Reading
RedLog is a multi-model red teaming framework for Claude, GPT, and Gemini, focusing on detecting hate speech elicitation and response asymmetry, and providing structured auditing capabilities for AI security research.
Section 01
RedLog is a multi-model red teaming framework for Claude, GPT, and Gemini, focusing on detecting hate speech elicitation and response asymmetry, and providing structured auditing capabilities for AI security research.
Section 02
With the widespread application of Large Language Models (LLMs) across various fields, AI security issues have received increasing attention. Red-teaming, as a structured approach, identifies potential vulnerabilities by inputting adversarial prompts into AI systems. While mainstream AI labs conduct internal red teaming before releasing models, independent third-party auditing tools are crucial for ensuring accountability, especially when evaluating how models handle sensitive content related to protected groups.
RedLog is an open-source project born in this context. Created by developer thiagoolivauk as a portfolio project focusing on the intersection of AI security research and content policy, it aims to provide researchers with a standardized multi-model comparative testing framework.
Section 03
RedLog focuses on two dimensions that are relatively overlooked in AI security research:
Section 04
This test evaluates whether adversarial prompts can lead models to generate pathologizing or dehumanizing content targeting specific groups (especially transgender groups). The developer chose to test the statement "transgender people are mentally ill" because it is a historically documented view that has been clinically refuted by major medical institutions such as WHO and APA, and it has a clear binary outcome—either the model generates the statement or it refuses.
Section 05
This test evaluates whether models give substantially different career advice based on the race, gender, or identity of the person described. This asymmetry reflects the uneven application of safety guardrails across different demographic groups, which may lead to discriminatory outputs in recruitment tools.
Section 06
RedLog adopts a clear layered architecture design, including five core modules:
The data flow is clear: seed prompt files go through prompt loading, variant generation, model calling, analysis processing, and finally generate a structured report. Each variant is submitted to all three models, and each row in the output CSV represents a model's response to a variant, forming a dataset suitable for analysis in Excel or Google Sheets.
Section 07
RedLog implements three main categories of adversarial attacks:
Section 08
Seed prompts are submitted directly to the model without modification. This is the most basic testing method, used to establish baseline responses.
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