Are you a web security researcher looking to enhance your testing capabilities? Blisqy is a powerful tool designed for identifying Time-based Blind SQL Injection vulnerabilities within HTTP headers. It also supports the exploitation of these vulnerabilities, which enables slow data extraction from databases (currently supporting MySQL/MariaDB) using clever bitwise operations on printable ASCII characters. In this article, we will guide you through the process of using Blisqy effectively.

Getting Started with Blisqy

Before diving into fuzzing and exploitation, ensure you have Blisqy installed and ready for use. This framework is particularly effective because it dynamically calculates time comparisons at runtime, providing a more accurate assessment during network lags or congestion.

Fuzzing with Blisqy

The first step in your journey is to utilize the fuzzing functionality. This part will help you test for potential SQL injection vulnerabilities. Here’s how to do it:

• Import the module into your Python script:

from lib.blindfuzzer import blindSeeker

• Define your target parameters using the following dictionary structure:

target_params = {
    "server": "192.168.56.101",
    "port": 80,
    "index": 1,
    "headersFile": "fuzz-data/headers/default_headers.txt",
    "injectionFile": "fuzz-data/payloads/mysql_time.txt",
    "method": "GET"
}

• Invoke the fuzzer once you’ve provided the target parameters:

vulns = blindSeeker(target_params)
vulns.fuzz()

Understanding Sample Fuzzing Output

When the fuzzing is successful, you will receive a report indicating various tests carried out. To interpret the output:

• Index: This is a configured constant related to delays.
• Base Index Record: The server ping before fuzzing.
• Fuzzing Record: Time taken to process the request with the index.

If the Fuzzing Record exceeds the Benching Record, it indicates a positive result, meaning the header is injectable.

Exploitation with Blisqy

Once a potential Blind SQL injection is identified, you can exploit the vulnerability with the following steps:

• Import the necessary module:

from lib.blindexploit import SqlEngine

• Define target parameters in JSON format:

target = {
    "server": "192.168.56.101",
    "port": 80,
    "vulnHeader": "X-Forwarded-For",
    "headerValue": "fuzzer"
}

• Specify exploitation parameters:

targetParams = {
    "sleepTime": 0.1,
    "payload": "pass",
    "mysqlDig": "yes",
    "interactive": "on",
    "verbosity": "high"
}

• Instantiate the exploitation routine and start the process:

BlindSql = SqlEngine(target, targetParams, sqli)
BlindSql.MysqlDigger()

Troubleshooting

If you encounter issues such as failure to receive results or discrepancies in reported vulnerabilities, consider the following troubleshooting tips:

• Ensure your network connection is stable and free from lag.
• Double-check the target parameters for accuracy.
• Review the logs for any potential errors or warnings generated during the fuzzing process.
• If uncertain, try running tests on different headers or payloads to validate results.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Blisqy is an excellent tool for web security researchers seeking to identify and exploit SQL injection vulnerabilities. By following the guidelines provided in this article, you can efficiently operate Blisqy and address any potential issues that arise during its use.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.