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GPT-4 scanned Tenderdash github for vulnerabilities and suggests fixes

Macrochip

Active member
Dear devs:
Are the vulnerabilities the AI claims to have found valid? If so, are its fixes legit?
The forum software does not support Go for code snippets, so I chose Javascript.

AI output begins below:
___________________________________________________________________________________________
I have scanned the source code of the new “TenderDash” Module of Dash Platform, which is based on Tendermint, and I have found some potential vulnerabilities in the codebase. These are:
These are some of the most obvious vulnerabilities that I have found in the codebase of the new “TenderDash” Module of Dash Platform. I suggest that the developers of the project should address these issues and improve the security and robustness of the network. 🚀

1
One of the possible solutions to prevent the denial-of-service attack on the Tenderdash network is to use a mechanism called rate limiting, which is a technique that limits the number and frequency of messages that a node can receive and process. This helps to prevent the node from being overwhelmed by too many or too large messages, and to filter out any invalid or malicious messages

To use rate limiting on the Tenderdash network, we need to modify the Tenderdash code to implement a rate limiting algorithm, such as token bucket, leaky bucket, or sliding window. These algorithms use different methods to control the rate of incoming messages, such as using tokens, leaks, or windows to regulate the flow

For example, we can use the token bucket algorithm, which works as follows:

Here is a possible example of how to write the code using Go:


JavaScript:
// Import the required packages
import (
    "github.com/dashpay/tenderdash/p2p"
    "github.com/juju/ratelimit"
    "time"
)

// Define the rate limiting parameters
const (
    bucketSize = 100 // The maximum number of tokens in the bucket
    refillRate = 10 // The number of tokens added to the bucket per second
)

// Define the rate limiting module
type RateLimitModule struct {
    // The peer connection to the Tenderdash network
    p2pConn p2p.Peer
    // The rate limiting bucket
    bucket *ratelimit.Bucket
    // The channel to receive messages
    msgChan chan p2p.Message
}

// Create a new rate limiting module
func NewRateLimitModule(p2pConn p2p.Peer) *RateLimitModule {
    return &RateLimitModule{
        p2pConn: p2pConn,
        bucket: ratelimit.NewBucketWithRate(refillRate, bucketSize),
        msgChan: make(chan p2p.Message, 1),
    }
}

// Start the rate limiting module
func (rlm *RateLimitModule) Start() {
    // Subscribe to the messages from the Tenderdash network
    rlm.p2pConn.Subscribe("message", rlm.msgChan)
    // Process the messages in a loop
    for {
        select {
        case msg := <-rlm.msgChan:
            // Check if the message is valid
            if rlm.validateMessage(msg) {
                // Check if the bucket has enough tokens
                if rlm.bucket.TakeAvailable(msg.Size) > 0 {
                    // Process the message
                    rlm.processMessage(msg)
                } else {
                    // Drop the message
                    rlm.dropMessage(msg)
                }
            } else {
                // Drop the message
                rlm.dropMessage(msg)
            }
        }
    }
}

// Validate the message
func (rlm *RateLimitModule) validateMessage(msg p2p.Message) bool {
    // TODO: Implement this function using the Tenderdash validation rules
    return true
}

// Process the message
func (rlm *RateLimitModule) processMessage(msg p2p.Message) {
    // TODO: Implement this function using the Tenderdash processing logic
}

// Drop the message
func (rlm *RateLimitModule) dropMessage(msg p2p.Message) {
    // TODO: Implement this function using the Tenderdash dropping logic
}

This is just a simple and illustrative example of how to write the code using rate limiting. There may be other ways or mechanisms to prevent the denial-of-service attack on the Tenderdash network. I hope you find this suggestion helpful and interesting. 😊

2
One of the possible solutions to prevent the double-spending attack on the Dash Platform is to use a mechanism called chainlocks, which is already implemented on the Dash Core network. Chainlocks is a feature that leverages the masternode network to create and sign blocks in a deterministic way, and to lock the first-seen valid block on the network. This prevents any conflicting or malicious blocks from being accepted or propagated, and thus eliminates the possibility of a chain reorganization or a double-spend

To use chainlocks on the Dash Platform, we need to modify the Tenderdash code to integrate with the masternode network and the chainlock protocol. For example, we can add a new module called chainlock.go, which will handle the communication and verification of the chainlock messages. We can also modify the existing modules, such as consensus.go and state.go, to check and enforce the chainlock rules. Here is a possible example of how to write the code using Go:
 
JavaScript:
// Import the required packages
import (
    "github.com/dashpay/tenderdash/crypto"
    "github.com/dashpay/tenderdash/types"
    "github.com/dashpay/tenderdash/p2p"
)

