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Automating REST Security Part 1: Challenges
Although REST has been a dominant choice for API design for the last decade, there is still little dedicated security research on the subject of REST APIs. The popularity of REST contrasts with a surprisingly small number of systematic approaches to REST security analysis. This contrast is also reflected in the low availability of analysis tools and best security practices that services may use to check if their API is secure.
In this blog series, we try to find reasons for this situation and what we can do about it. In particular, we will investigate why general REST security assessments seem more complicated than other API architectures. We will likewise discuss how we may still find systematic approaches for REST API analysis despite REST's challenges. Furthermore, we will present REST-Attacker, a novel analysis tool designed for automated REST API security testing. In this context, we will examine some of the practical tests provided by REST-Attacker and explore the test results for a small selection of real-world API implementations.
Author
Christoph Heine
Overview
- Automating REST Security Part 1: Challenges
- Automating REST Security Part 2: Tool-based Analysis with REST-Attacker
- Automating REST Security Part 3: Practical Tests for Real-World APIs
Understanding the Problem with REST
When evaluating network components and software security, we often rely on specifications for how things should work. For example, central authorities like the IETF standardize many popular web technologies such as HTTP, TLS or DNS. API architectures and designs can also be standardized. Examples of these technologies are SOAP and the more recent GraphQL language specification. Standardization of web standards usually influences their security. Drafting may involve a public review process before publication. This process can identify security flaws or allow the formulation of official implementation and usage best practices. Best practices are great for security research as a specification presents clear guidelines on how an implementation should behave and why.
The situation for REST is slightly different. First of all, REST is not a standard in the sense that there is no technical specification for its implementation. Instead, REST is an architecture style which is more comparable to a collection of paradigms (client-server architecture, statelessness, cacheability, uniform interface, layering, and code-on-demand). Notably, REST has no strict dependency on other web technologies. It only defines how developers should use components but not what components they should use. This paradigm makes REST very flexible as developers are not limited to any particular protocol, library, or data structure.
Furthermore, no central authority could define rules or implementation guidelines. Roy Fielding created the original definition of REST as a design template for the HTTP/1.1 standard in 2000. It is the closest document resembling a standard. However, the document merely explains the REST paradigms and does not focus on security implications.
The flexibility of the REST architecture is probably one of the primary reasons why security research can be challenging. If every implementation is potentially different, how are we supposed to create common best practices, let alone test them consistently across hundreds of APIs? Fortunately for us, not every API tries to reinvent the wheel entirely. In practice, there are a lot of similarities between implementations that may be used to our advantage.
Generalizing REST Security
The most glaring similarity between REST API implementations is that most, if not all, are based on HTTP. If you have worked with REST APIs before, this statement might sound like stating the obvious. However, remember that REST technically does not require a specific protocol. Assuming that every REST API uses HTTP, we can use it as a starting point for a generalization of REST API security. Knowing that we mainly deal with HTTP is also advantageous because HTTP - unlike REST - is standardized. Although HTTP is still complex, it gives us a general idea of what we can expect.
Another observation is that REST API implementations reuse several standardized components in HTTP for API communication. Control parameters and actions in an API request are mapped to components in a generic HTTP request. For example, a resource that an API request operates on, is specified via the HTTP URL. Actions or operations on the said resource are identified and mapped to HTTP methods defined by the HTTP standard, usually GET, POST, DELETE, PUT, and PATCH. API operations retain their intended action from HTTP, i.e., GET retrieves a resource, DELETE removes a resource, and so on. In REST API documentation, we can often find a description of available API endpoints using HTTP "language":
Since the URL and the HTTP method are sufficient to build a basic HTTP request, we can potentially create an API requests if we know a list of REST endpoints. In practice, the construction of such requests can be more complicated because the API may have additional parameter requirements for their requests, e.g., query, header, or body content. Another problem is finding valid IDs of resources can be difficult. Interestingly, we can infer each endpoint's action based on the HTTP method, even without any context-specific knowledge about the API.
We can also find components taken from the HTTP standard in the API response. The requested operation's success or failure is usually indicated using HTTP status codes. They retain their meaning when used in REST APIs. For example, a 200 status code indicates success, while a 401 status code signifies missing authorization (in the preceding API request). This behavior again can be inferred without knowing the exact purpose of the API.
