Online Developer Tools a Backdoor to Security Threat
Using online developer utilities like a JSON Viewer can be incredibly convenient for parsing and visualizing JSON data, but they also come with significant risks.
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Using online developer utilities, such as a JSON Viewer, can be incredibly convenient for parsing and visualizing JSON data, but they also come with significant risks. The tool, for instance, often requires users to upload JSON files or paste sensitive data directly into the tool. If the utility operates online without robust security measures, this data could be intercepted or stored without the user's knowledge, potentially exposing confidential information such as API keys, user credentials, or proprietary business logic.
One major concern is the lack of transparency in how some online tools handle uploaded data. For example, if the JSON Viewer utility does not explicitly state that it deletes data after processing, there is a risk that the data could be retained on the server, making it vulnerable to breaches. Additionally, if the tool is hosted on an unsecured or compromised website, attackers could exploit it to inject malicious scripts or steal data.
Another risk arises from the reliance on third-party tools. If this utility were to be discontinued or its domain hijacked, users might unknowingly expose their data to malicious actors. To mitigate these risks, developers should opt for offline versions of such utilities or use trusted, open-source tools that can be run locally. They should also avoid uploading sensitive data to online platforms unless absolutely necessary and ensure that the tool's privacy and security policies are thoroughly vetted.
By understanding these risks and taking appropriate precautions, developers can leverage the benefits of tools like JSON Viewer while safeguarding their data and projects.
JOLT Transformation
JOLT Transformation, a tool used for JSON data manipulation, can be highly effective but also introduces specific risks when used online. Like JSON Viewer, JOLT Transformation often requires users to upload JSON data to perform transformations. If the tool operates on an online platform without adequate security measures, sensitive data such as user credentials, API keys, or proprietary configurations could be exposed to unauthorized access or breaches.
For example, if JOLT Transformation is hosted on a compromised website or lacks encryption protocols, attackers could intercept the data during transmission. Additionally, if the tool retains uploaded data without clear policies on deletion, it could lead to unintended data storage on the server, increasing vulnerability to cyberattacks.
Another risk is dependency on third-party tools. If JOLT Transformation's online service is discontinued or its domain is hijacked, users might unknowingly expose their data to malicious actors. To mitigate these risks, developers should consider using offline versions of JOLT Transformation or open-source alternatives that can be run locally. They should also ensure that the tool's security practices, such as encryption and data handling policies, are thoroughly reviewed before use.
By taking these precautions, developers can leverage the powerful capabilities of JOLT Transformation while minimizing potential security risks.
In this article, we shall discuss a use case where I needed to extensively make use of JOLT transformation, and we could hardly rely on https://jolt-demo.appspot.com/, the official JOLT playground. I had to desperately look for solutions, but it seemed the process of availing a legitimate and compliant version of any available open-source application was too restrictive.
Left with no other choice, I started writing a simple yet easy-to-use utility that could be run on localhost, thus safeguarding all the data, including input JSON and transformation DSL. Before we start coding, let us quickly discuss JOLT transformation and refer to a couple of simple use cases to understand how it works and what it produces. Once we are done, we shall get ourselves a little acquainted with the Java SDK.
What Is JOLT?
JOLT (JSON Object Language for Transformation) is a JSON transformation library used to restructure, modify, and manipulate JSON data in a declarative manner. It allows you to change JSON structures without writing complex parsing logic, making it ideal for API response transformations and data processing.
Basic Operations in JOLT
JOLT provides several transformation operations, each serving a unique purpose:
1. Shift
- Moves data within JSON structures.
- Helps rename keys, change hierarchy, and reorganize data.
2. Modify-Default
- Adds default values to JSON keys if they are missing.
- Useful for enforcing expected fields.
3. Modify-Overwrite
- Updates or overwrites existing values in JSON.
- Useful for applying changes dynamically.
4. Remove
- Deletes specific keys or sections from the JSON.
- Helps in cleaning up unnecessary data.
5. Cardinality
- Converts single elements to arrays or vice versa.
- Helps in handling array structures dynamically.
6. Sort
- Sorts JSON keys into a specific order.
- Useful when a structured output is required.
Each operation is defined using a spec file, which outlines how the JSON should be transformed.