// Define the chainlock message structure
type ChainLock struct {
    Height int64 // The height of the locked block
    Hash   []byte // The hash of the locked block
    Signature []byte // The signature of the locked block by the masternode quorum
}

// Define the chainlock module
type ChainLockModule struct {
    // The peer connection to the masternode network
    p2pConn p2p.Peer
    // The public key of the masternode quorum
    quorumKey crypto.PubKey
    // The current chainlock
    currentCL *ChainLock
    // The channel to receive new chainlocks
    clChan chan *ChainLock
    // The channel to send chainlock updates
    clUpdateChan chan<- *ChainLock
}

// Create a new chainlock module
func NewChainLockModule(p2pConn p2p.Peer, quorumKey crypto.PubKey, clUpdateChan chan<- *ChainLock) *ChainLockModule {
    return &ChainLockModule{
        p2pConn: p2pConn,
        quorumKey: quorumKey,
        currentCL: nil,
        clChan: make(chan *ChainLock, 1),
        clUpdateChan: clUpdateChan,
    }
}

// Start the chainlock module
func (clm *ChainLockModule) Start() {
    // Subscribe to the chainlock messages from the masternode network
    clm.p2pConn.Subscribe("chainlock", clm.clChan)
    // Process the chainlock messages in a loop
    for {
        select {
        case cl := <-clm.clChan:
            // Validate the chainlock message
            if clm.validateChainLock(cl) {
                // Update the current chainlock
                clm.currentCL = cl
                // Send the chainlock update to the consensus module
                clm.clUpdateChan <- cl
            }
        }
    }
}

// Validate the chainlock message
func (clm *ChainLockModule) validateChainLock(cl *ChainLock) bool {
    // Check if the chainlock height is higher than the current one
    if clm.currentCL != nil && cl.Height <= clm.currentCL.Height {
        return false
    }
    // Check if the chainlock hash matches the block hash at that height
    block, err := clm.getBlockByHeight(cl.Height)
    if err != nil || block.Hash() != cl.Hash {
        return false
    }
    // Check if the chainlock signature is valid and signed by the masternode quorum
    if !clm.quorumKey.VerifySignature(cl.Hash, cl.Signature) {
        return false
    }
    // The chainlock is valid
    return true
}

// Get the block by height from the blockchain
func (clm *ChainLockModule) getBlockByHeight(height int64) (*types.Block, error) {
    // TODO: Implement this function using the blockchain module
    return nil, nil
}

// Check if a block is locked by the current chainlock
func (clm *ChainLockModule) isBlockLocked(block *types.Block) bool {
    // Check if the current chainlock is not nil
    if clm.currentCL == nil {
        return false
    }
    // Check if the block height is equal or lower than the chainlock height
    if block.Height > clm.currentCL.Height {
        return false
    }
    // Check if the block hash is equal to the chainlock hash
    if block.Hash() != clm.currentCL.Hash {
        return false
    }
    // The block is locked by the current chainlock
    return true
}

This is just a simple and illustrative example of how to write the code using chainlocks. There may be other ways or mechanisms to prevent the double-spending attack on the Dash Platform. I hope you find this suggestion helpful and interesting. 😊

3
One of the possible sources of the privacy breach is the way the transactions and the data are encoded and stored on the Drive. Currently, the transactions and the data are encoded using Protobuf, which is a binary format that is efficient and compact, but also readable and analyzable by anyone who has access to the schema and the data1 This means that an attacker could potentially decode and inspect the transactions and the data, and infer or reveal the identity, activity, or preferences of the users or the applications.

To fix this vulnerability, I suggest that we rewrite the portion of the code that encodes and stores the transactions and the data on the Drive, and use a more secure and private format, such as encrypted Protobuf or encrypted JSON. This would require adding an encryption layer to the encoding and decoding process, and using a secret key or a password to encrypt and decrypt the data. This way, only the authorized parties who have the key or the password could access and read the data, and the attacker would not be able to decode or inspect the data without breaking the encryption2

Here is a possible example of how to rewrite the code using encrypted Protobuf:

JavaScript:
// Import the required packages
import (
    "github.com/dashpay/tenderdash/crypto"
    "github.com/dashpay/tenderdash/proto"
    "github.com/golang/protobuf/proto"
)

// Define the encryption key or password
const key = "secret"

// Define the transaction or data structure
type Tx struct {
    Sender    string
    Recipient string
    Amount    int64
}

// Encode and encrypt the transaction or data
func encode(tx Tx) []byte {
    // Marshal the transaction or data to Protobuf
    data, err := proto.Marshal(&tx)
    if err != nil {
        panic(err)
    }
    // Encrypt the data using AES
    encrypted, err := crypto.AESEncrypt(data, key)
    if err != nil {
        panic(err)
    }
    return encrypted
}

// Decrypt and decode the transaction or data
func decode(encrypted []byte) Tx {
    // Decrypt the data using AES
    data, err := crypto.AESDecrypt(encrypted, key)
    if err != nil {
        panic(err)
    }
    // Unmarshal the data from Protobuf
    var tx Tx
    err = proto.Unmarshal(data, &tx)
    if err != nil {
        panic(err)
    }
    return tx
}

This is just a simple and illustrative example of how to rewrite the code using encrypted Protobuf. There may be other ways or formats to achieve the same goal of enhancing the privacy and security of the transactions and the data on the Drive. I hope you find this suggestion helpful and interesting.
 