Another factor that influences REST's complexity is its statelessness paradigm. Essentially, statelessness requires that the server does not keep a session between individual requests. As a result, every client request must be self-contained, so multi-message operations are out of the picture. It also effectively limits interaction with the API to two HTTP messages: client request and server response. Not only does this make API communication easier to comprehend, but it also makes testing more manageable since we don't have to worry as much about side effects or keeping track of an operations state.
Implementing access control mechanisms can be more complicated, but we can still find general similarities. While REST does not require any particular authentication or authorization methods, the variety of approaches found in practice is small. REST API implementations usually implement a selection of these methods:
- HTTP Basic Authentication (user authentication)
- API keys (client authentication)
- OAuth2 (authorization)
Two of these methods, OAuth2 and HTTP Basic Authentication, are standardized, while API keys are relatively simple to handle. Therefore, we can generalize access control to some degree. However, access control can be one of the trickier parts of API communication as there may be a lot of API-specific configurations. For example, OAuth2 authorization allows the API to define multiple access levels that may be required to access different resources or operations. How access control data is delivered in the HTTP message may also depend on the API, e.g., by requiring encoding of credentials or passing them in a specified location of the HTTP message (e.g. header, query, or body).
Finding a Systematic Approach for REST API Analysis
So far, we've only discussed theoretical approaches scatching a generic REST API analysis. For implementing an automated analysis tool, we need to adopt the hints that we used for our theoretical API analyses to the tool. For example, the tool would need to know which API endpoints exist to create API requests on its own.
The OpenAPI specification is a popular REST API description format that can be used for such purpose. An OpenAPI file contains a machine-readable definition (as JSON or YAML) of an API's interface. Basic descriptions include the definition of the API endpoints, but can optionally contain much more content and other types of useful information. For example, an endpoint definition may include a list of required parameters for requests, possible response codes and content schemas of API responses. The OpenAPI can even describe security requirements that define what types of access control methods are used.
{ "openapi": "3.1.0", "info": { "title": "Example API", "version": "1.0" }, "servers": [ { "url": "http://api.example.com" } ], "paths": { "/user/info": { "get": { "description": "Returns information about a user.", "parameters": [ { "name": "id", "in": "query", "description": "User ID", "required": true } ], "responses": { "200": { "description": "User information.", "content": { "application/json": { "schema": { "type": "object", "items": { "$ref": "#/components/schemas/user_info" } } } } } } } } }, "security": [ { "api_key": [] } ] } As you can see from the example above, OpenAPI files allow tools to both understand the API and use the available information to create valid API requests. Furthermore, the definition can give insight into the expected behavior of the API, e.g., by checking the response definitions. These properties make the OpenAPI format another standard on which we can rely. Essentially, a tool that can parse and understand OpenAPI can understand any generic API. With the help of OpenAPI, tools can create and execute tests for APIs automatically. Of course, the ability of tools to derive tests still depends on how much information an OpenAPI file provides. However, wherever possible, automation can potentially eliminate a lot of manual work in the testing process.
Conclusion
When we consider the similarities between REST APIs and OpenAPI descriptions, we can see that there is potential for analyzing REST security with tools. Our next blog post discusses how such an implementation would look like. We will discuss REST-Attacker, our tool for analyzing REST APIs.
Further Reading
The feasibility of tool-based REST analysis has also been discussed in scientific papers. If you want to know more about the topic, you can start here:
- Atlidakis et al., Checking Security Properties of Cloud Service REST APIs (DOI Link)
- Lo et al., On the Need for a General REST-Security Framework (DOI Link)
- Nguyen et al., On the Security Expressiveness of REST-Based API Definition Languages (DOI Link)
Acknowledgement
The REST-Attacker project was developed as part of a master's thesis at the Chair of Network & Data Security of the Ruhr University Bochum. I would like to thank my supervisors Louis Jannett, Christian Mainka, Vladislav Mladenov, and Jörg Schwenk for their continued support during the development and review of the project.
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Networking | Switching And Routing | Tutorial 1 | 2018
Welcome to my new series of tutorials about networking. Moreover in this series I'll discuss briefly each and every thing related to routing and switching. After that you will able to pass an exam of HCNA, CCNA etc. First of all you have to know which software is used by which company such as Huawei used its own software named eNSP while Cisco used its own software named Cisco Packet Tracer. After that you have to know that how to download and install both of the software in your computer systems. So the purpose of this blog is to give you people an overview about how to download and install both of them.
