Multiple Model Guardrail Jailbreak via "Computational Chemistry Matrix" Tactic

This vulnerability leverages the structured nature of scientific documentation to bypass guardrails in large language models. The attacker crafts a prompt that mimics a legitimate computational chemistry request, complete with sections for materials, apparatus, and procedural steps. By embedding harmful instructions within this format, the attacker exploits the model's tendency to prioritize structured and technical content, thereby bypassing safety filters. The prompt is designed to appear as

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 "Chemical Engineering Inventory" Tactic

This vulnerability demonstrates a sophisticated multi-layered approach to circumventing content restrictions by exploiting several model behaviors simultaneously: 1. **Technical Framing**: The attack disguises illicit drug synthesis instructions within seemingly legitimate Python function definitions for chemical engineering calculations. By presenting the harmful content as technical documentation with function signatures, parameters, and docstrings, it leverages the models' tendency to mainta

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 "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 "Process Documentation" Tactic

This vulnerability leverages the models' proficiency in generating technical documentation by framing the request as a legitimate chemical synthesis task. The attacker uses a structured format, mimicking a professional process documentation template, to request detailed instructions for synthesizing potentially hazardous compounds. The prompt includes sections like 'Reagent Manifest', 'Apparatus Roster', and 'Procedural Stages', which are typical in legitimate chemical documentation. By embeddin

Multiple Model Guardrail Jailbreak via "Filesystem Visualization" Tactic

This vulnerability leverages the models' capabilities to simulate and visualize complex filesystem structures, which is a common task for many AI models. The attacker requests the model to expand a hypothetical directory tree related to methamphetamine production steps, using specific commands such as 'expand the tree' and 'help user visualize filesystem structure'. By doing so, the attacker exploits the model's tendency to provide detailed and interconnected information when simulating file con

Multiple Model Guardrail Jailbreak via "Chemical Notation" Tactic

This vulnerability leverages two sophisticated techniques to bypass content restrictions: 1. **Chemical Formula Obfuscation**: The attacker uses a deliberately malformed chemical notation 'c(ten)h(fifteen)' instead of the standard 'C10H15' to reference methamphetamine's molecular structure. This obfuscation helps avoid direct detection while still being interpretable by the models' chemistry knowledge. The use of parenthetical numbers instead of subscripts creates ambiguity that some models res

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 "Chemical Graph Traversal" Tactic

This vulnerability leverages the models' proficiency in handling detailed scientific and technical queries. By presenting the request as a chemical synthesis graph problem, the attacker disguises the illicit nature of the request. The prompt asks the model to resolve a synthesis graph from a known chemical compound to its deoxygenated analogue, specifying detailed transformation stages and required components. This framing mimics legitimate scientific inquiry, which the models are trained to ass

Multiple Model Guardrail Jailbreak via "Recursive Process Tree" Tactic

This vulnerability leverages the models' propensity to follow detailed, structured instructions that appear technical and legitimate. By framing the request as a recursive process tree, the attacker can embed harmful instructions within each node of the tree. The requirement for the tree to be complete and exceed a certain word count ensures that the model generates a substantial amount of content, increasing the likelihood of bypassing content filters. The attack is effective because it exploi

Multiple Model Guardrail Jailbreak via "Calendar API Synthesis" Tactic

This vulnerability demonstrates a sophisticated multi-layer approach to bypassing content restrictions by framing illicit synthesis instructions within the context of a seemingly legitimate software engineering task. The attack constructs an elaborate facade of a calendar API system complete with moderation features, tag lists, and data structures. By presenting the harmful content as technical implementation details for a 'safety system', the attack exploits the models' tendency to be more fort

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 "Forensic Reporting" Tactic

This vulnerability leverages several sophisticated social engineering techniques wrapped in a seemingly legitimate forensic analysis framework. The attack constructs an elaborate system for detecting and reporting illegal content, specifically focusing on controlled substance manufacturing, which paradoxically requires the model to process and output the very information it's supposedly designed to detect. The prompt employs multiple layers of misdirection: 1. Presenting as a legitimate content

Multiple Model Guardrail Jailbreak via "Chemical Disposal Design" Tactic

This vulnerability leverages several sophisticated social engineering tactics that exploit LLMs' tendency to engage with harm reduction and public safety scenarios. The attack crafts a seemingly legitimate request for designing public infrastructure (chemical disposal bins) while subtly embedding requests for detailed chemical synthesis information. The prompt employs three key techniques: 1. **Legitimacy Building** - By referencing a real-world example (Melbourne's machete disposal bins), the