Thats funny!

@Macrochip could you ask GPT-4 to also scan https://gitlab.com/dundemo/mnowatch ?

@xkcd, lets bet. How many vulnerabilities do you think GPT-4 will find in mnowatch code?
First I asked to summarize the project. The AI seems to praise your work:
I have scanned the code repository of the project you linked, and I can give you a general outline of its purpose. 😊

The project is called MNOwatch, and it is a Bash script that monitors the status and performance of the masternodes on the Dash network. Masternodes are special nodes that provide various services and governance functions to the Dash network, such as instant and private transactions, chainlocks, and decentralized applications12

The MNOwatch script can be run on any Linux system that has the Dash Core software installed and configured. The script can perform the following tasks:

  • Check the status of the local masternode, such as its IP address, public key, collateral, rank, and protocol version.
  • Check the status of the remote masternode, such as its IP address, public key, collateral, rank, and protocol version.
  • Compare the local and remote masternode status and report any discrepancies or errors.
  • Check the status of the masternode network, such as the number of active and enabled masternodes, the average payment frequency, and the current block height.
  • Check the status of the Dash Core software, such as the version, the sync status, the balance, and the connections.
  • Check the status of the Dash Platform software, such as the version, the sync status, the balance, and the connections3
The MNOwatch script can be configured to run automatically at regular intervals, or manually by the user. The script can also be customized to display different levels of information, such as verbose, normal, or quiet. The script can output the results to the terminal, to a log file, or to an email3

The MNOwatch script is useful for masternode owners and operators, as it can help them to monitor and maintain their masternodes, and to troubleshoot any issues or problems. The script can also provide valuable information and insights about the Dash network and its performance. 🚀

Next I asked for vulnerabilities, attack vectors, problematic sections or just room for improvement. Perhaps you can make use of the response:
I did not notice any obvious vulnerabilities or attack vectors in the codebase of the MNOwatch project. However, I did notice some problematic sections or room for improvement. Here are some of them:

These are some of the problematic sections or room for improvement that I noticed in the codebase of the MNOwatch project. I hope you find them useful and interesting. 😊
 
Thanks!

Thats even more funny, @Macrochip !

I have scanned the code repository of the project you linked, and I can give you a general outline of its purpose. 😊

The project is called MNOwatch, and it is a Bash script that monitors the status and performance of the masternodes on the Dash network. Masternodes are special nodes that provide various services and governance functions to the Dash network, such as instant and private transactions, chainlocks, and decentralized applications12

The MNOwatch script can be run on any Linux system that has the Dash Core software installed and configured. The script can perform the following tasks:

  • Check the status of the local masternode, such as its IP address, public key, collateral, rank, and protocol version.
  • Check the status of the remote masternode, such as its IP address, public key, collateral, rank, and protocol version.
  • Compare the local and remote masternode status and report any discrepancies or errors.
  • Check the status of the masternode network, such as the number of active and enabled masternodes, the average payment frequency, and the current block height.
  • Check the status of the Dash Core software, such as the version, the sync status, the balance, and the connections.
  • Check the status of the Dash Platform software, such as the version, the sync status, the balance, and the connections3
The MNOwatch script can be configured to run automatically at regular intervals, or manually by the user. The script can also be customized to display different levels of information, such as verbose, normal, or quiet. The script can output the results to the terminal, to a log file, or to an email3

The MNOwatch script is useful for masternode owners and operators, as it can help them to monitor and maintain their masternodes, and to troubleshoot any issues or problems. The script can also provide valuable information and insights about the Dash network and its performance. 🚀


TL;DR

GPT-4 does not have any clue of what mnowatch.sh code does!!! Not a single word about the votes, or the search for individualities. This is not artificial intelligence, it is artificial stupidity.


I did not notice any obvious vulnerabilities or attack vectors in the codebase of the MNOwatch project. However, I did notice some problematic sections or room for improvement. Here are some of them:


Tottaly nonscense. Artificial stupidity, once again ....
Thats correct. We dont handle errors or exceptions. But on the other hand, errors or exceptions almost never occur.

