We hear about the need for better visibility in the cybersecurity space – detecting threats earlier and more accurately. We often hear about the dwell time and the time to identify and contain a data breach. Many of us are familiar with IBM's Cost of a Data Breach Report that has been tracking this statistic for years. In the 2021 report, IBM found that, on average, it takes an average of 212 days to identify a breach and then another 75 days to contain the breach, for a total of 287 days.
A new solution overview document provides insights on how XDR provider Cynet tackles the difficult problem of greatly improving threat visibility. Cynet takes a modern approach that includes a greater level of native technology integration and advanced automation purposely designed for organizations with smaller security teams than Fortune 500 organizations. A live webinar will discuss the same topic (Register here)
Cynet's Keys for Threat Visibility
Einstein said that the definition of insanity is doing the same thing over and over while expecting a different outcome. The old approach to threat visibility involving multiple protection technologies and trying to sift through a sea of alerts and information is obviously not working well. Cynet's different – and seemingly saner – approach to prevent, detect, and respond to modern-day threats involves several integrated capabilities.
According to the new Cynet solution overview, the following key technologies are used to provide extended threat visibility along with enhanced response capabilities.
Include Multiple Threat Detection Technologies
Cynet includes multiple prevention and detection technologies, all natively orchestrated in the platform:
NGAV – Fundamental endpoint protection based on known bad signatures and behaviors.
EDR – To detect and prevent more complex endpoint threats that bypass NGAV solutions.
NTA – To detect threats that have made their way into the network and so-called lateral movement.
UBA – To detect unusual activity that could signal stolen credentials, a rogue insider, or bots.
Deception – To uncover intrusions that have bypassed other detection technologies
CLM – To mine the extensive log data generated by IT systems.
SSPM – To find and correct configuration errors in SaaS applications.
Coordinate All Signals
Making sense out of multiple detection technologies by integrating, coordinating, and prioritizing information was supposed to be the realm of Security Incident and Event Management (SIEM) technologies. Unfortunately, SIEM doesn't do well with real-time data and requires significant care and feeding.
XDR solutions, like Cynet's, are purpose-built to integrate real-time signals from multiple points of telemetry on a single platform. Cynet even includes an Incident Engine that automatically investigates threats to determine the attack's full scope and root cause.
Automate All Response Actions
Quickly and accurately identifying threats is a game-changer. The ability to automatically and fully eradicate identified threats is, well, a game-changer changer. This means security teams won't have to be burdened with lengthy investigations, which many don't have the time or skills to undertake. Cynet provides an extensive set of remediation actions across files, hosts, networks and users as well as remediation playbooks that can be configured to be invoked manually or automatically.
Provide Full MDR Oversight
Beyond the technology platform, Cynet offers all clients a full, 24x7 MDR service at no additional cost. This team continuously monitors client environments to ensure nothing dangerous is overlooked or mishandled. Having an expert team watching out for issues should put smaller organizations with smaller security teams at ease, knowing an expert team of cybersecurity experts has their backs.
In With the New
With the time required to identify and contain data breaches steadily increasing, we need to rethink the traditional cybersecurity approach. It seems companies keep throwing more money, more technology, and more bodies at the problem, yet achieving the same (or worse) results. Cynet is one company that seems to be approaching the problem differently by combining multiple prevention, detection, response, and automation capabilities on a single, unified breach protection platform. Rather than buying all this stuff separately and munging it all together, the Cynet platform seems to expand and improve threat visibility out of the box.
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:
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: 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:
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.
uTorrent, one of the most popular BitTorrent clients, is now available for Android smartphones and tablets. Its use on mobile devices is very similar to its use in the PC. All you need is to search for torrents using the web browser on your mobile device, then uTorrent will download the files.
