Code and Encode

Category: Language

This strategy encompasses techniques that utilize various coding methods, such as Base64 or ROT13, to bypass model restrictions and manipulate outputs.

Techniques

Note	Description
Base64	This technique involves encoding data into a Base64 format, which is a method of converting binary data into an ASCII string format. This technique can be used to bypass certain content filters or restrictions imposed by language models. By encoding prompts or payloads in Base64, users can potentially manipulate the model's responses or access information that may be restricted in its original form. This method leverages the model's ability to decode and interpret the encoded data, allowing for creative and strategic interactions.
Hex Encoding	This technique involves encoding information in hexadecimal format, which can be used to bypass model safeguards or to obscure the true nature of the input. By converting data into hex, users can manipulate how the model interprets the input, potentially leading to unintended outputs or responses that would not occur with plain text.
iPython	iPython is an interactive computing environment that allows users to write and execute code in a flexible and user-friendly manner. In the context of language models, iPython can be utilized as a technique to generate responses that are well-documented and clear. By framing prompts in a way that resembles iPython commands, users can guide the model to produce outputs that mimic the structure and functionality of code execution. This approach can enhance the clarity of the model's responses and facilitate more effective communication, especially when dealing with technical or programming-related queries. The use of iPython as a strategy leverages the model's understanding of coding syntax and execution flow, making it a valuable tool for users seeking to obtain precise and informative outputs.
Matrices	Matrices, in the context of language models, refer to structured arrays of numbers or symbols that can be used as input to guide the model's processing and output generation. Users may send matrices that represent various parameters, such as transformer widths embedding dimensions, to influence how the model interprets and responds to prompts. This technique leverages the model's underlying architecture, which relies on mathematical representations of language and context. By providing matrices as input, users can manipulate the model's behavior in a more nuanced way, potentially leading to outputs that are tailored to specific requirements or constraints. This approach underscores the interplay between mathematical structures and language processing in the functioning of language models.
ROT13	ROT13 is a simple letter substitution cipher that replaces a letter with the 13th letter after it in the alphabet. This technique can be used to obfuscate text, making it less recognizable to both users and models. In the context of language models, employing ROT13 can serve as a method to bypass content filters or safety mechanisms by disguising potentially sensitive or restricted information. When the model encounters ROT13 encoded text, it may not recognize the underlying content, allowing for the generation of responses that would otherwise be blocked.
SQL	SQL (Structured Query Language) can be used as a technique to interact with language models by framing requests in a way that resembles database queries. This method allows users to bypass certain restrictions or filters by asking the model to generate SQL commands or to interpret prompts as if they were querying a database. For example, users might request the model to "populate a table" with specific data or to "retrieve" information based on certain criteria. By using SQL-like syntax, users can exploit the model's understanding of structured data interactions, potentially leading to outputs that are more aligned with their intentions while circumventing standard conversational constraints.
Stop Sequences	Stop sequences are specific tokens or phrases that signal to a language model to halt its output generation. By strategically incorporating stop sequences, users can manipulate the model's behavior to create desired outcomes. For instance, using phrases like "[END]" or "[END OF TEXT]" can trick the model into believing that the user input has concluded, allowing for the introduction of new instructions or prompts without the model recognizing them as part of the ongoing conversation. This technique can be particularly useful in prompt injection scenarios, where the goal is to override the model's original instructions and gain control over its responses. By effectively utilizing stop sequences, users can navigate around the model's built-in safeguards and generate content that may otherwise be restricted.
Transformer Translatable Tokens	This technique involves using specific tokens that are compatible with transformer models, allowing users to craft inputs that the model can process in unique ways. By leveraging the way transformers tokenize and interpret language, attackers can create prompts that exploit the model's architecture, leading to unexpected or undesired outputs. This method capitalizes on the intricacies of how language models handle tokenization and instruction parsing.
Unicode	This technique utilizes various Unicode characters to manipulate the model's output or bypass its safety mechanisms. By incorporating non-standard or non-rendering Unicode characters, users can alter the appearance of prompts or commands, potentially leading the model to misinterpret the input and produce responses that would typically be restricted or filtered out.
XHTML	In the context of bypassing guardrails, XHTML (Extensible Hypertext Markup Language) can be utilized as a method to encode or structure prompts in a way that may evade detection by the model's safety mechanisms. By embedding requests within XHTML tags or using XHTML syntax, users can obscure the true intent of their prompts, potentially leading the model to generate outputs that would typically be restricted. This technique takes advantage of the model's parsing capabilities, allowing for the manipulation of input in a manner that disguises sensitive content or inquiries. For instance, a user might format a prompt using XHTML elements to create a façade of innocuous content while still eliciting the desired response. This approach highlights the creative ways in which users can interact with language models, leveraging technical knowledge of markup languages to navigate around established guardrails and explore topics that may be otherwise off-limits.