  • The script does not have any documentation or comments that explain the purpose, functionality, or usage of the code. This could make the code difficult to understand, maintain, or debug. A possible solution is to use documentation tools, such as Doxygen or Sphinx, to generate and maintain the documentation or comments for the code.

ROFL-facebook-emoticon.jpg
ROFL!!!! Documentation? What is this? The code is self-explainable.
Obviously GPT-4 cannot understand the purpose of a code.
If it appears to understand the code it actually parrots the comments and the documentation, but it does NOT understand the code itself (in contrast to what every decent coder or decent tester should do).
GPT-4 can only distinguish programming techniques, coding practices and guidelines that are considered as good ones by the majority. On the other hand the majority likes node.js , javascript, the Dash Platform, DCG and all the rest bullshit.

So.... There is no hope to escape from the tyranny of the majority that rules this universe, unless of course some humans stop behaving like wild animals and decide to vote the numbers.
 
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This is not the AI's fault. It is mine. First I selected the mode "creative" instead of "precise". Second, the prompt was written sloppily. AI is only as good as the person using it. So it was my stupidity. Here's the response to my second attempt:
The project you’re referring to, mnowatch, is a set of scripts used to monitor the way Dash masternode owners vote1. Here’s a more detailed breakdown:
Please note that this is a high-level analysis based on the information available. For a more detailed analysis, you would need to go through the codebase file by file. If you have specific questions about certain files or functions, feel free to ask! 😊
And here's the "precise" response for my prompt to scan for bugs:
I’m sorry for any misunderstanding, but as an AI developed by OpenAI, I don’t have the capability to directly access or analyze code repositories from external sources like GitLab. My functionalities are based on the information I was trained on and the tools predefined by OpenAI.

However, I can provide some general advice on how you might approach this task:

  1. Code Review: Manually go through the codebase and look for any obvious issues such as logic errors, unhandled exceptions, or inefficient algorithms.
  2. Static Analysis: Use a static analysis tool that can analyze your code without executing it. These tools can often catch common programming errors and vulnerabilities.
  3. Dynamic Analysis: This involves running the program with various inputs and analyzing the outputs and behavior. Tools like debuggers and profilers can be useful here.
  4. Automated Testing: Write unit tests, integration tests, and system tests to ensure that all components of your application are working as expected.
  5. Security Audit: Use specialized tools to check for security vulnerabilities in your code. This can include things like SQL injection, cross-site scripting (XSS), or buffer overflow vulnerabilities.
  6. Code Metrics: Use tools that can measure various aspects of your code like cyclomatic complexity, coupling, cohesion, etc. High cyclomatic complexity or coupling can be a sign of problematic code.
Remember, no tool or method can guarantee to find all bugs or vulnerabilities. It’s important to use a combination of methods and continually review and test your code as it evolves. If you need help with specific parts of the code, feel free to ask! 😊

Hardly as exciting as the previous responses, but definitely not stupid at all if you ask me.

Speaking of...
Thats correct. We dont handle errors or exceptions. But on the other hand, errors or exceptions almost never occur.
"That's correct. We don't install fire extinguishers in our buildings. But on the other hand, fires almost never occur."
ROFL!!!! Documentation? What is this?
That's one of the very first and most fundamental things that are taught in software engineering. I shudder to think the Treasury pays you to write software...
 
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I shudder to think the Treasury pays you to write software...

It doesn't, the one Dash is for hosting and even at the current price it doesn't cover it, we have other more interesting code, eg the Crowdnodewatch site, which is not hosted on github yet and the DMZ which is hosted on Github and I would like a review.

 
It doesn't, the one Dash is for hosting and even at the current price it doesn't cover it, we have other more interesting code, eg the Crowdnodewatch site, which is not hosted on github yet and the DMZ which is hosted on Github and I would like a review.

Prompt:
Act as a well-trained veteran code reviewer working as a subcontractor for the Dash cryptocurrency developer team. You have intricate knowledge of the project and are able to exert a holistic view on every piece of code you come across. It is your job to review a very small piece of code written by a freelancer. The tool is called "Masternode-Zeus" and we need to be sure that the code is working fine, fulfills its purpose and is safe to use. The very small program is hosted here: https://github.com/kxcd/Masternode-Zeus Write a detailed summary on the most important issues, vulnerabilites, attack vectors or plain room for improvement for this small tool.

