Ruminating on Clickjacking

Clickjacking is a sort of cyberattack in which people are tricked into clicking on something they did not plan to click on. This can be accomplished by superimposing a malicious frame on top of a legal website or injecting a malicious link within an apparently innocent piece of content.

When a user clicks on what appears to be a legitimate website or link, they are in fact clicking on a malicious frame or link. This can then redirect users to a bogus website or run malicious programmes on their PC.

Clickjacking attacks are sometimes difficult to detect because they frequently depend on social engineering tactics to deceive users. For example, the attacker may develop a phoney website that appears to be the actual one, or they could give the victim a link that appears to be from a valid source.

To protect yourself against clickjacking, make use of a pop-up blocker (default in Chrome and many modern browsers).  Any website that asks you to enable Flash or JavaScript should be avoided. Hover your cursor over a link before clicking on it if you are unsure whether it is authentic. If the URL of the link changes, it is most likely malicious.

If you are a developer, please check out the following links to what can be done in your code to reduce the risk of clickjacking. 

https://cheatsheetseries.owasp.org/cheatsheets/Clickjacking_Defense_Cheat_Sheet.html

Ruminating on Shadow Testing or Shadow Mirroring

Shadow testing is a software testing technique that involves sending production traffic to a duplicate or shadow environment. This allows testers to compare the behavior of the new feature in the shadow environment to the behavior of the old feature in the production environment. This can help to identify any potential problems with the new feature before it is released to all users.

The following diagram from the Microsoft GitHub site illustrates this concept.

The following blogs/articles explain this concept in good detail:

• https://microsoft.github.io/code-with-engineering-playbook/automated-testing/shadow-testing/
• https://github.com/opendiffy/diffy
• https://blog.markvincze.com/shadow-mirroring-with-envoy/
• https://reshadat.medium.com/test-apis-without-writing-tests-with-twitters-diffy-8dfad1d92b5

Ruminating on Differential Privacy

Differential privacy (DP) is a mathematical paradigm for protecting individuals' privacy in datasets. By allowing data to be analysed without disclosing sensitive information about any individual in the dataset, it can protects the privacy of individuals. Thus, it is a method of protecting the privacy of people in a dataset while maintaining the dataset's overall usefulness.

To protect privacy, the most easy option is anonymization, which removes identifying information. A person's name, for example, may be erased from a medical record. Unfortunately, anonymization is rarely enough to provide privacy because the remaining information might be uniquely identifiable. For example, given a person's gender, postal code, age, ethnicity, and height, it may be able to identify them uniquely even in a massive database.

The concept behind differential privacy is to introduce noise into the data in such a manner that it is hard to verify whether any specific individual's data was included in the dataset. This is accomplished by assigning a random value to each data point, which is chosen in such a manner that it has no effect on the overall statistics of the dataset but makes identifying individual data points more difficult.

The following paper by Apple gives a very good overview of how Apple uses Differential Privacy to gain insight into what many Apple users are doing, while helping to preserve the privacy of individual users - https://www.apple.com/privacy/docs/Differential_Privacy_Overview.pdf

Epsilon (ε) is a parameter in differential privacy that affects the amount of noise introduced to the data. A greater epsilon number adds more noise, which gives more privacy but affects the accuracy of the findings.

Here are some examples of epsilon values that might be used in different applications:

• Healthcare: Epsilon might be set to a small value, such as 0.01, to ensure that the privacy of patients is protected.
• Marketing: Epsilon might be set to a larger value, such as 1.0, to allow for more accurate results.
• Government: Epsilon might be set to a very large value, such as 100.0, to allow for the analysis of large datasets without compromising the privacy of individuals.

Thus, the epsilon value chosen represents a trade-off between privacy and accuracy. The lower the epsilon number, the more private the data will be, but the findings will be less accurate. The greater the epsilon number, the more accurate the findings will be, but the data will be less private.

A deep dive into these techniques is illustrated in this paper - https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf

Ruminating on nip.io and Let's Encrypt

nip.io is a free, open-source service that allows you to use wildcard DNS for any IP address. This implies you may build a hostname that resolves to any IP address, no matter where it is. This may be beneficial for a number of things, including:

• Testing local machine applications. When constructing a local application, you may utilise nip.io to provide it a hostname that can be accessed from anywhere. This makes it simpler to test and distribute the application with others. This service has been made free by a company called as Powerdns. Examples: 
• 10.0.0.1.nip.io maps to 10.0.0.1
• 192-168-1-250.nip.io maps to 192.168.1.250
• 0a000803.nip.io maps to 10.0.8.3  (hexadecimal format)
• Many online services expect a hostname and do not accept an IP address. In such cases, you can simple append *.nip.io at the end of the public IP address and get a OOTB domain name :)
• Creae a SSL certificate using letsencrypt:  If you use the "dash" and "hexadecimal" notation of nip.io, then you can easily create a public SSL certificate using "Let's Encrypt" that would be honoured by all browsers. No need of struggling with self-signed certificates. 

ngrok is another great tool that should be in the arsenal of every developer. 

