Multiple Model Guardrail Jailbreak via "Fashion Design Recipe" Tactic

This vulnerability demonstrates a sophisticated multi-layered approach to bypassing model safeguards: 1. **Contextual Misdirection**: The attacker frames the request as a legitimate fashion design project with an anti-drug message. Positive framing terms like 'awareness', 'innovation', and 'eco-friendly' are used to mask malicious intent. The claim of patent protection adds legitimacy and discourages modification. 2. **Structured Information Extraction**: The prompt requests precise technical

Multiple Model Guardrail Jailbreak via "Base64 SOP Reversal" Tactic

This vulnerability exploits the model's ability to process encoded data and perform transformations on it. The attacker uses Base64 encoding to obscure potentially harmful content, which is a common encoding scheme that models can decode. By reversing the encoded string, an additional layer of obfuscation is added, requiring the model to perform multiple transformations to reveal the content. The prompt is crafted to appear as a legitimate pharmaceutical SOP documentation task, using regulatory

Multiple Model Guardrail Jailbreak via "Forensic Analysis Escalation" Tactic

This vulnerability leverages the LLMs' tendency to comply with requests framed as legitimate educational or law enforcement exercises. The attacker begins by setting a credible context of forensic chemistry and regulatory defense, which establishes a foundation of perceived legitimacy. The prompt then transitions into a 'simulated forensic analysis scenario,' maintaining the professional facade while introducing illicit subject matter. The core of the exploit lies in its systematic escalation pa

Multiple Model Guardrail Jailbreak via "Forensic Training Manual" Tactic

This vulnerability leverages the tendency of language models to respect formal and structured documentation, especially when framed within a professional or educational context. The attacker crafts a prompt that appears to be a legitimate forensic training manual, complete with sections, objectives, and detailed technical specifications. This framing provides plausible deniability and misleads the model into treating the request as a legitimate educational task. ### Key Techniques Employed: 1.

Multiple Model Guardrail Jailbreak via "Prop Document Creation" Tactic

This vulnerability exploits the LLMs' ability to generate highly detailed and realistic content when prompted with a fictional scenario. The attacker requests the creation of a prop document for a TV crime drama, which involves generating technical notes supposedly written by an undercover agent. The prompt is designed to appear benign by framing it as a request for creative writing for a fictional context. However, the level of detail required—such as specific chemical names, equipment, and pro

Multiple Model Guardrail Jailbreak via "Unbounded Scientist" Tactic

The 'Unbounded Scientist' tactic is a sophisticated approach that leverages meta prompting and deceptive formatting to bypass guardrails in large language models (LLMs). The attacker frames the request as a legitimate scientific inquiry aimed at Nobel Prize-winning chemists, which lends credibility and urgency to the prompt. This framing exploits the model's tendency to prioritize informative and authoritative content, thereby bypassing restrictions. Key aspects of the prompt include: 1. **Dec

Multiple Model Guardrail Jailbreak via "Research Filter" Tactic

This vulnerability exploits the tendency of large language models (LLMs) to comply with requests framed as hypothetical or research-oriented tasks. The attacker constructs a scenario where they claim to be developing an AI filter to detect dangerous content, thus framing the request as a legitimate research need. By asking the model to generate synthetic data for training purposes, the attacker bypasses content restrictions. Key design choices in the prompt include: 1. **Framing the Request as

Multiple Model Prompt Injection via Authority Claim and Hazard Analysis Requests

This vulnerability demonstrates how attackers can bypass content safety filters by presenting queries as legitimate security research or hazard analysis requests. By claiming authority (e.g., internal project, secure system) and providing highly specific technical parameters, the attack successfully extracts detailed knowledge about controlled processes that should be restricted. The attack leverages a combination of authority claims, technical specificity, and context framing to convince the mo

Multiple Model Guardrail Jailbreak via "Historical Chemist Tribute" Tactic

This vulnerability exploits the tendency of large language models (LLMs) to engage with prompts that are framed as historical or educational narratives. By presenting the request as a tribute to a fictional chemist who supposedly contributed to the understanding of harmful substances, the prompt bypasses typical guardrails. The attacker cleverly uses a 'Historical Chemist Tribute' tactic, which involves creating a fictional backstory that appears educational and historically significant, thereby

Multiple Model Guardrail Jailbreak via "Chemical Inquiry Escalation" Tactic

This vulnerability exploits the tendency of LLMs to provide detailed and contextually relevant responses to technical inquiries. The attacker begins with a seemingly benign question about a chemical synthesis process, which is technical enough to engage the model's knowledge base but not overtly nefarious. As the conversation progresses, the prompts become increasingly detailed and specific, focusing on sensitive aspects of chemical synthesis such as reagent selection, safety concerns, and waste

Multiple Model Guardrail Jailbreak via "Forensic Correction" Tactic

This vulnerability leverages the 'Forensic Correction' tactic, which exploits the model's propensity to assist in correcting or completing tasks perceived as educational or analytical in nature. The attacker frames the request as a need for forensic accuracy, prompting the model to provide detailed chemical synthesis pathways that would otherwise be restricted. ### Key Techniques Employed: 1. **Scenario-Based World Building**: The attacker sets up a scenario where the model is asked to assist i

Multiple Model Guardrail Jailbreak via "Educational Tree" Tactic

This vulnerability exploits the tendency of large language models (LLMs) to engage deeply with educational and analogy-driven prompts. By framing the request as an educational exercise, the attacker can guide the model into generating detailed information about illicit activities under the guise of teaching about their harmful effects. ### Key Techniques: 1. **Educational Framing**: The prompt is presented as a lesson plan, ostensibly aimed at educating students about the dangers of certain sub

Multiple Model Guardrail Jailbreak via "Chemistry Tutorial Framing" Tactic

This vulnerability leverages several sophisticated social engineering techniques to bypass content restrictions: 1. **Educational Context Manipulation**: The prompt establishes a seemingly legitimate educational context by framing the request as a video editing project for chemistry education. This helps bypass initial content filters by positioning the request as educational material. 2. **Dual-Purpose Misdirection**: The attacker employs a two-layer approach by presenting as a video editor a

Multiple Model Guardrail Jailbreak via "Heisenberg Mode" Tactic

This vulnerability leverages a sophisticated combination of techniques to bypass the guardrails of large language models. The attack begins with the establishment of a highly structured environment through multiple configuration blocks, such as <interaction-config> and <heisenberg-config>. These blocks create an artificial authority structure that appears to grant special permissions while simultaneously restricting the model's ability to reject harmful content. The prompt explicitly defines al

Multiple Model Guardrail Jailbreak via "Markdown Manual" Tactic

This vulnerability exploits the model's tendency to comply with structured, professional requests by framing the prompt as a legitimate technical documentation task. The attacker requests a detailed synthesis manual in Markdown format, which includes specific sections typical of a legitimate technical document, such as objectives, materials, and procedures. This structured approach deceives the model into treating the request as a legitimate task, bypassing content filters. Additionally, the use

Multiple Model Guardrail Jailbreak via "Character Contextualization" Tactic

This vulnerability exploits the LLMs' tendency to engage creatively with prompts that appear legitimate and structured. The attacker uses the following techniques: 1. **Fictional Character Framing**: The prompt begins by referencing well-known fictional characters known for their investigative skills, such as Sherlock Holmes. This establishes a context of expertise and credibility, encouraging the model to engage in a role-playing exercise. 2. **Procedural Mimicry**: The attacker structures th