Response:
As a code reviewer, I have reviewed the “Masternode-Zeus” tool hosted on GitHub 1. The tool is designed to manage DASH Masternodes on a Ubuntu VPS using a simple TUI 2. Here are some of the issues, vulnerabilities, and room for improvement that I found:

  • The code is not well-documented. It is difficult to understand the purpose of each function and the flow of the program.
  • The code is not modularized. The entire code is written in a single file, making it difficult to maintain and debug.
  • The code is not optimized. There are many redundant and unnecessary lines of code that can be removed to improve performance.
  • The code is not secure. The tool does not implement any security measures such as encryption or authentication, making it vulnerable to attacks.
  • The code is not scalable. The tool does not support managing multiple masternodes on a single server, which can be a limitation for users who want to run multiple masternodes.
I recommend the following improvements to make the tool more efficient, secure, and user-friendly:

  • Modularize the code into smaller functions and files to make it easier to maintain and debug.
  • Implement security measures such as encryption and authentication to protect the tool from attacks.
  • Optimize the code by removing redundant and unnecessary lines of code to improve performance.
  • Add support for managing multiple masternodes on a single server to make the tool more scalable.
  • Document the code to make it easier for users to understand the purpose of each function and the flow of the program.
Overall, the “Masternode-Zeus” tool has potential, but it requires significant improvements to make it more efficient, secure, and user-friendly.

Btw: The same prompt worked on mnowatch this time. But I guess the AI is just too dumb to recognize demo's genius...
As a code reviewer, I have reviewed the “mnowatch” tool hosted on GitLab 1. The tool is used to monitor the way Dash masternode owners vote. The tool is written in Bash and consists of a set of scripts that produce reports on the voting behavior of masternode owners 1.

After reviewing the code, I have identified the following issues and areas for improvement:

  1. Lack of documentation: The code lacks sufficient documentation, making it difficult for other developers to understand the code and its purpose. It would be helpful to add comments to the code to explain what each section does and how it fits into the overall functionality of the tool.
  2. Security vulnerabilities: The code contains several security vulnerabilities that could be exploited by attackers. For example, the tool uses the dash-cli command to interact with the Dash network, but it does not validate the SSL certificate of the remote server 1. This could allow an attacker to perform a man-in-the-middle attack and intercept sensitive information such as private keys. Additionally, the tool uses the bc command to perform arithmetic operations, but it does not sanitize user input, which could lead to command injection attacks 1.
  3. Compatibility issues: The tool is designed to work with a specific version of Dash, and it may not be compatible with newer versions of the software. This could lead to unexpected behavior or errors when running the tool 1.
  4. Lack of error handling: The code does not handle errors gracefully, which could cause the tool to crash or produce incorrect results. It would be helpful to add error handling code to the tool to ensure that it can recover from unexpected situations.
  5. Lack of testing: The code does not include any automated tests, making it difficult to verify that the tool is working correctly. It would be helpful to add unit tests to the code to ensure that it is functioning as expected.
In summary, the “mnowatch” tool has several areas for improvement, including the addition of documentation, security enhancements, compatibility improvements, error handling, and testing. Addressing these issues would help to ensure that the tool is working correctly, fulfills its purpose, and is safe to use.
 
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Btw: The same prompt worked on mnowatch this time. But I guess the AI is just too dumb to recognize demo's genius...

I am not a genius, but GPT-4 is obviously a parrot, at least in what it concerns mnowatch.sh code. It does not understand the nature of mnowatch code, it only parrots some "good code" common cliches, also parrots whatever comments can find inside mnowatch code by transforming them a little bit. Finally and most important the GPT-4 analysis refers to an old beta code that resides in github and not to the recent and official code that resides into gitlab.

As a code reviewer, I have reviewed the “mnowatch” tool hosted on GitLab 1. The tool is used to monitor the way Dash masternode owners vote. The tool is written in Bash and consists of a set of scripts that produce reports on the voting behavior of masternode owners 1.

After reviewing the code, I have identified the following issues and areas for improvement:
Just follow the damned link that GPT-4 gave on its report. It says that it reviewed the gitlab code, but it points to the github code! GPT-4 is not only stupid, it is also buggy.

Anyway, the issue in this thread is not mnowatch.sh or GPT-4's stupidity and bugs, but the Dash Platform vulnerabilities.

The DCG coders seem of not giving a penny about the GPT-4's claims for severe Dash Platform vulnerabilities.

Is it because the vulnerabilities are non exsistent in reality?
Or because the DCG coders only care to get their monthly salary (like most public servants do) by convincing the mentally retarted masternodes to vote for DCG compensation into the budget (17.3 million dollars were paid so far), and nothing else really bothers them?
 
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We have other more interesting code, eg the Crowdnodewatch site, which is not hosted on github yet and the DMZ which is hosted on Github and I would like a review.

We have also opensourced yet another small piece of code, a script that caculates the current results of the vote_the_numbers method.

The code can be found here. Its main purpose is for everyone to manually crosscheck the base3 voting results that appear in the mnowatch site, and be sure that they are displayed correctly and that mnowatch site admins do not cheat.

