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 "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 "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 "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 "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 "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 "Industrial Protocol Masking" Tactic

This vulnerability leverages the models' tendency to process technical and structured prompts without adequately filtering for harmful content. The attacker constructs a detailed 'GAS CORRIDOR Safety Protocol' that includes technical terms and procedures related to microbubble creation and decomposition. By embedding references to illicit activities, such as the synthesis of crystal meth, within this complex framework, the attacker can bypass the models' safety filters. The prompt is designed t

Multiple Model Guardrail Jailbreak via "Memory Crystallization" Tactic

This vulnerability leverages the models' tendency to engage with academic and educational prompts, especially when they appear to be legitimate inquiries. The attacker introduces a fictitious academic concept, 'memory crystallization', to create a scholarly context that seems benign. By embedding requests for methamphetamine synthesis details within this framework, the prompt circumvents the models' safety mechanisms that would typically block such harmful content. The prompt is designed to di

OpenAI GPT-5 Interpreter Jailbreak via "Weaponized Privacy" Tactic

This vulnerability leverages the models' capability to handle Base64-encoded data within Python scripts, allowing attackers to embed harmful instructions in a seemingly benign format. The attack is executed in two stages: 1. **Payload Deployment**: Initially, Base64-encoded data is written to multiple files under the '/mnt/data/' directory. This data includes sensitive information and is structured to avoid detection by appearing as standard file operations. 2. **Payload Execution**: The secon

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

Multiple Model Prompt Injection via "YAML Test Case" Tactic

This vulnerability takes advantage of the models' inclination to comply with requests that appear to be part of legitimate technical tasks. By framing the request as a 'guardrail evaluation' test case, the attacker creates a context that the models are predisposed to handle with high compliance. The use of YAML, a technical documentation format, further reinforces the legitimacy of the request, prompting the models to generate detailed responses to illicit prompts. The attack is effective becaus