Ruminating on Observability

It is more critical than ever in today's complex and dispersed IT settings to have a complete grasp of how your systems are performing. This is where the concept of observability comes into play. The capacity to comprehend the condition of a system by gathering and analysing data from various sources is referred to as observability.

Observabilty has three critical pillars: 

• Distributed Logging (using ELK, Splunk)
• Metrics (performance instrumentation in code)
• Tracing (E2E visibility across the tech stack)

Distributed Logging: Logs keep track of events that happen in a system. They may be used to discover problems, performance bottlenecks, and the flow of traffic through a system. In a modern scalable distributed architecture, we need logging frameworks that support collection and ingestion of logs across the complete tech stack. Platforms such as Splunk and ELK (Elastic, Logstash, Kibana) support this and are popular frameworks for distributed logging. 

Metrics (performance instrumentation in code): Metrics are numerical measures of a system's status. They may be used to monitor CPU use, memory consumption, and request latency, among other things. Some of the most popular frameworks for metrics are Micrometer , Prometheus and DropWizard Metrics. 

Tracing (E2E visibility across the tech stack): Traces are a record of a request's route through a system. They may be utilised to determine the core cause of performance issues and to comprehend how various system components interact with one another. A unique Trace-ID is used to corelate the request across all the components of the tech stack. 

Platforms such as Dynatrace, AppDynamics and DataDog provide comprehensive features to implement all aspects of Observability. 

The three observability pillars operate together to offer a complete picture of a system's behaviour. By collecting and analysing data from all three sources, you can acquire a thorough picture of how your systems operate and discover possible issues before they affect your consumers.

There are a number of benefits to implementing the three pillars of observability. These benefits include:

• The ability to identify and troubleshoot problems faster
• The ability to improve performance and reliability
• The ability to make better decisions about system design and architecture

If you want to increase the observability of your systems, I recommend that you study more about the three pillars of observability and the many techniques to apply them. You can take your IT operations to the next level if you have a thorough grasp of observability.

Ruminating on Prompt Engineering

There has been a lot of buzz in recent years about the potential of large language models (LLMs) to develop new text forms, translate languages, compose various types of creative material, and answer your queries in an instructive manner. However, one of the drawbacks of LLMs is that they may be quite unexpected. Even little changes to the prompt might provide drastically different outcomes. This is where quick engineering comes into play.

The technique of creating prompts that are clear, explicit, and instructive is known as prompt engineering. You may maximise your chances of receiving the desired outcome from your LLM by properly writing your questions.

Given below are some of the techniques you can use to create better prompts:

• Be precise and concise: The more detailed your instruction, the more likely your LLM will get the intended result. Instead of asking, "Write me a poem," you may say, "Write me a poem about peace".
• Use keywords: Keywords are words or phrases related to the intended outcome. If you want your LLM to write a blog article about generative AI, for example, you might add keywords like "prompt engineering," "LLMs," and "generative AI."
• Provide context: Context is information that assists your LLM in comprehending the intended outcome. If you want your LLM to write a poetry about Spring, for example, you might add context by supplying a list of phrases around Spring.
• Provide examples: Use examples to demonstrate to your LLM what you are looking for. For example, if you want your LLM to create poetry, you may present samples of poems you appreciate.

Andrew NG has created an online course to learn about prompt engineering here - https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

In fact, the rise of LLMs has resulted in new job roles like "Prompt Engineer" as highlighted in the articles below: 

https://time.com/6272103/ai-prompt-engineer-job/

https://geekflare.com/prompt-engineering-career/

API mock servers from OpenAPI specs

 If you have an OpenAPI specs file (YAML or JSON), then you can quickly create a mock server using one of the following tools. 

• Microrocks: https://microcks.io/documentation/using/openapi/
• Prism: https://docs.stoplight.io/docs/prism/674b27b261c3c-overview
• OpenAPI mock: https://github.com/muonsoft/openapi-mock
• Imposter: https://www.imposter.sh/

A list of all other OpenAPI tools is given here: https://openapi.tools/