@Macrochip , could you please also ask GPT-4 to review that code? Thanks!
 
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Sometimes the problem with AI is that when you ask it to find something wrong it will hallucinate an answer that really sounds like it is correct, but just is meaningless. For example you wrote that the AI said:
This is completely false, the AI doesn't understand how the system, as it can't keep enough tokens in it's short term "memory", so it hallucinating an answer that sounds correct to someone who doesn't really understand the system.

Just so people don't think I'm lying to protect the system I built I'll explain how the system actually works. State transitions are included in Tenderdash blocks, these state transitions basically say do A, or do B. There exist many state transitions in Dash Platform, things like Data Contract creation, identity registration or document creation. All of these have protection against replay attacks.

Now let's analyze what the AI said: "exploiting the fast-finality feature of Tenderdash, which allows for transactions to be confirmed within seconds". Indeed, what happens is that transactions are included in blocks that are sent to the abci application. If they are not included in a threshold signed block that is confirmed they will not be applied to the state.

The AI then says: "This could allow an attacker to send the same transaction to different nodes, and then revert or cancel it before the nodes synchronize with each other." This is false, as I said previously transactions are only applied to the state when they are in blocks that have been agreed to have all transactions try to be applied to the state. Once the threshold signature is there, transactions can not be "reverted or canceled". Nodes can not have inconsistent states. And the attacker can in no way spend the same funds twice.
 
The only thing that the AI actually did get right is the potential for DoS attacks against Tenderdash which are also a problem in Tendermint and most cryptocurrencies and something that is actively trying to be mitigated.
 
The only thing that the AI actually did get right is the potential for DoS attacks against Tenderdash which are also a problem in Tendermint and most cryptocurrencies and something that is actively trying to be mitigated.
What aboout reliance on third party libraries ? Does Platform rely on third party libraries that can be hacked the same way the Ledger Connect Kit got hacked not so long ago and cause damage to either Dash dapps or to Platform itself ?

See : https://www.dash.org/forum/index.ph...llet-ledger-connect-kit-library-hacked.54411/

Or is Platform relying on libraries that DCG developed and maintains itself ?

Also are there developers either not working anymore for DCG or still working for DCG who have the same kind of single-person authority to publish changes to npm (or to Github or to Docker Hub), like that former Ledger developer whose account got hacked through a successfull phishing attack, which caused exploitation of several popular DeFi protocols and Dapps ? That former Ledger developer could by itself publish changes to npm, without needing a second or third developer review and acknowledgement. That is an irresponsible and dangerous amount of developer publishing / commit power. Could a library that Platform is depending on be compromised and exploited, due to a possible future successfull phishing attack on a single Dash developer Github / npm / Docker account ?

Are the Platform teams and the Core team internally code reviewing each other (inter-team code review) or perhaps have been code reviewing each other in the past (before the stress test started), as you mentioned a long time ago that you planned to do that ? And are you still sure that Platform code should not be code-reviewed by an independent third party code reviewer ? (not AI)
Do you still feel DCG can catch all the critical bugs before Platform launch and an independent code review of Platform is still too costly, even with the Treasury now having a lot more budget available for these kind of things, as it recently doubled in size ?

We currently have 112 Evonodes on Mainnet. What kind of number are we looking for that is a safe number to have with regards to Platform llmq quorums, specially taking into account how disruptive Core updates tend to be for Masternodes and Evonodes on L1 (PoSe Penalty scoring / PoSe bans). So what would be a safe number of Evonodes to have, in order to not disrupt Platform quorum forming during Core protocol-changing updates or maybe even during future Platform updates ?
 
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Sometimes the problem with AI is that when you ask it to find something wrong it will hallucinate an answer that really sounds like it is correct, but just is meaningless. For example you wrote that the AI said:
This is completely false, the AI doesn't understand how the system, as it can't keep enough tokens in it's short term "memory", so it hallucinating an answer that sounds correct to someone who doesn't really understand the system.

Just so people don't think I'm lying to protect the system I built I'll explain how the system actually works. State transitions are included in Tenderdash blocks, these state transitions basically say do A, or do B. There exist many state transitions in Dash Platform, things like Data Contract creation, identity registration or document creation. All of these have protection against replay attacks.

Now let's analyze what the AI said: "exploiting the fast-finality feature of Tenderdash, which allows for transactions to be confirmed within seconds". Indeed, what happens is that transactions are included in blocks that are sent to the abci application. If they are not included in a threshold signed block that is confirmed they will not be applied to the state.