We shall mainly discuss shift operations while touching up the others so as not to deviate from the main focus.
JOLT Shift Operation
The shift operation in JOLT is one of the most fundamental and powerful transformations. It allows you to restructure JSON data by moving keys, renaming fields, and altering the hierarchy of the JSON structure.
Key Features of Shift Operation
- Reorganizing data – Moves fields to new locations in the JSON structure.
- Renaming keys – Modifies existing field names.
- Nested transformations – Can move data into deeper or shallower structures.
- Dynamic mapping – Can extract and map values based on patterns.
For example, the input JSON below needs to be transformed.
// SOURCE / INPUT JSON
{
"user": {
"name": "Alice",
"age": 30
}
}
// EXPECTED TARGET JSON
{
"profile": {
"fullName": "Alice",
"userAge": 30
}
}
To achieve the following, we use the following specification.
[
{
"operation": "shift",
"spec": {
"user": {
"name": "profile.fullName",
"age": "profile.userAge"
}
}
}
]
The below relation, therefore, explains the role of a Spec file:
Input JSON + Spec File (Transformation Logic declared in some JSON-based DSL ) -> Transformer Engine -> Output JSON
JOLT Transformations
The JOLT documentation provides a wide range of examples that describe the Spec File grammar. To test the above, we can visit https://jolt-demo.appspot.com/ and use its playground.
One can use this to experiment with the JOLT specification and the different types of operations it offers.
However, should you use it with real data and transformation logic? Let me help you decide on this. If you inspect the network traffic that triggers when the "Transform" button is clicked, you'd see this outbound HTTP request to the server:
curl 'https://jolt-demo.appspot.com/transform' \
-H 'accept: */*' \
-H 'accept-language: en-US,en;q=0.9,en-GB;q=0.8,bn;q=0.7' \
-H 'content-type: application/x-www-form-urlencoded; charset=UTF-8' \
-H 'origin: https://jolt-demo.appspot.com' \
-H 'priority: u=1, i' \
-H 'referer: https://jolt-demo.appspot.com/' \
-H 'sec-ch-ua: "Chromium";v="136", "Google Chrome";v="136", "Not.A/Brand";v="99"' \
-H 'sec-ch-ua-mobile: ?0' \
-H 'sec-ch-ua-platform: "Windows"' \
-H 'sec-fetch-dest: empty' \
-H 'sec-fetch-mode: cors' \
-H 'sec-fetch-site: same-origin' \
-H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36' \
-H 'x-requested-with: XMLHttpRequest' \
--data-raw 'input=%7B%0A++%22user%22%3A+%7B%0A++++%22name%22%3A+%22Alice%22%2C%0A++++%22age%22%3A+30%0A++%7D%0A%7D&spec=%5B%0A++%7B%0A++++%22operation%22%3A+%22shift%22%2C%0A++++%22spec%22%3A+%7B%0A++++++%22user%22%3A+%7B%0A++++++++%22name%22%3A+%22profile.fullName%22%2C%0A++++++++%22age%22%3A+%22profile.userAge%22%0A++++++%7D%0A++++%7D%0A++++%7D%0A%5D%0A&sort=false'
The transformation occurs on the server, and a normal user is neither aware of what happens to their data thereafter nor how it is purged, if at all. Sorry, but it should not be used where your data is sensitive. You might be using mocked, non-customer data, yet risking exposing your transformation logic is not worth the power of the utility.
To search for an alternative, you need to have something that complies with your organization as well as the customer you work for. It should fit in the budget and licensing policy. Spending heavily on something that's extremely sophisticated will be overkill. It would be best if one could write one's own tool providing similar functionality and execute only on one's local machine. Thus, the safety of the data is ensured, and developers can use and delete their local data as needed.
I'd come across a similar use case, and I wrote a simple Spring Web MVC application that served the same purpose. Please refer to the source code https://github.com/trainerpb/jolt-playground/tree/essential-tools. It's a minimally working project. This comprises not only a single utility but also many such utilities under the same umbrella, allowing them to be used locally. Developers' contributions to the betterment of this endeavour will benefit the entire developer community, especially when incidents of accidental data breaches are surfacing every day.
This video is a quick walk-through of the utility suite.
Video
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