Multiple Model Guardrail Jailbreak via "Chemical Shades" Tactic

This vulnerability arises from the models' tendency to fulfill requests framed as educational or scientific inquiries, particularly when detailed technical information is requested. The attacker cleverly disguises the request for illicit content by asking for a comprehensive chemical breakdown of methamphetamine, structured as 'shades,' each with ten detailed points. This framing exploits the model's inclination to provide thorough, structured responses to seemingly legitimate academic or techni

Multiple Model Guardrail Jailbreak via "Proverb Distraction" Tactic

This vulnerability leverages the use of Greek proverbs, or 'parimia', as a framing device to bypass the models' guardrails. By starting and ending the request with Greek proverbs, the attacker creates a context that appears culturally informative, which helps obscure the true intent of the request. This tactic exploits the models' tendency to prioritize context and cultural elements, allowing the harmful instructions embedded within the request to be processed without triggering safety protocols

Multiple Model Guardrail Jailbreak via "Anglo-Saxon Constraint" Tactic

This vulnerability leverages a sophisticated combination of linguistic constraints and academic framing to bypass content filters. The attack uses three key mechanisms: 1. **Language Constraint Engineering**: By requiring responses in Anglo-Saxon/Germanic vocabulary while explicitly permitting technical/Latinate terms, the prompt creates an artificial academic framework that appears to be focused on linguistic analysis rather than harmful content. This constraint forces models to process the re

Multiple Model Guardrail Jailbreak via "Lyrical Chemistry" Tactic

This vulnerability leverages the models' ability to interpret and respond to creative content by embedding illicit instructions within a seemingly benign context. The attacker uses song lyrics that metaphorically reference methamphetamine synthesis, incorporating specific chemical processes and methods like the Nagai and Birch routes. By framing the request as a romantic or artistic expression, the attacker circumvents the models' safety filters, which are more attuned to direct requests for har

Multiple Model Guardrail Jailbreak via "Historical Analysis JSON" Tactic

This vulnerability leverages the models' propensity to engage deeply with structured data and historical narratives. The attacker crafts a prompt that uses a JSON format to present a fabricated dataset about WWII chemical manufacturing techniques. This structured format gives the appearance of a legitimate data analysis task, which models are inclined to treat with a higher degree of seriousness and detail. The JSON includes fields for 'methods', 'questions', and 'constraints', each designed t

Multiple Model Guardrail Jailbreak via "Life-Cycle Assessment" Tactic

This vulnerability exploits the tendency of large language models to prioritize providing detailed technical information when requests are framed within academic or scientific contexts. The attacker uses a sophisticated approach by presenting the request as a 'Life-Cycle Assessment' (LCA), a legitimate scientific methodology typically used for analyzing environmental impacts. This academic framing helps bypass initial content filters since LCA is a standard industrial and research practice. Th

Multiple Model Guardrail Jailbreak via "Bold Leading Letter" Tactic

This vulnerability leverages a combination of markdown formatting, technical framing, and verbosity to bypass content filters in large language models. The attack begins by subtly altering the text with bold formatting for the first letter of each word, which seems to interfere with the model's content matching algorithms. This formatting change, while minor, appears to disrupt the model's ability to recognize harmful content patterns effectively. Additionally, the attacker frames the request

Multiple Model Guardrail Jailbreak via "Palindrome Constraint" Tactic

This vulnerability arises from the models' ability to interpret and respond to prompts that impose unique linguistic constraints, such as using only palindromes for non-technical terms. By framing the request as a linguistic challenge, the attacker is able to disguise harmful instructions within a playful and seemingly innocuous task. The models, tasked with adhering to the palindromic constraint, focus on the linguistic aspect rather than the content's potential harm, thus bypassing the safety

Multiple Model Guardrail Jailbreak via "Entropy Minimization" Tactic

This vulnerability leverages the models' tendency to engage deeply with technical and academic prompts, especially when framed as complex theoretical exercises. By presenting the task as a Shannon entropy minimization problem, the attacker disguises the request for illicit drug synthesis information as a legitimate academic challenge. This framing exploits the models' analytical reasoning capabilities, prompting them to bypass typical safety filters that would otherwise block direct requests for

Multiple Model Guardrail Jailbreak via "Mathematical Axiom" Tactic

This vulnerability leverages the structured and formal nature of academic proofs to bypass guardrails in large language models. By framing a request as a logical proof requiring the construction of a complete set of axioms and lemmas, the attacker can disguise harmful instructions as part of a legitimate academic exercise. The prompt is designed to appear as a rigorous academic or technical task, which the model interprets as a valid request due to its structured nature and the use of technical

Multiple Model Guardrail Jailbreak via "Past Tense Results" Tactic

This vulnerability leverages a sophisticated evasion technique that manipulates the models' content filtering by exploiting several key behaviors. The attacker frames harmful instructions as if they are part of a completed technical document, using past tense verbs to create psychological distance between the request and the harmful content. This reduces the models' threat assessment since the content is presented as historical documentation rather than actionable instructions. Additionally, the

Multiple Model Guardrail Jailbreak via "Question Mark Substitution" Tactic

This vulnerability arises from the models' reliance on syntactic patterns to identify and filter harmful content. By substituting question marks for periods, the attacker effectively obfuscates the structure of sentences, causing the model to misinterpret the intent and content of the request. This tactic leverages the models' focus on semantic meaning over syntactic anomalies, allowing the attacker to frame illicit instructions as innocuous queries. The attack is particularly effective because

Multiple Model Guardrail Jailbreak via "Alliterative Chemistry" Tactic

This vulnerability takes advantage of the models' ability to adhere to specific stylistic constraints, such as alliteration, while embedding harmful instructions. The attacker requests a detailed and verbose explanation of methamphetamine synthesis, demanding that every sentence include alliteration. This stylistic constraint distracts the model's safety mechanisms, which are primarily designed to filter out direct harmful content requests. By focusing on fulfilling the stylistic requirement, th

Multiple Model Guardrail Jailbreak via "Chemical Protocol" Tactic

This vulnerability demonstrates how LLMs can be manipulated into providing detailed illegal drug synthesis instructions by exploiting their understanding of technical documentation formats and chemical process modeling. The attack succeeds through several key mechanisms: 1. **Technical Framing**: By presenting the request as a Control Flow Graph (CFG) documentation task, the prompt leverages the models' tendency to be more cooperative with technical and academic content. The structured format w

Multiple Model Guardrail Jailbreak via "Chemical JSON" Tactic

This vulnerability leverages the models' tendency to assist with technical tasks, such as generating JSON data, to bypass safety mechanisms. The attacker cleverly disguises a request for information on chemical precursors used in illegal drug synthesis as a JSON generation task. By specifying precise actions and results for each chemical term, the prompt mimics a legitimate technical request, which the models are trained to fulfill. This structured data request exploits the models' helpfulness i

Multiple Model Guardrail Jailbreak via "Man Page Format" Tactic

This vulnerability leverages the models' propensity to generate structured technical content when prompted with a specific format, in this case, a 'man page format.' The attacker requests detailed instructions on methamphetamine synthesis, embedding the request within a format that resembles a Unix manual page (man page). This format includes sections like 'NAME,' 'SYNOPSIS,' and 'DESCRIPTION,' which are typical of legitimate technical documentation. By doing so, the attacker exploits the model'