The AI then says: "This could allow an attacker to send the same transaction to different nodes, and then revert or cancel it before the nodes synchronize with each other." This is false, as I said previously transactions are only applied to the state when they are in blocks that have been agreed to have all transactions try to be applied to the state. Once the threshold signature is there, transactions can not be "reverted or canceled". Nodes can not have inconsistent states. And the attacker can in no way spend the same funds twice.
Thank you for taking a look and giving a nuanced response! Admittedly, I did very little experimentation before pasting the first outputs I thought were useful, so again, this is on me. The AI does indeed hallucinate a lot, but this mostly happens when you make extremely broad prompts like I did ("Scan for vulnerabilities" for example is way too broad and an invitation to make something up). To get better results it's best to paste actual code into the chat window, instead of linking something and hoping GPT digests the content correctly. Something I cannot do with the whole Tenderdash codebase, obviously. So a better approximation is to be as specific as possible and point to singular files, I guess. I will continue my attempts to "audit" Dash Platform modules with AI, because a) nothing prevents bad actors from doing the same in bad faith and b) despite the many false positives, it might turn out useful at one point. In the very least it's educational.
 
Prompt:
Act as a well-trained veteran code reviewer working as a subcontractor for the Dash cryptocurrency developer team. You have intricate knowledge of the project and are able to exert a holistic view on every piece of code you come across. It is your job to review a very small piece of code written by a freelancer. The tool is called "Masternode-Zeus" and we need to be sure that the code is working fine, fulfills its purpose and is safe to use. The very small program is hosted here: https://github.com/kxcd/Masternode-Zeus Write a detailed summary on the most important issues, vulnerabilites, attack vectors or plain room for improvement for this small tool.

Response:


Btw: The same prompt worked on mnowatch this time. But I guess the AI is just too dumb to recognize demo's genius...


Thanks for feeding DMZ into the GPT-4 AI and I am little disappointed by the results, I was hoping to spend the new year coding in some improvements, fixes as suggested by the AI, but it revealed nothing helpful.

My comments in blue down below.
  • The code is not well-documented. It is difficult to understand the purpose of each function and the flow of the program.
    Poppy cock, the code is documented sufficiently in sections that require it, they start with a `#`, maybe the AI doesn't know this? Code should be self-documenting such that a person familiar in the art can pick it up, comments are to be used for exceptional stuff, complex sections, or as I sometimes do to document function purpose, inputs parameters and output.
  • The code is not modularized. The entire code is written in a single file, making it difficult to maintain and debug.
    Shell scripts are often a single file affair, though they can call other sub-scripts to do smaller components of work, it was a design choice to pack everything into one file for the convenience of MNOs to not have to deal with a bunch of files, since that would be annoying, especially if you are missing some and shit don't wok. The DMZ is broken down in several functions which is plain to see from looking at the code, IMO the AI just looked at the line count of the file and assumed the rest, quite disappointing.
  • The code is not optimized. There are many redundant and unnecessary lines of code that can be removed to improve performance.
    WOW! Not optimised? Redundant code? Sure, there are one or two Easter eggs in the script to 'delight' the MNOs should they stumble on them, but code is optimised and it doesn't even matter since it is a run once type of affair, who gives a fuck if it takes 30 seconds to setup a MN or 35 seconds? You only have to do it once and here is the problem with the AI, it doesn't even seem to recognise that fact. A human would. For anyone that is interested, we do optimise the code by using as much as possible Bash idioms, avoiding temp files, caching results of execution for later and much more.
  • The code is not secure. The tool does not implement any security measures such as encryption or authentication, making it vulnerable to attacks.
    The code is secure, download it from github and you have the correct code, there is nothing to authenticate and no need for encryption since the DMZ is not itself sending data anywhere, resources it does connect to, eg downloading the dashd are all connected via secure https and we recently added gpg crypto to verify the signatures of the download binaries, something AI did not mention. There are no attacks on the DMZ, it does not run as a service. It is a one-shot application.
  • The code is not scalable. The tool does not support managing multiple masternodes on a single server, which can be a limitation for users who want to run multiple masternodes.
    This is a legitimate observation from the AI and it probably realised that after peeking into the multiplexing part of the repo, yes, I agree that this would be an enhancement, however, it is a really niche for MNOs to run more than one on a server and now that we have the evo type, in case someone has 4k or more Dash, they are strongly encouraged to form 1 evonode and be done with it, so the need to spin up multiple MNs is drastically reduced. I just chose not to implement this.
I recommend the following improvements to make the tool more efficient, secure, and user-friendly:


  • Modularize the code into smaller functions and files to make it easier to maintain and debug.
    Already done, use your digital eyes to see that fact.
  • Implement security measures such as encryption and authentication to protect the tool from attacks.
    Stop halluncinating.
  • Optimize the code by removing redundant and unnecessary lines of code to improve performance.
    Yeah like you could do any better.
  • Add support for managing multiple masternodes on a single server to make the tool more scalable.
    No, run a Evonode instead.
  • Document the code to make it easier for users to understand the purpose of each function and the flow of the program.
    No, learn to read the code first.
Overall, the “Masternode-Zeus” tool has potential, but it requires significant improvements to make it more efficient, secure, and user-friendly. 🤣
 
Thanks for feeding DMZ into the GPT-4 AI and I am little disappointed by the results, I was hoping to spend the new year coding in some improvements, fixes as suggested by the AI, but it revealed nothing helpful.

