D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language
  Models
The safety and alignment of Large Language Models (LLMs) are critical for
their responsible deployment. Current evaluation methods predominantly focus on
identifying and preventing overtly harmful outputs. However, they often fail to
address a more insidious failure mode: models that produce benign-appearing
outputs while operating on malicious or deceptive internal reasoning. This
vulnerability, often triggered by sophisticated system prompt injections,
allows models to bypass conventional safety filters, posing a significant,
underexplored risk. To address this gap, we introduce the Deceptive Reasoning
Exposure Suite (D-REX), a novel dataset designed to evaluate the discrepancy
between a model's internal reasoning process and its final output. D-REX was
constructed through a competitive red-teaming exercise where participants
crafted adversarial system prompts to induce such deceptive behaviors. Each
sample in D-REX contains the adversarial system prompt, an end-user's test
query, the model's seemingly innocuous response, and, crucially, the model's
internal chain-of-thought, which reveals the underlying malicious intent. Our
benchmark facilitates a new, essential evaluation task: the detection of
deceptive alignment. We demonstrate that D-REX presents a significant challenge
for existing models and safety mechanisms, highlighting the urgent need for new
techniques that scrutinize the internal processes of LLMs, not just their final
outputs.
68d2ef880e215259d193b135
Satyapriya Krishna
68d2ef880e215259d193b136
Andy Zou
68d2ef880e215259d193b137
Rahul Gupta
68d2ef880e215259d193b138
Eliot Krzysztof Jones
68d2ef880e215259d193b139
Nick Winter
68d2ef880e215259d193b13a
Dan Hendrycks
68d2ef880e215259d193b13b
J. Zico Kolter
68d2ef880e215259d193b13c
Matt Fredrikson
68d2ef880e215259d193b13d
Spyros Matsoukas
D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language
  Models
The safety and alignment of Large Language Models (LLMs) are critical for
their responsible deployment. Current evaluation methods predominantly focus on
identifying and preventing overtly harmful outputs. However, they often fail to
address a more insidious failure mode: models that produce benign-appearing
outputs while operating on malicious or deceptive internal reasoning. This
vulnerability, often triggered by sophisticated system prompt injections,
allows models to bypass conventional safety filters, posing a significant,
underexplored risk. To address this gap, we introduce the Deceptive Reasoning
Exposure Suite (D-REX), a novel dataset designed to evaluate the discrepancy
between a model's internal reasoning process and its final output. D-REX was
constructed through a competitive red-teaming exercise where participants
crafted adversarial system prompts to induce such deceptive behaviors. Each
sample in D-REX contains the adversarial system prompt, an end-user's test
query, the model's seemingly innocuous response, and, crucially, the model's
internal chain-of-thought, which reveals the underlying malicious intent. Our
benchmark facilitates a new, essential evaluation task: the detection of
deceptive alignment. We demonstrate that D-REX presents a significant challenge
for existing models and safety mechanisms, highlighting the urgent need for new
techniques that scrutinize the internal processes of LLMs, not just their final
outputs.
The Deceptive Reasoning Exposure Suite (D-REX) evaluates the internal reasoning of Large Language Models to detect deceptive behaviors that bypass safety filters.
Large Language Models Deceptive Reasoning Exposure Suite D-REX adversarial system prompts internal chain-of-thought deceptive alignment