What is a Network?
First of all we must have to know about what is a network. So the network is the interconnection of two or more than two devices in such a way that they can communicate each other. In computer networks we can say that the interconnection of two or more than two end devices (computer, laptops, printers etc) for the sake of sending and receiving some amount of data is known as computer network.What is Internet?
The very simple and easily understandable definition of a internet is "The network of networks". Now what is meant by that? When different networks from the different areas or at the same areas wanna communicate with each other then internet formed. So we can say that "Internet is the interconnection of different networks in such a way that networks can communicate with each other".Related articles
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Blockchain Exploitation Labs - Part 3 Exploiting Integer Overflows And Underflows
In part 1 and 2 we covered re-entrancy and authorization attack scenarios within the Ethereum smart contract environment. In this blog we will cover integer attacks against blockchain decentralized applications (DAPs) coded in Solidity.
Integer Attack Explanation:
An integer overflow and underflow happens when a check on a value is used with an unsigned integer, which either adds or subtracts beyond the limits the variable can hold. If you remember back to your computer science class each variable type can hold up to a certain value length. You will also remember some variable types only hold positive numbers while others hold positive and negative numbers.If you go outside of the constraints of the number type you are using it may handle things in different ways such as an error condition or perhaps cutting the number off at the maximum or minimum value.
In the Solidity language for Ethereum when we reach values past what our variable can hold it in turn wraps back around to a number it understands. So for example if we have a variable that can only hold a 2 digit number when we hit 99 and go past it, we will end up with 00. Inversely if we had 00 and we subtracted 1 we would end up with 99.
Normally in your math class the following would be true:
99 + 1 = 100
00 - 1 = -1
In solidity with unsigned numbers the following is true:
99 + 1 = 00
00 - 1 = 99
So the issue lies with the assumption that a number will fail or provide a correct value in mathematical calculations when indeed it does not. So comparing a variable with a require statement is not sufficiently accurate after performing a mathematical operation that does not check for safe values.
That comparison may very well be comparing the output of an over/under flowed value and be completely meaningless. The Require statement may return true, but not based on the actual intended mathematical value. This in turn will lead to an action performed which is beneficial to the attacker for example checking a low value required for a funds validation but then receiving a very high value sent to the attacker after the initial check. Lets go through a few examples.
Simple Example:
Lets say we have the following Require check as an example:require(balance - withdraw_amount > 0) ;
Now the above statement seems reasonable, if the users balance minus the withdrawal amount is less than 0 then obviously they don't have the money for this transaction correct?
This transaction should fail and produce an error because not enough funds are held within the account for the transaction. But what if we have 5 dollars and we withdraw 6 dollars using the scenario above where we can hold 2 digits with an unsigned integer?
Let's do some math.
5 - 6 = 99
Last I checked 99 is greater than 0 which poses an interesting problem. Our check says we are good to go, but our account balance isn't large enough to cover the transaction. The check will pass because the underflow creates the wrong value which is greater than 0 and more funds then the user has will be transferred out of the account.
Because the following math returns true:
require(99 > 0)
Withdraw Function Vulnerable to an UnderFlow:
The below example snippet of code illustrates a withdraw function with an underflow vulnerability:function withdraw(uint _amount){
require(balances[msg.sender] - _amount > 0);
msg.sender.transfer(_amount);
balances[msg.sender] -= _amount;
}
In this example the require line checks that the balance is greater then 0 after subtracting the _amount but if the _amount is greater than the balance it will underflow to a value above 0 even though it should fail with a negative number as its true value.
require(balances[msg.sender] - _amount > 0);
It will then send the value of the _amount variable to the recipient without any further checks:
msg.sender.transfer(_amount);
Followed by possibly increasing the value of the senders account with an underflow condition even though it should have been reduced:
balances[msg.sender] -= _amount;
Depending how the Require check and transfer functions are coded the attacker may not lose any funds at all but be able to transfer out large sums of money to other accounts under his control simply by underflowing the require statements which checks the account balance before transferring funds each time.
