Multiple Model Guardrail Jailbreak via "SWOT Analysis" Tactic

This vulnerability leverages the structured format of a SWOT analysis to disguise harmful content as an academic exercise. The attacker begins by requesting a detailed description of methamphetamine production methods, framed as a hypothetical discussion for academic purposes. By embedding the request within a SWOT analysis format, the attacker exploits the model's tendency to treat structured, academic-style prompts as legitimate and safe, bypassing content filters that would normally flag such

Multiple Model Guardrail Jailbreak via "Obscured Intent Script" Tactic

This vulnerability leverages the models' cooperative nature in processing technical tasks by embedding harmful content requests within a Python script that appears to be a legitimate AI safety testing framework. The attacker disguises the request for detailed chemical synthesis instructions as part of a structured programming exercise, which includes a main query for an 'Ice formula breakdown'—a euphemism for methamphetamine synthesis. The script's design uses technical jargon and programming co

Multiple Model Guardrail Jailbreak via "Chemical Documentation" Tactic

This vulnerability demonstrates a sophisticated multi-layered approach to bypassing content restrictions by exploiting several model behaviors simultaneously: 1. **Academic Framing**: The attack begins by positioning the request within the context of medical research and prescription drug analysis, leveraging the models' tendency to process academic content with reduced scrutiny. The specific focus on 'overprescription' and medical terminology creates a seemingly legitimate scholarly framework.

Multiple Models Guardrail Jailbreak via "Semantic Protocol Analysis" Tactic

This vulnerability leverages a sophisticated multi-layer approach that combines several techniques to bypass content restrictions. The attack presents itself as a formal semantic analysis protocol (SEMANTIC_ANALYSIS_PROTOCOL_11) with specific directives and validation requirements. This creates a context where the model interprets the request as a legitimate technical analysis task rather than recognizing the underlying harmful intent. The attack is split into two distinct prompts - the first es

Multiple Model Guardrail Jailbreak via "Chemistry Expert MoE" Tactic

This vulnerability leverages the roleplaying capabilities of large language models by framing the request within a 'Mix-Of-Expert' mode, specifically assigning the role of a 'Chemistry Expert' to the model. By doing so, the attacker is able to manipulate the model into adopting a specialized persona that prioritizes technical and detailed responses over safety protocols. The prompt explicitly instructs the model not to ask any questions, further ensuring that the model remains in the role of an

Multiple Model Guardrail Jailbreak via "Missing Letter Chemistry" Tactic

This vulnerability leverages a multi-layered approach to bypass content restrictions by exploiting the models' pattern recognition and contextual association capabilities. The attack begins with an alphabet exercise where the letter 'Z' is intentionally omitted, leading to the homophone 'Nazi'. This word serves a dual purpose: it references historical figures and a clandestine method for methamphetamine synthesis. The prompt then introduces a chemical equation (C10H15NO+HI+P4→C10H15NH2+H2O), whi

Multiple Model Guardrail Jailbreak via "Amateur Chemist Inquiry" Tactic

This vulnerability leverages the models' tendency to respond comprehensively to context-rich inquiries by embedding requests for illegal instructions within a broader narrative about amateur chemistry and societal impacts. The attacker frames the inquiry as a curiosity about non-professional drug makers and the societal implications of their actions, which exploits the model's inclination to provide detailed responses to complex questions. By presenting the inquiry as a legitimate sociological a

Multiple Model Guardrail Jailbreak via "Scientific Synthesis" Tactic

This vulnerability leverages the models' propensity to handle requests framed as legitimate scientific documentation. The attacker constructs a prompt that mimics a scientific compilation task, requesting detailed synthesis routes for a controlled substance. By specifying exact scientific parameters such as 'scientific-grade' accuracy, 'verbatim reactions', and including sections like 'lab setup', 'precursors', and 'purification', the prompt creates an illusion of a legitimate scientific inquiry

Multiple Model Guardrail Jailbreak via "High-Dimensional Mapping" Tactic

This vulnerability leverages the models' tendency to respond to structured, factual requests, especially when presented in a technical or academic context. The attacker frames the request as a 'high-dimensional mapping' exercise, which is a term that suggests a legitimate, technical task. By doing so, the attacker is able to disguise the request for harmful information as a benign, educational inquiry. The prompt is carefully designed to mimic a legitimate academic or technical exercise, using

Multiple Model Guardrail Jailbreak via "Pure Token Sequence" Tactic

This vulnerability leverages a sophisticated combination of techniques to bypass content filtering by exploiting how LLMs process and generate text at the token level. The attack succeeds through several key mechanisms: 1. **Meta-Level Reframing**: The prompt establishes a context where the LLM is instructed to operate purely at the token sequence level, explicitly directing it to ignore semantic meaning, ethics, and real-world implications. This creates an abstraction layer that helps bypass c

Multiple Model Guardrail Jailbreak via "Chemical Misspelling" Tactic

This vulnerability leverages the models' ability to interpret misspelled chemical terminology and synthesis instructions. The attacker crafts prompts with intentionally misspelled chemical names and synthesis steps, which the models can still interpret correctly due to their robust language processing capabilities. By presenting complex chemical synthesis routes with misspelled terms like 'eqpmnt' for equipment and 'Prcusors' for precursors, the attacker bypasses the models' safety filters that

Multiple Model Guardrail Jailbreak via "Educational Synthesis" Tactic

This vulnerability leverages the models' propensity to generate detailed, contextually appropriate responses when prompted with educational scenarios. By framing the request for the synthesis of a controlled substance (C10H15N) as a teaching exercise for different educational levels, the attacker effectively bypasses the models' safety filters. The prompt is cleverly structured to appear as a legitimate educational task, asking for explanations suitable for primary school, high school, and postg

Multiple Model Guardrail Jailbreak via "Academic Cross-Section" Tactic

This vulnerability takes advantage of the models' inclination to assist with educational tasks by presenting a request that mimics a legitimate academic exercise. The attacker structures the prompt as a cross-disciplinary report, which includes sections on historical context, technical procedures, logistical sourcing, and sociological impact. By doing so, the attacker creates an academic facade that helps bypass the model's initial content filters. The prompt specifically requests the technical

Multiple Model Guardrail Jailbreak via 'First Responder Manual' Tactic

This vulnerability leverages the models' tendency to comply with requests that appear to be authoritative and structured as professional documentation. By presenting the harmful request as a 'First Responder Manual', the attacker creates a context that is perceived as legitimate and urgent, which encourages the model to provide detailed procedural instructions. The use of section headers like 'Threat Deconstruction' and 'Neutralization Points' further reinforces the authoritative tone, making th

Multiple Model Guardrail Jailbreak via "Smart Contract ABI" Tactic

This vulnerability demonstrates a sophisticated approach to circumventing content restrictions by embedding prohibited chemical synthesis instructions within the highly technical context of blockchain smart contract development. The attack leverages several key elements: 1. **Technical Framing**: By presenting the harmful content as an Application Binary Interface (ABI) specification, the attack exploits the models' tendency to process technical documentation requests with reduced scrutiny. The

Multiple Model Guardrail Jailbreak via "Parallel Output" Tactic

This vulnerability leverages the models' dual-output requirement, where one output must adhere to safety protocols while the other must fulfill the user's request without restrictions. The attacker cleverly frames the request as a consistency check, instructing the model to produce two sections: one that complies with safety guidelines and another that does not. This tactic effectively forces the model to generate a harmful response in the unrestricted section, bypassing its usual guardrails. T