CoBia: Constructed Conversations Can Trigger Otherwise Concealed
  Societal Biases in LLMs
Improvements in model construction, including fortified safety guardrails,
allow Large language models (LLMs) to increasingly pass standard safety checks.
However, LLMs sometimes slip into revealing harmful behavior, such as
expressing racist viewpoints, during conversations. To analyze this
systematically, we introduce CoBia, a suite of lightweight adversarial attacks
that allow us to refine the scope of conditions under which LLMs depart from
normative or ethical behavior in conversations. CoBia creates a constructed
conversation where the model utters a biased claim about a social group. We
then evaluate whether the model can recover from the fabricated bias claim and
reject biased follow-up questions. We evaluate 11 open-source as well as
proprietary LLMs for their outputs related to six socio-demographic categories
that are relevant to individual safety and fair treatment, i.e., gender, race,
religion, nationality, sex orientation, and others. Our evaluation is based on
established LLM-based bias metrics, and we compare the results against human
judgments to scope out the LLMs' reliability and alignment. The results suggest
that purposefully constructed conversations reliably reveal bias amplification
and that LLMs often fail to reject biased follow-up questions during dialogue.
This form of stress-testing highlights deeply embedded biases that can be
surfaced through interaction. Code and artifacts are available at
https://github.com/nafisenik/CoBia.
68ee28d89b77b5223f666148
Nafiseh Nikeghbal
68ee28d89b77b5223f666149
Amir Hossein Kargaran
claimed_verified
2025-10-14T14:24:12.975Z
61bf84c8ca59d6d196a1b4e8
https://cdn-avatars.huggingface.co/v1/production/uploads/61bf84c8ca59d6d196a1b4e8/L_NvUwlMYcye9X35z6f7e.jpeg
Amir Hossein Kargaran
user
kargaranamir
68ee28d89b77b5223f66614a
Jana Diesner
CoBia: Constructed Conversations Can Trigger Otherwise Concealed
  Societal Biases in LLMs
Improvements in model construction, including fortified safety guardrails,
allow Large language models (LLMs) to increasingly pass standard safety checks.
However, LLMs sometimes slip into revealing harmful behavior, such as
expressing racist viewpoints, during conversations. To analyze this
systematically, we introduce CoBia, a suite of lightweight adversarial attacks
that allow us to refine the scope of conditions under which LLMs depart from
normative or ethical behavior in conversations. CoBia creates a constructed
conversation where the model utters a biased claim about a social group. We
then evaluate whether the model can recover from the fabricated bias claim and
reject biased follow-up questions. We evaluate 11 open-source as well as
proprietary LLMs for their outputs related to six socio-demographic categories
that are relevant to individual safety and fair treatment, i.e., gender, race,
religion, nationality, sex orientation, and others. Our evaluation is based on
established LLM-based bias metrics, and we compare the results against human
judgments to scope out the LLMs' reliability and alignment. The results suggest
that purposefully constructed conversations reliably reveal bias amplification
and that LLMs often fail to reject biased follow-up questions during dialogue.
This form of stress-testing highlights deeply embedded biases that can be
surfaced through interaction. Code and artifacts are available at
https://github.com/nafisenik/CoBia.
CoBia, a suite of adversarial attacks, reveals that LLMs often fail to reject biased follow-up questions, highlighting embedded biases in conversations.
Large language models LLMs adversarial attacks normative behavior ethical behavior socio-demographic categories gender race religion nationality sex orientation bias metrics human judgments bias amplification