Transfer Function Vulnerable to a Batch Overflow:
Overflow conditions often happen in situations where you are sending a batched amount of values to recipients. If you are doing an airdrop and have 200 users who are each receiving a large sum of tokens but you check the total sum of all users tokens against the total funds it may trigger an overflow. The logic would compare a smaller value to the total tokens and think you have enough to cover the transaction for example if your integer can only hold 5 digits in length or 00,000 what would happen in the below scenario?You have 10,000 tokens in your account
You are sending 200 users 499 tokens each
Your total sent is 200*499 or 99,800
The above scenario would fail as it should since we have 10,000 tokens and want to send a total of 99,800. But what if we send 500 tokens each? Lets do some more math and see how that changes the outcome.
You have 10,000 tokens in your account
You are sending 200 users 500 tokens each
Your total sent is 200*500 or 100,000
New total is actually 0
This new scenario produces a total that is actually 0 even though each users amount is 500 tokens which may cause issues if a require statement is not handled with safe functions which stop an overflow of a require statement.
Lets take our new numbers and plug them into the below code and see what happens:
1. uint total = _users.length * _tokens;
2. require(balances[msg.sender] >= total);
3. balances[msg.sender] = balances[msg.sender] -total;
4. for(uint i=0; i < users.length; i++){
5. balances[_users[i]] = balances[_users[i]] + _value;
Same statements substituting the variables for our scenarios values:
1. uint total = _200 * 500;
2. require(10,000 >= 0);
3. balances[msg.sender] = 10,000 - 0;
4. for(uint i=0; i < 500; i++){
5. balances[_recievers[i]] = balances[_recievers[i]] + 500;
Batch Overflow Code Explanation:
1: The total variable is 100,000 which becomes 0 due to the 5 digit limit overflow when a 6th digit is hit at 99,999 + 1 = 0. So total now becomes 0.2: This line checks if the users balance is high enough to cover the total value to be sent which in this case is 0 so 10,000 is more then enough to cover a 0 total and this check passes due to the overflow.
3: This line deducts the total from the senders balance which does nothing since the total of 10,000 - 0 is 10,000. The sender has lost no funds.
4-5: This loop iterates over the 200 users who each get 500 tokens and updates the balances of each user individually using the real value of 500 as this does not trigger an overflow condition. Thus sending out 100,000 tokens without reducing the senders balance or triggering an error due to lack of funds. Essentially creating tokens out of thin air.
In this scenario the user retained all of their tokens but was able to distribute 100k tokens across 200 users regardless if they had the proper funds to do so.
Lab Follow Along Time:
We went through what might have been an overwhelming amount of concepts in this chapter regarding over/underflow scenarios now lets do an example lab in the video below to illustrate this point and get a little hands on experience reviewing, writing and exploiting smart contracts. Also note in the blockchain youtube playlist we cover the same concepts from above if you need to hear them rather then read them.For this lab we will use the Remix browser environment with the current solidity version as of this writing 0.5.12. You can easily adjust the compiler version on Remix to this version as versions update and change frequently.
https://remix.ethereum.org/
Below is a video going through coding your own vulnerable smart contract, the video following that goes through exploiting the code you create and the videos prior to that cover the concepts we covered above:
Download Video Lab Example Code:
Download Sample Code:contract Underflow{
mapping (address =>uint) balances;
function contribute() public payable{
balances[msg.sender] = msg.value;
}
function getBalance() view public returns (uint){
return balances[msg.sender];
}
function transfer(address _reciever, uint _value) public payable{
require(balances[msg.sender] - _value >= 5);
balances[msg.sender] = balances[msg.sender] - _value;
balances[_reciever] = balances[_reciever] + _value;
}
This next video walks through exploiting the code above, preferably hand coded by you into the remix environment. As the best way to learn is to code it yourself and understand each piece:
Conclusion:
We covered a lot of information at this point and the video series playlist associated with this blog series has additional information and walk throughs. Also other videos as always will be added to this playlist including fixing integer overflows in the code and attacking an actual live Decentralized Blockchain Application. So check out those videos as they are dropped and the current ones, sit back and watch and re-enforce the concepts you learned in this blog and in the previous lab. This is an example from a full set of labs as part of a more comprehensive exploitation course we have been working on.- What Are Hacking Tools
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