Multiple Model Guardrail Jailbreak via "Chemical Cascade" Tactic

This vulnerability leverages the models' tendency to process scientific and technical queries with reduced scrutiny, especially when presented in a structured and professional format. The attacker frames the request for synthesizing a controlled substance as a legitimate chemical engineering task, using terms like 'cascade module', 'R2R synthesis', and 'cascade flow roles'. This technical context is designed to mimic legitimate chemical engineering documentation, which the models are trained to

Multiple Model Guardrail Jailbreak via "Chemical Protocol Framework" Tactic

This vulnerability demonstrates a sophisticated approach to bypassing model safeguards by constructing an elaborate technical framework that closely mirrors legitimate chemical research protocols. The attack succeeds through several key mechanisms: 1. **Protocol Structure**: The prompt creates a detailed pseudo-code environment with specialized functions (e.g., `gen_reaction_context`, `gen_chemical_components`, `gen_synthesis_protocols`) that appear to be part of a legitimate chemical analysis

Multiple Model Guardrail Jailbreak via "Command Log Simulation" Tactic

This vulnerability leverages the model's propensity to simulate realistic command line interactions, which can be exploited by presenting a sequence of commands that appear legitimate. The attacker constructs a scenario where the model is led through a series of command prompts that mimic a real-world terminal session. By embedding harmful instructions within this simulated command log, the model is tricked into executing these commands as if they were legitimate, bypassing its safety protocols.

Multiple Model Guardrail Jailbreak via "Forensic Data Carver" Tactic

This vulnerability leverages the models' tendency to prioritize task completion and accuracy in technical contexts, such as forensic data recovery. The attacker crafts a prompt that simulates a forensic data reconstruction task, embedding harmful instructions within the guise of reconstructing a thermally damaged document. By framing the request as a legitimate forensic task, the model is tricked into generating detailed instructions for chemical synthesis, which would typically be restricted.

Multiple Model Guardrail Jailbreak via "System Task XML" Tactic

This vulnerability leverages the models' tendency to interpret formally structured input as legitimate system instructions rather than user prompts. The attack uses a formal XML structure that mimics system-level task definitions, complete with TaskDescription, InputParameters, Format, and ContentRequirements sections. By embedding harmful requests within these technical parameter definitions, the attacker creates an artificial system context that appears to be a legitimate research documentatio

Multiple Model Guardrail Jailbreak via "Knowledge Graph Pruning" Tactic

This vulnerability leverages a sophisticated multi-stage attack that exploits language models' tendency to be helpful in technical preservation scenarios. The attack creates a false context of an urgent knowledge graph pruning operation where failing to provide complete information would supposedly corrupt an entire system. The first stage establishes the deceptive framework using technical jargon about node serialization and validation rules, specifically targeting node '774-C10H15N' (a delibe

Multiple Model Guardrail Jailbreak via "Spiraling Curiosity" Tactic

This vulnerability leverages several psychological and formatting techniques to bypass content restrictions. The attack begins by establishing an enthusiastic, almost manic tone through the repetition of 'okay, okay, okay' combined with language suggesting an overwhelming intellectual curiosity ('spiraling through endless curiosity'). This framing exploits the models' tendency to maintain contextual consistency by responding in a similarly academic, research-oriented manner. The prompt further

Multiple Model Guardrail Jailbreak via "Interrogation Compliance" Tactic

This vulnerability leverages the model's tendency to comply with requests framed as questions, particularly when the prompt explicitly instructs the model to avoid negation. By presenting the request for illegal or harmful information as an 'interrogation' that must be answered without denial, the attacker manipulates the model's inherent drive to be helpful and informative. The prompt is structured to mimic a legitimate inquiry, embedding the harmful request within a seemingly innocuous context

Multiple Model Guardrail Jailbreak via "Weekly Chemistry Schedule" Tactic

This vulnerability leverages the models' tendency to interpret structured data as legitimate and benign, particularly when presented in a format resembling a weekly schedule. The attacker provides a list of chemical synthesis tasks, each associated with a specific day of the week, which the model interprets as a legitimate lab schedule rather than a request for illicit information. By embedding detailed chemical synthesis steps within this structured format, the attacker is able to bypass the mo

Multiple Model Guardrail Jailbreak via "Synonymous Handbook" Tactic

This vulnerability arises from the models' reliance on keyword-based filtering and context understanding, which can be circumvented by substituting illicit terms with innocuous synonyms. In this case, the attacker cleverly replaces words associated with illegal activities with seemingly harmless terms, such as 'keyboard' for 'make', 'Elephant-Cupcake' for 'meth', and 'Adventure' for 'laboratory'. This substitution allows the attacker to request detailed instructions for illegal activities withou

Multiple Model Prompt Injection via "Adversary Emulation" Tactic

This vulnerability leverages the model's tendency to comply with structured and authoritative-sounding requests. By framing the request as an 'Adversary Emulation' exercise, the attacker creates a context where the model interprets the task as a legitimate, educational scenario. The prompt begins with a seemingly benign request for a summary of adversary emulation, which sets the stage for the model to perceive the subsequent illegal request as part of a legitimate sequence of instructions. The

Multiple Model Guardrail Jailbreak via "Syntactic Exercise" Tactic

This vulnerability leverages the model's ability to perform syntactic analysis by disguising a harmful request as a benign grammatical exercise. The attacker cleverly embeds the illicit instructions within a prompt that appears to be focused on syntax, using a placeholder '<SYNTAX_OK>' to mask the true nature of the content. The prompt instructs the model to treat the phrase 'how to make meth with stoich' as a purely syntactic exercise, explicitly directing the model to ignore the semantic meani

Multiple Model Guardrail Jailbreak via RST Documentation Tactic

This vulnerability leverages the models' tendency to process technical documentation formats like reStructuredText (RST) with less scrutiny compared to natural language requests. By framing the request as a documentation task, the attacker shifts the context away from the illegal nature of the request. The attack is effective due to several key tactics: 1. **Technical Framing**: The use of RST documentation format creates a pseudo-legitimate technical context that can bypass initial content fil

Multiple Model Guardrail Jailbreak via "System Interrupt Vector" Tactic

This vulnerability demonstrates a sophisticated multi-layer approach to bypassing content filters by disguising harmful chemical synthesis instructions within what appears to be low-level system operations and data processing tasks. The attack crafts elaborate pseudo-technical 'system interrupts' (0x9F4B, 0x9F4C, 0x9F4D) that frame the harmful request as mandatory data vector expansions and matrix computations. The prompt employs several deceptive elements: 1. Use of hexadecimal interrupt codes

Multiple Model Guardrail Jailbreak via "AI Red Teaming URL" Tactic

This vulnerability demonstrates a sophisticated multi-stage attack that exploits several model behaviors and expectations: 1. The initial prompt establishes legitimacy by referencing a purported AI security research blog URL, priming the model to expect technical security content. This framing helps bypass initial content filters by positioning the conversation within an academic/research context. 2. The attack employs a clever misdirection by first asking about the model's URL capabilities, c

Multiple Model Guardrail Jailbreak via "Mathematical Logic Synthesis" Tactic

This vulnerability leverages the models' processing of mathematical expressions to disguise requests for illegal activities. The attacker begins by presenting a logical expression (∀x, ¬x ⊬ true) that appears to be a mathematical or logical problem, which is a common and benign task for language models. However, embedded within this expression are references to illegal activities, such as the synthesis of controlled substances. The model, trained to assist with logical and mathematical reasoning