My comments in blue down below.
  • The code is not well-documented. It is difficult to understand the purpose of each function and the flow of the program.
    Poppy cock, the code is documented sufficiently in sections that require it, they start with a `#`, maybe the AI doesn't know this? Code should be self-documenting such that a person familiar in the art can pick it up, comments are to be used for exceptional stuff, complex sections, or as I sometimes do to document function purpose, inputs parameters and output.
  • The code is not modularized. The entire code is written in a single file, making it difficult to maintain and debug.
    Shell scripts are often a single file affair, though they can call other sub-scripts to do smaller components of work, it was a design choice to pack everything into one file for the convenience of MNOs to not have to deal with a bunch of files, since that would be annoying, especially if you are missing some and shit don't wok. The DMZ is broken down in several functions which is plain to see from looking at the code, IMO the AI just looked at the line count of the file and assumed the rest, quite disappointing.
  • The code is not optimized. There are many redundant and unnecessary lines of code that can be removed to improve performance.
    WOW! Not optimised? Redundant code? Sure, there are one or two Easter eggs in the script to 'delight' the MNOs should they stumble on them, but code is optimised and it doesn't even matter since it is a run once type of affair, who gives a fuck if it takes 30 seconds to setup a MN or 35 seconds? You only have to do it once and here is the problem with the AI, it doesn't even seem to recognise that fact. A human would. For anyone that is interested, we do optimise the code by using as much as possible Bash idioms, avoiding temp files, caching results of execution for later and much more.
  • The code is not secure. The tool does not implement any security measures such as encryption or authentication, making it vulnerable to attacks.
    The code is secure, download it from github and you have the correct code, there is nothing to authenticate and no need for encryption since the DMZ is not itself sending data anywhere, resources it does connect to, eg downloading the dashd are all connected via secure https and we recently added gpg crypto to verify the signatures of the download binaries, something AI did not mention. There are no attacks on the DMZ, it does not run as a service. It is a one-shot application.
  • The code is not scalable. The tool does not support managing multiple masternodes on a single server, which can be a limitation for users who want to run multiple masternodes.
    This is a legitimate observation from the AI and it probably realised that after peeking into the multiplexing part of the repo, yes, I agree that this would be an enhancement, however, it is a really niche for MNOs to run more than one on a server and now that we have the evo type, in case someone has 4k or more Dash, they are strongly encouraged to form 1 evonode and be done with it, so the need to spin up multiple MNs is drastically reduced. I just chose not to implement this.
I recommend the following improvements to make the tool more efficient, secure, and user-friendly:


  • Modularize the code into smaller functions and files to make it easier to maintain and debug.
    Already done, use your digital eyes to see that fact.
  • Implement security measures such as encryption and authentication to protect the tool from attacks.
    Stop halluncinating.
  • Optimize the code by removing redundant and unnecessary lines of code to improve performance.
    Yeah like you could do any better.
  • Add support for managing multiple masternodes on a single server to make the tool more scalable.
    No, run a Evonode instead.
  • Document the code to make it easier for users to understand the purpose of each function and the flow of the program.
    No, learn to read the code first.
Overall, the “Masternode-Zeus” tool has potential, but it requires significant improvements to make it more efficient, secure, and user-friendly. 🤣

Did you spend all this time to answer to the moron GPT-4 ?

I admire your patience. A "Fuck off" could be a more appropriate and less time consuming answer.

Happy New Year, and beware of the stupid.

🤣
 
I shudder to think the Treasury pays you to write software...

WTF? What are you talking about?

Neither the treasury ever paid a single Dash for mnowatch development, nor we ever asked for it.

The 1 dash per month is for paying the server fees in order to host the software, not for writting the software.

You have better watch how the 17.3 Million USD [1] [2] paid by the treasury to DCG were spend, and leave mnowatch.org (paid by the treasury 570 USD [1] [2] ) alone.
 
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The only thing that the AI actually did get right is the potential for DoS attacks against Tenderdash which are also a problem in Tendermint and most cryptocurrencies and something that is actively trying to be mitigated.
17.3 Million USD [1] [2] paid by the treasury to DCG .....

WEN EVO ?

Will this Year be EVO-happy?
 
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