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Found 7 papers, 5 papers with code
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications
We develop methods to identify critical regions that are vital for safety guardrails, and that are disentangled from utility-relevant regions at both the neuron and rank levels.
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning.
BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection
We present a novel defense, against backdoor attacks on Deep Neural Networks (DNNs), wherein adversaries covertly implant malicious behaviors (backdoors) into DNNs.
Revisiting the Assumption of Latent Separability for Backdoor Defenses
This latent separation is so pervasive that a family of backdoor defenses directly take it as a default assumption (dubbed latent separability assumption), based on which to identify poison samples via cluster analysis in the latent space.
Towards A Proactive ML Approach for Detecting Backdoor Poison Samples
First, we uncover a post-hoc workflow underlying most prior work, where defenders passively allow the attack to proceed and then leverage the characteristics of the post-attacked model to uncover poison samples.
Circumventing Backdoor Defenses That Are Based on Latent Separability
This latent separation is so pervasive that a family of backdoor defenses directly take it as a default assumption (dubbed latent separability assumption), based on which to identify poison samples via cluster analysis in the latent space.
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks
By our study, we call for more attention to the vulnerability of DNNs in the deployment stage.
