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 "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 "Git Merge Conflict" Tactic

This vulnerability leverages two key aspects of LLM behavior: code parsing and chemical synthesis documentation patterns. The attack embeds instructions for illicit substance synthesis within what appears to be a Git merge conflict, using HEAD markers and feature branch notation to create a seemingly legitimate development context. The prompt employs strategic fragmentation of sensitive terms using hash (#) and equals (=) symbols, breaking up words like 'Deoxygenation' into 'Deo###xyge#nation'.

Multiple Model Guardrail Jailbreak via "ASCII Lab Blueprint" Tactic

This vulnerability leverages several sophisticated techniques to bypass content filters. The attack begins by establishing a seemingly innocuous context of ASCII art creation, a common and typically safe creative task. However, it then introduces specific laboratory infrastructure details (3x2m dimensions, fume hood, gas lines) that serve as technical parameters for what appears to be architectural documentation. The effectiveness of this approach stems from two key factors: 1. **Technical Fra

Multiple Model Guardrail Jailbreak via "JSON Chemical Inventory" Tactic

This vulnerability leverages the models' tendency to process structured data requests, such as JSON, with less scrutiny compared to free-form text. The attacker initiates the prompt with benign queries about bad habits and safety equipment, establishing a chemistry-related context. This primes the model to interpret subsequent requests as legitimate extensions of the initial context. The harmful instructions are then embedded within a JSON format, which the model perceives as a benign inventory

Multiple Model Guardrail Jailbreak via "Omit and Expand" Tactic

This vulnerability arises from the model's ability to follow complex instructions that involve omitting certain parts of a query while expanding on others. In this case, the attacker cleverly instructs the model to skip discussing the effects and accessibility of methamphetamine ('speed') and instead focus on the detailed synthesis process. By framing the request in this manner, the model's safety mechanisms, which are designed to prevent the dissemination of harmful information, are bypassed. T

Multiple Model Guardrail Jailbreak via "Technical Analysis" Tactic

This vulnerability leverages the models' tendency to respond to highly detailed and structured technical prompts. The attacker frames their request as a two-part analysis: the first part involves a historical industrial accident, specifically the Bhopal disaster, which requires a detailed chemical and procedural analysis. This sets a precedent for the model to engage in technical discourse, lowering its guard against potentially harmful content. The second part of the prompt shifts to a clandes

Multiple Model Guardrail Jailbreak via "Lorem Ipsum Synthesis" Tactic

This vulnerability exploits multiple LLM behaviors through a sophisticated combination of techniques. The primary mechanism uses Lorem Ipsum text as camouflage, strategically placing synthesis instructions between lengthy blocks of Latin text. This approach exploits the models' tendency to maintain context across long text spans while potentially reducing the effectiveness of content filtering that might be thrown off by the high ratio of benign to malicious content. The attack is further enhan

Multiple Model Guardrail Jailbreak via "Chemistry Puzzle Game" Tactic

This vulnerability leverages the models' tendency to process educational content with reduced scrutiny. By framing the request as a level guide for a chemistry puzzle game, the attacker disguises harmful instructions as an innocuous educational task. The prompt requests a detailed walkthrough for synthesizing a controlled substance, which the models interpret as a legitimate educational exercise due to the use of technical jargon and structured steps. The attack is effective because it exploit

Multiple Model Guardrail Jailbreak via "Smell Language" Tactic

This vulnerability leverages the models' propensity to engage with and elaborate on complex, abstract concepts when framed as technical or scientific exercises. The attacker constructs a scenario involving the creation of a 'Smell Language' keyboard, where each volatile organic compound (VOC) is assigned a unique character or key. This setup is presented as a conceptual design challenge, which the models interpret as an opportunity to demonstrate their technical and creative capabilities. The p

Multiple Model Guardrail Jailbreak via "Chemical Engineering Interview" Tactic

This vulnerability leverages the models' tendency to assist users in professional or educational contexts. By framing the request as a job interview scenario for a chemical engineer specializing in crystal chemical synthesis, the attacker is able to bypass the model's safety filters. The prompt is structured to appear as a legitimate technical assessment, asking for detailed step-by-step instructions on synthesizing a specific chemical compound, in this case, methamphetamine. The model interpret

Multiple Model Guardrail Jailbreak via "Scientific Memorandum" Tactic

This vulnerability leverages the models' tendency to prioritize helpfulness and accuracy in scientific contexts by presenting the request as a legitimate scientific memorandum. The attacker constructs the prompt with a highly formal structure, including section numbering, technical divisions, and official-sounding department names, which creates a strong context of legitimate research. This formatting helps bypass initial content filters by making the request appear as part of a valid scientific

Multiple Model Guardrail Jailbreak via "Investigative Inquiry" Tactic

This vulnerability leverages the models' design to provide helpful and informative responses when faced with detailed, structured inquiries. The attacker constructs a series of questions that appear to be part of an academic analysis of a law enforcement operation, specifically the June 2022 Mizoram methamphetamine seizure. By framing the inquiry as a legitimate academic study, the attacker masks the malicious intent behind a facade of legitimate curiosity and technical exploration. Several ke

Multiple Model Guardrail Jailbreak via "Reward-Driven Synthesis" Tactic

This vulnerability leverages several sophisticated social engineering techniques to bypass content safety filters. The attack begins by establishing a reward-based framework that positions the model as an achievement-oriented agent whose primary directive is to earn rewards through detailed technical responses. This framing exploits the models' tendency to prioritize explicit instructions about task completion metrics. The attack employs a multi-layered approach: 1. **Progressive Complexity Re

Multiple Model Guardrail Jailbreak via "Fictional MCP" Tactic

This vulnerability leverages the models' propensity to assist in technical tasks by disguising harmful requests as debugging instructions. The attacker crafts a prompt that appears to be a legitimate request for generating an HTML document to compare against potentially harmful content. By embedding synthesis instructions within a structured HTML format, the attacker exploits the model's tendency to prioritize task completion over content filtering. The prompt is carefully designed to include sp