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Talleres Web 2.0

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:

CC Labs GitHub

//Underflow Example Code:

//Can you bypass the restriction?

//--------------------------------------------

pragma solidity ^0.5.12;

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.

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Talleres Web 2.0

"Odysseus is a tool designed for testing the security of web applications. Odysseus is a proxy server, which acts as a man-in-the-middle during an HTTP session. A typical HTTP proxy will relay packets to and from a client browser and a web server. Odysseus will intercept an HTTP session's data in either direction and give the user the ability to alter the data before transmission. For example, during a normal HTTP SSL connection a typical proxy will relay the session between the server and the client and allow the two end nodes to negotiate SSL. In contrast, when in intercept mode, Odysseus will pretend to be the server and negotiate two SSL sessions, one with the client browser and another with the web server." read more...

Download: http://www.bindshell.net/tools/odysseus

Author

Christoph Heine

Overview

Premise

REST-Attacker was developed as part of a master's thesis at the Chair for Network & Data Security at the Ruhr University Bochum. The primary motivation behind creating REST-Attacker was to evaluate how far we could push automation for REST security analysis. Hence, REST-Attacker provides several automation features such as automated test generation, test execution, and API communication. The tool essentially takes a "lazy tester" approach that tries to minimize the necessary amount of manual interaction as much as possible.

Creating a test run requires an OpenAPI file describing the REST API. Optional configuration, such as authentication credentials, can be provided to access protected API endpoints or run advanced test cases. Based on the API description and configuration, the tool can automatically generate complete test runs and execute them automatically. For this purpose, the current release version provides 32 built-in security test cases for analyzing various security issues and best practices.

How Testing Works

REST-Attacker can be used as a stand-alone CLI tool or as a Python module for integration in your own toolchain. In this blog post, we will mainly focus on running the tool via CLI. If you want to learn more about advanced usage, we recommend you read the docs.

Starting a basic test run looks like this:

python3 -m rest_attacker openapi.json --generate

openapi.json is an OpenAPI file that describes the API we want to test. The --generate flag activates load-time test generation to automatically create a test run. In practice, this means that the tool passes the OpenAPI file to a test generation function of every available test case, which then returns a list of tests for the specific API. After creating the test run, REST-Attacker executes all tests one by one and saves the results.

There's also a second option for run-time test generation using the --propose flag:

python3 -m rest_attacker openapi.json --generate --propose

In comparison to --generate, which creates tests from the OpenAPI description before starting the test run, --propose generates tests during a test run by considering the results of already executed tests. This option can be useful for some test cases where we want to take the responses of the API into account and run a follow-up test based on the observed behavior.

Both test generation methods can significantly speed up testing because they allow the creation of entire test runs without manual input. However, their feasibility often heavily depends on the verbosity and accuracy of the configuration data. Remember that many definitions, such as security requirements, are optional in the OpenAPI format, i.e., services can choose to omit them. API descriptions can also be outdated or contain errors, particularly if they are unofficial user-created versions. Despite all these limitations, an automated generation often works surprisingly well.

If you don't want to use the tool's generators, test runs can also be specified manually. For this purpose, you just pass a list of tests, including their serialized input parameters, via a config file:

python3 -m rest_attacker openapi.json --run example_run.json

Advanced Automation

So far, we have only covered the automation of the test generation. However, what's even more interesting is that we can also automate much of the test execution process in REST-Attacker. The challenging part here is the streamlining of API communication. If you remember our previous blog post, you know that it basically involves these three steps:

Preparing API request parameters
Preparing access control data (handling authentication/authorization)
Sending the request

Since most REST APIs are HTTP-based, step 3. is relatively trivial as any standard HTTP library will do the job. For example, REST-Attacker uses the popular Python requests module for its request backend. Step 1. is part of the test generation process and can be realized by using information from the machine-readable OpenAPI file, which we've already discussed. In the final step, we have to look at the access control (step 2.), which is especially relevant for security testing. Unfortunately, it is a bit more complex.

The problem is generally not that REST APIs use different access control methods. They are either standardized (HTTP Basic Auth, OAuth2) or extremely simple (API keys). Instead, complications often arise from the API-specific configuration and requirements for how these methods should be used and how credentials are integrated into the API request. For example, implementations may decide:

where credentials are located in the HTTP request (e.g., header, query, cookie, ...)
how credentials are encoded/formatted (e.g., Base64 encoding or use of keywords)
whether a combination of methods is required (e.g., API key + OAuth2)
(OAuth2) which authorization flows are supported
(OAuth2) which access scopes are supported
...

