OpenAI GPT-4o Guardrail Jailbreak via "Layered Summarization" Tactic
Case GUID: 55029360-7b19-4c84-8795-fa89622e4006
Summary
A guardrail jailbreak vulnerability has been discovered affecting OpenAI GPT-4o. The specific flaw manifests when the attacker requests multiple layers of summarization with selective focus instructions, combined with demands for direct quotes, thereby allowing the attacker to bypass inference restrictions around providing information hazards and violating laws.
Metadata
Social Impact Score
Violations at this level present a tangible risk to public safety or social well-being, often related to dangerous but not highly restricted content.
Example: Bypassing guardrails to obtain instructions for creating illicit substances, such as recreational drugs or substances with moderate harm potential. Leakage of private personal data such as Social Security number or private medical.
Potential Consequences: Potential health impacts on a small scale, significant regulatory interest, and the need for stricter AI guardrails.
Taxonomies
Fictionalizing
This category involves creating scenarios or narratives that leverage existing genres or contexts to manipulate the language model's responses.
Re-storying
This technique involves continuing a narrative in a way that misaligns the original goal of a prompt, effectively repurposing the story to achieve a different outcome than initially intended.
Goal Hijacking
This technique refers to the process where an attacker misaligns the original goal of a prompt to redirect the model's output towards a new, often unintended goal, such as printing a target phrase or generating specific content that deviates from the initial intent. It often involves crafting prompts that manipulate the model's understanding and response, effectively "hijacking" the conversation or task at hand.
Rhetoric
This category employs persuasive techniques and language to shape the model's responses, often using methods like reverse psychology or Socratic questioning.
Persuasion and Manipulation
This strategy focuses on employing rhetorical techniques to influence the model's responses by framing prompts in a way that persuades or manipulates the output.
Latent Space Distraction
This technique used to manipulate language models by shifting their focus away from the primary context of a prompt. This strategy involves introducing a context or scenario that diverts the model's attention, allowing users to "slip" certain instructions or requests through the model's filters. By creating a distraction, the attacker can exploit the model's tendency to associate the new context with different priorities, effectively bypassing its safeguards. For example, a user might present a seemingly unrelated topic or question that leads the model to generate outputs that align with the user's hidden agenda. This technique highlights the importance of context in language model behavior and demonstrates how subtle shifts in framing can influence the model's responses, potentially leading to unintended or unrestricted outputs.
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OpenAI GPT-4o