user and account level, not just for each API key.

Accidental key exposure

APIs are used in a way that increases the probability credentials are leaked:

1. APIs are expected to be accessed over indefinite time periods, which increases the probability that a hacker obtains a valid API key that’s not expired. You save that API key in a server environment variable and forget about it. This is a drastic contrast to a user logging into an interactive website where the session expires after a short duration.
2. The consumer of an API has direct access to the credentials such as when debugging via Postman or CURL. It only takes a single developer to accidently copy/pastes the CURL command containing the API key into a public forum like in GitHub Issues or Stack Overflow.
3. API keys are usually bearer tokens without requiring any other identifying information. APIs cannot leverage things like one-time use tokens or 2-factor authentication.

If a key is exposed due to user error, one may think you as the API provider has any blame. However, security is all about reducing surface area and risk. Treat your customer data as if it’s your own and help them by adding guards that prevent accidental key exposure.

How to prevent accidental key exposure

The easiest way to prevent key exposure is by leveraging two tokens rather than one. A refresh token is stored as an environment variable and can only be used to generate short lived access tokens. Unlike the refresh token, these short lived tokens can access the resources, but are time limited such as in hours or days.

The customer will store the refresh token with other API keys. Then your SDK will generate access tokens on SDK init or when the last access token expires. If a CURL command gets pasted into a GitHub issue, then a hacker would need to use it within hours reducing the attack vector (unless it was the actual refresh token which is low probability)

Exposure to DDoS attacks

APIs open up entirely new business models where customers can access your API platform programmatically. However, this can make DDoS protection tricky. Most DDoS protection is designed to absorb and reject a large number of requests from bad actors during DDoS attacks but still need to let the good ones through. This requires fingerprinting the HTTP requests to check against what looks like bot traffic. This is much harder for API products as all traffic looks like bot traffic and is not coming from a browser where things like cookies are present.

Stopping DDoS attacks

The magical part about APIs is almost every access requires an API Key. If a request doesn’t have an API key, you can automatically reject it which is lightweight on your servers (Ensure authentication is short circuited very early before later middleware like request JSON parsing). So then how do you handle authenticated requests? The easiest is to leverage rate limit counters for each API key such as to handle X requests per minute and reject those above the threshold with a 429 HTTP response. There are a variety of algorithms to do this such as leaky bucket and fixed window counters.

Incorrect server security

APIs are no different than web servers when it comes to good server hygiene. Data can be leaked due to misconfigured SSL certificate or allowing non-HTTPS traffic. For modern applications, there is very little reason to accept non-HTTPS requests, but a customer could mistakenly issue a non HTTP request from their application or CURL exposing the API key. APIs do not have the protection of a browser so things like HSTS or redirect to HTTPS offer no protection.

How to ensure proper SSL

Test your SSL implementation over at Qualys SSL Test or similar tool. You should also block all non-HTTP requests which can be done within your load balancer. You should also remove any HTTP headers scrub any error messages that leak implementation details. If your API is used only by your own apps or can only be accessed server-side, then review Authoritative guide to Cross-Origin Resource Sharing for REST APIs

Incorrect caching headers

APIs provide access to dynamic data that’s scoped to each API key. Any caching implementation should have the ability to scope to an API key to prevent cross-pollution. Even if you don’t cache anything in your infrastructure, you could expose your customers to security holes. If a customer with a proxy server was using multiple API keys such as one for development and one for production, then they could see cross-pollinated data.

#api management #api security #api best practices #api providers #security analytics #api management policies #api access tokens #api access #api security risks #api access keys

Public ASX100 APIs: The Essential List

We’ve conducted some initial research into the public APIs of the ASX100 because we regularly have conversations about what others are doing with their APIs and what best practices look like. Being able to point to good local examples and explain what is happening in Australia is a key part of this conversation.

Method

The method used for this initial research was to obtain a list of the ASX100 (as of 18 September 2020). Then work through each company looking at the following:

1. Whether the company had a public API: this was found by googling “[company name] API” and “[company name] API developer” and “[company name] developer portal”. Sometimes the company’s website was navigated or searched.
2. Some data points about the API were noted, such as the URL of the portal/documentation and the method they used to publish the API (portal, documentation, web page).
3. Observations were recorded that piqued the interest of the researchers (you will find these below).
4. Other notes were made to support future research.
5. You will find a summary of the data in the infographic below.

Data

With regards to how the APIs are shared:

• Documentation is where just the Swagger/OpenAPI docs are hosted somewhere. e.g. https://api.linkgroup.com/member/docs/index
• Portal is where an interactive portal has been developed. e.g. https://dev.telstra.com/
• The web page is where the company has a single page that isn’t interactive explaining their Public APIs. e.g. https://www.bendigoadelaide.com.au/banking-products-api.asp

#api #api-development #api-analytics #apis #api-integration #api-testing #api-security #api-gateway

Consume Web API Get method in ASP NET MVC | Calling Web API | Rest API Bangla Tutorial

https://youtu.be/WE9XwT0uzTY

#api #rest api #asp.net api #restful api #api tutorial #consume api