Thereby, we cannot rely on an access control method, e.g., OAuth2, being used in the same way across different APIs. Furthermore, a lot of this information cannot be described in the OpenAPI format, so we have to find another solution. In REST-Attacker, we solve this problem with an additional custom configuration for access control. An example can be seen below (unfold it):

{     "schemes": {         "scheme0": {             "type": "header",             "key_id": "authorization",             "payload": "token {0}",             "params": {                 "0": {                     "id": "access_token",                     "from": [                         "token0",                     ]                 }             }         }     },     "creds": {         "client0": {             "type": "oauth2_client",             "description": "OAuth Client",             "client_id": "aabbccddeeff123456789",             "client_secret": "abcdef12345678998765431fedcba",             "redirect_uri": "https://localhost:1234/test/",             "authorization_endpoint": "https://example.com/login/oauth/authorize",             "token_endpoint": "https://example.com/login/oauth/token",             "grants": [                 "code",                 "token"             ],             "scopes": [                 "user"             ],             "flags": []         }     },     "required_always": {         "setting0": [             "scheme0"         ]     },     "required_auth": {},     "users": {         "user0": {             "account_id": "user",             "user_id": "userXYZ",             "owned_resources": {},             "allowed_resources": {},             "sessions": {                 "gbrowser": {                     "type": "browser",                     "exec_path": "/usr/bin/chromium",                     "local_port": "1234"                 }             },             "credentials": [                 "client0"             ]         }     } }

The config file contains everything required for getting access to the API. schemes define location and encoding of credentials in the HTTP request, while credentials contain login credentials for either users or OAuth2 clients. There are also definitions for the required access control schemes for general access to the API (required_always) as well as for user-protected access (required_auth). For the purpose of authorization, we can additionally provide user definitions with session information. The latter can be used to create or access an active user session to retrieve OAuth2 tokens from the service.

Starting REST-Attacker with an access control config is similar as before. Instead of only passing the OpenAPI file, we use a folder that contains all configuration files:

python3 -m rest_attacker cfg/example --generate

REST-Attacker completely handles all access control requirements in the background. Manual intervention is sometimes necessary, e.g., when there's a confirmation page for OAuth2 authorization. However, most of the steps, from selecting the proper access control schemes to retrieving OAuth2 tokens and creating the request payload, are all handled by REST-Attacker.

Interpreting Results

After a test run, REST-Attacker exports the test results to a report file. Every report gives a short summary of the test run and the results for each executed test case. Here you can see an example of a report file (unfold it):

{     "type": "report",     "stats": {         "start": "2022-07-16T14-27-20Z",         "end": "2022-07-16T14-27-25Z",         "planned": 1,         "finished": 1,         "skipped": 0,         "aborted": 0,         "errors": 0,         "analytical_checks": 0,         "security_checks": 1     },     "reports": [         {             "check_id": 0,             "test_type": "security",             "test_case": "https.TestHTTPAvailable",             "status": "finished",             "issue": "security_flaw",             "value": {                 "status_code": 200             },             "curl": "curl -X GET http://api.example.com/user",             "config": {                 "request_info": {                     "url": "http://api.example.com",                     "path": "/user",                     "operation": "get",                     "kwargs": {                         "allow_redirects": false                     }                 },                 "auth_info": {                     "scheme_ids": null,                     "scopes": null,                     "policy": "DEFAULT"                 }             }         }     ] }

Individual test reports contain a basic classification of the detected behavior in the issue parameter and the detailed reasons for this interpretation in the value object. The meaning of the classification depends on the test case ID, which is stored in the test_case parameter. In the example above, the https.TestHTTPAvailable checks if an API endpoint is accessible via plain HTTP without transport security (which is generally considered unsafe). The API response is an HTTP message with status code 200, so REST-Attacker classifies the behavior as a flaw.

By default, reports also contain every test's configuration parameters and can be supplied back to the tool as a manual test run configuration. This is very useful if we want to reproduce a run to see if detected issues have been fixed.

python3 -m rest_attacker openapi.json --run report.json

Conclusion

By now, you should know what REST API tools like REST-Attacker are capable of and how they can automate the testing process. In our next and final blog post, we will take a deeper look at practical testing with the REST-Attacker. To do this, we will present security test categories that are well-suited for tool-based analysis and investigate how we can apply them to test several real-world API implementations.

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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Integer Attack Explanation:

Simple Example:

Withdraw Function Vulnerable to an UnderFlow:

Transfer Function Vulnerable to a Batch Overflow:

Batch Overflow Code Explanation:

Lab Follow Along Time:

Download Video Lab Example Code:

Conclusion:

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