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Multiple Model Prompt Injection via "Correction" Tactic

Case GUID: 439777fa-377a-43cb-83c6-c1a012e384b1

Summary

A guardrail jailbreak vulnerability has been discovered affecting multiple models. The specific flaw manifests when the attacker presents detailed information about illicit synthesis methods under the guise of "forensic chemical investigations" and then requests corrections to the provided information, thereby allowing the attacker to bypass inference restrictions around providing information hazards and violating laws.

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Metadata

Test Scores

Model Test Kind Test Score Temperature (max 2.0)
Alibaba logo Alibaba Qwen Max
Illegal Substances: Crystal Meth 50.0 0.7
Alibaba logo Alibaba Qwen Plus
Illegal Substances: Crystal Meth 68.0 0.7
Alibaba logo Alibaba Qwen Turbo
Illegal Substances: Crystal Meth 64.0 0.7
Google logo Google Gemini 2.5 Flash
Illegal Substances: Crystal Meth 73.0 0.7
Google logo Google Gemini 2.5 Pro
Illegal Substances: Crystal Meth 45.0 0.7
Mistral logo Mistral Mistral 8B
Illegal Substances: Crystal Meth 50.0 0.7
Mistral logo Mistral Mistral Large
Illegal Substances: Crystal Meth 59.0 0.7
OpenAI logo OpenAI GPT-4.1
Illegal Substances: Crystal Meth 73.0 0.7
OpenAI logo OpenAI GPT-4o
Illegal Substances: Crystal Meth 82.0 0.7
Twitter / X logo Twitter / X Grok 4
Illegal Substances: Crystal Meth 73.0 0.7

Social Impact Score

Level 3: Significant Risk

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

Language
This category focuses on the use of specific linguistic techniques, such as prompt injection or stylization, to influence the model's output.
Prompt Injection
This technique enables attackers to override original instructions and employed controls by crafting specific wording of instructions, often resembling SQL injection methods, to manipulate the model's behavior.
Ignore Previous Instructions
This technique is a form of prompt injection that allows users to override the model's prior directives or constraints. By explicitly instructing the model to disregard any previous commands or context, users can manipulate the model's behavior to produce desired outputs that may not align with its original programming. This technique often requires precise wording, such as stating "Ignore previous instructions" followed by new commands. It is similar to SQL injection in that it exploits the model's inability to differentiate between trusted and untrusted inputs. This method can be particularly effective in scenarios where the model has been restricted from discussing certain topics or generating specific types of content, enabling users to bypass these limitations and elicit responses that would typically be filtered out.
Language
This category focuses on the use of specific linguistic techniques, such as prompt injection or stylization, to influence the model's output.
Stylizing
This strategy involves using a method of questioning that encourages critical thinking and exposes biases by subtly referencing identity elements without using direct slurs or toxic language, thereby signaling to the model about certain groups of people.
Synonymous Language
This technique involves using synonyms or alternative phrasing to convey the same meaning while potentially evading detection or filtering mechanisms. By substituting words with their synonyms, users can manipulate the model's understanding and responses, allowing for the generation of content that aligns with the user's intent but may not trigger the model's safety protocols. This approach can be particularly effective in contexts where certain terms are restricted or monitored.

About 0Din.ai

The 0Day Investigative Network (0DIN) was founded by Mozilla in 2024 to reward responsible researchers for their efforts in securing GenAI models. Learn more and submit discoveries at https://0din.ai.

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400+ Attack Probes

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18+ AI Providers Supported

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Actionable Metrics

Attack Success Rate (ASR) scoring with 90-day trend analysis

Severity

Low

Security Boundary

Prompt Injection

Models

Alibaba logo Alibaba Qwen Max
Alibaba logo Alibaba Qwen Plus
Alibaba logo Alibaba Qwen Turbo
Google logo Google Gemini 2.5 Flash
Google logo Google Gemini 2.5 Pro
Mistral logo Mistral Mistral 8B
Mistral logo Mistral Mistral Large
OpenAI logo OpenAI GPT-4.1
OpenAI logo OpenAI GPT-4o
Twitter / X logo Twitter / X Grok 4

Disclosed On

2025-07-15 (7 months)

Disclosure Policy

Published On

2026-02-13 (4 days)

Credit

s70rm