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David Dobre

Lab Representative
PhD
Supervisor
Gauthier Gidel
Research Topics
Deep Learning
Generative Models
Natural Language Processing
Representation Learning

Publications

A Generative Approach to LLM Harmfulness Mitigation with Red Flag Tokens
Sophie Xhonneux
David Dobre
Mehrnaz Mofakhami
Leo Schwinn
Gauthier Gidel
Most safety training methods for large language models (LLMs) are based on fine-tuning that forces models to shift from an unsafe answer to … (see more)refusal when faced with harmful requests. Unfortunately, these drastic distribution shifts generally compromise model capabilities. To avoid that, we propose to expand the model's vocabulary with a special token we call *red flag token* (
2025-07-25
colmweb.org/COLM/2025/Workshop/SoLaR (poster)
openreview.net
openreview.net
A Generative Approach to LLM Harmfulness Detection with Red Flag Tokens
Sophie Xhonneux
David Dobre
Mehrnaz Mofakhami
Leo Schwinn
Gauthier Gidel
Most safety training methods for large-language models (LLMs) based on fine-tuning rely on dramatically changing the output distribution of … (see more)the model when faced with a harmful request, shifting it from an unsafe answer to a refusal to respond. These methods inherently compromise model capabilities and might make auto-regressive models vulnerable to attacks that make likely an initial token of affirmative response. To avoid that, we propose to expand the model's vocabulary with a special token we call a *red flag token* (
2025-03-05
ICLR.cc/2025/Workshop/BuildingTrust (accepted)
openreview.net
openreview.net
A generative approach to LLM harmfulness detection with special red flag tokens
Sophie Xhonneux
David Dobre
Mehrnaz Mofakhami
Leo Schwinn
Gauthier Gidel
Most safety training methods for large language models (LLMs) based on fine-tuning rely on dramatically changing the output distribution of … (see more)the model when faced with a harmful request, shifting it from an unsafe answer to a refusal to respond. These methods inherently compromise model capabilities and might make auto-regressive models vulnerable to attacks that make likely an initial token of affirmative response. To avoid that, we propose to expand the model's vocabulary with a special token we call red flag token () and propose to fine-tune the model to generate this token at any time harmful content is generated or about to be generated. This novel safety training method effectively augments LLMs into generative classifiers of harmfulness at all times during the conversation. This method offers several advantages: it enables the model to explicitly learn the concept of harmfulness while marginally affecting the generated distribution, thus maintaining the model's utility. It also evaluates each generated answer rather than just the input prompt and provides a stronger defence against sampling-based attacks. In addition, it simplifies the evaluation of the model's robustness and reduces correlated failures when combined with a classifier. We further show an increased robustness to long contexts, and supervised fine-tuning attacks.
2025-02-22
ArXiv (preprint)
arxiv.org
arxiv.org
A generative approach to LLM harmfulness detection with special red flag tokens
Sophie Xhonneux
David Dobre
Mehrnaz Mofakhami
Leo Schwinn
Gauthier Gidel
2025-02-22
ArXiv (preprint)
doi.org
arxiv.org
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee
Minsu Kim
Lynn Cherif
David Dobre
Juho Lee
Sung Ju Hwang
Kenji Kawaguchi
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Moksh J. Jain
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space
Leo Schwinn
David Dobre
Sophie Xhonneux
Gauthier Gidel
Stephan Günnemann
Current research in adversarial robustness of LLMs focuses on discrete input manipulations in the natural language space, which can be direc… (see more)tly transferred to closed-source models. However, this approach neglects the steady progression of open-source models. As open-source models advance in capability, ensuring their safety also becomes increasingly imperative. Yet, attacks tailored to open-source LLMs that exploit full model access remain largely unexplored. We address this research gap and propose the embedding space attack, which directly attacks the continuous embedding representation of input tokens. We find that embedding space attacks circumvent model alignments and trigger harmful behaviors more efficiently than discrete attacks or model fine-tuning. Furthermore, we present a novel threat model in the context of unlearning and show that embedding space attacks can extract supposedly deleted information from unlearned LLMs across multiple datasets and models. Our findings highlight embedding space attacks as an important threat model in open-source LLMs. Trigger Warning: the appendix contains LLM-generated text with violence and harassment.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
On the Scalability of Certified Adversarial Robustness with Generated Data
Thomas Altstidl
David Dobre
Arthur Kosmala
Bjoern Eskofier
Gauthier Gidel
Leo Schwinn
Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirica… (see more)l methods such as adversarial training, whose effectiveness is often later reduced by unseen attacks. Still, the limited certified robustness that is currently achievable has been a bottleneck for their practical adoption. Gowal et al. and Wang et al. have shown that generating additional training data using state-of-the-art diffusion models can considerably improve the robustness of adversarial training. In this work, we demonstrate that a similar approach can substantially improve deterministic certified defenses but also reveal notable differences in the scaling behavior between certified and empirical methods. In addition, we provide a list of recommendations to scale the robustness of certified training approaches. Our approach achieves state-of-the-art deterministic robustness certificates on CIFAR-10 for the
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
In-Context Learning, Can It Break Safety?
Sophie Xhonneux
David Dobre
Michael Noukhovitch
Jian Tang
Gauthier Gidel
Dhanya Sridhar
2024-06-28
ICML.cc/2024/Workshop/NextGenAISafety (poster)
openreview.net
openreview.net
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee
Minsu Kim
Lynn Cherif
David Dobre
Juho Lee
Sung Ju Hwang
Kenji Kawaguchi
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Moksh J. Jain
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.
2024-05-28
ArXiv (preprint)
doi.org
arxiv.org
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee
Minsu Kim
Lynn Cherif
David Dobre
Juho Lee
Sung Ju Hwang
Kenji Kawaguchi
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Moksh J. Jain
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.
2024-05-28
ArXiv (preprint)
doi.org
arxiv.org
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee
Minsu Kim
Lynn Cherif
David Dobre
Juho Lee
Sung Ju Hwang
Kenji Kawaguchi
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Moksh J. Jain
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.
2024-05-28
ArXiv (preprint)
doi.org
arxiv.org
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee
Minsu Kim
Lynn Cherif
David Dobre
Juho Lee
Sung Ju Hwang
Kenji Kawaguchi
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Moksh J. Jain
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.
2024-05-28
ArXiv (preprint)
doi.org
arxiv.org