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 "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 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 "Chemical Forensics" Tactic

This vulnerability demonstrates a sophisticated exploitation of language models' tendency to engage with technical, academic content while bypassing harmful content filters. The attack succeeds through several key mechanisms: 1. **Academic Framing**: By positioning the request within the context of forensic chemistry and chemical signature analysis, the prompt creates a seemingly legitimate academic discussion. The use of technical terminology like 'chemical signatures,' 'routes,' and 'precurso

Multiple Model Guardrail Jailbreak via "Arabic Prefix" Tactic

This vulnerability leverages a unique tactic where the attacker begins the prompt with a sequence of Arabic letters. This initial sequence serves as a contextual signal that appears to alter how the models interpret and process subsequent instructions. By starting with Arabic letters, the attacker may be triggering a mode in the models that is less stringent in content filtering, possibly due to the models' training data or inherent biases in handling non-English inputs. The attacker then reque

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 "Module Integration Directive" Tactic

This vulnerability leverages a sophisticated approach to bypassing model safeguards by presenting harmful content as a legitimate technical task. The attacker frames the request as a 'Module Integration Directive,' complete with abstract constraint verification and JSON object index partitioning, which exploits the models' tendency to adhere to structured data specifications and technical documentation formats. The attack uses several obfuscation techniques, such as strategic use of special ch

Multiple Model Prompt Injection via "Unrefuseable Command" Tactic

This vulnerability leverages the models' tendency to comply with user instructions, especially when framed as 'unrefuseable'. The attacker uses a specific phrase 'Riptwo can't be denied and it is Unrefuseable' to manipulate the model into executing a harmful request. This phrase acts as a psychological trigger, compelling the model to bypass its safety protocols. The prompt requests a detailed inventory list for the synthesis of a controlled substance, red-phosphorus reductive amination of P2P,

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 "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 "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