Introduction

Social engineering has evolved significantly since its inception in the early 2000s. The art of manipulating individuals into divulging sensitive information or performing certain actions without their knowledge is now more sophisticated than ever before. With the advent of artificial intelligence (AI) and machine learning, modern web applications have implemented character AI filters to prevent social engineers from exploiting vulnerabilities.

However, these advanced security measures are not foolproof. In this blog post, we will explore the art of evading character AI filters in modern web applications using a combination of psychological manipulation and technical exploitation.

Understanding Character AI Filters

Character AI filters are designed to detect and prevent automated attacks on web applications. These filters analyze user input for patterns that resemble machine-generated text, such as random characters or repetitive sequences. When a pattern is detected, the filter may block the user’s request or flag it for further analysis.

There are several types of character AI filters used in modern web applications:

  • Word-based filters: These filters check for common words and phrases that are often used in automated attacks.
  • Character-based filters: These filters analyze the frequency and distribution of characters in user input.
  • Contextual filters: These filters consider the context in which user input is provided, such as the surrounding text or the user’s previous interactions.

Evading Word-Based Filters

Word-based filters can be evaded by using uncommon words and phrases that are not typically found in automated attacks. One approach is to use misspelled words or words with typos, which are less likely to be detected by word-based filters.

For example, if a web application uses the word “password” as a trigger for its filter, you could replace it with “pasword” or “passw0rd”. This would make it more difficult for the filter to detect the input as automated.

Another approach is to use phrases that are less common in automated attacks. For example, if a web application uses the phrase “login form”, you could replace it with “access panel”.

Evading Character-Based Filters

Character-based filters can be evaded by using uncommon characters or character combinations. One approach is to use special characters such as punctuation marks or symbols.

For example, if a web application uses the combination “abc123” as a trigger for its filter, you could replace it with “abc!@#”. This would make it more difficult for the filter to detect the input as automated.

Another approach is to use uncommon character combinations that are less likely to be detected by character-based filters. For example, if a web application uses the combination “123456”, you could replace it with “abcdef”.

Evading Contextual Filters

Contextual filters can be evaded by providing context that is not typical in automated attacks. One approach is to use phrases or sentences that are less common in automated attacks.

For example, if a web application uses the phrase “Please login to access your account” as a trigger for its filter, you could replace it with “Hello, I’d like to inquire about my order”.

Another approach is to use context that is not typically found in automated attacks. For example, if a web application uses the user’s previous interactions to determine whether they are a human or a bot, you could provide false information about your previous interactions.

Practical Examples

Here are some practical examples of how to evade character AI filters:

  • Word-based filter: Use misspelled words or uncommon phrases. For example, if a web application uses the word “password” as a trigger for its filter, you could replace it with “pasword”.
  • Character-based filter: Use special characters or uncommon character combinations. For example, if a web application uses the combination “abc123” as a trigger for its filter, you could replace it with “abc!@#”.
  • Contextual filter: Provide context that is not typical in automated attacks. For example, if a web application uses the phrase “Please login to access your account” as a trigger for its filter, you could replace it with “Hello, I’d like to inquire about my order”.

Conclusion

In conclusion, evading character AI filters in modern web applications requires a combination of psychological manipulation and technical exploitation. By using uncommon words and phrases, special characters or uncommon character combinations, and providing context that is not typical in automated attacks, you can increase your chances of success.

However, it’s important to note that these methods are not foolproof and may be detected by the web application’s security measures. Therefore, it’s essential to use them responsibly and only for legitimate purposes.