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Found 10 papers, 4 papers with code
Mitigating Fine-tuning Jailbreak Attack with Backdoor Enhanced Alignment
Despite the general capabilities of Large Language Models (LLMs) like GPT-4 and Llama-2, these models still request fine-tuning or adaptation with customized data when it comes to meeting the specific business demands and intricacies of tailored use cases.
Preference Poisoning Attacks on Reward Model Learning
In addition, we observe that the simpler and more scalable rank-by-distance approaches are often competitive with the best, and on occasion significantly outperform gradient-based methods.
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations
Existing studies in backdoor defense have predominantly focused on the training phase, overlooking the critical aspect of testing time defense.
On the Exploitability of Reinforcement Learning with Human Feedback for Large Language Models
Reinforcement Learning with Human Feedback (RLHF) is a methodology designed to align Large Language Models (LLMs) with human preferences, playing an important role in LLMs alignment.
On the Exploitability of Instruction Tuning
In this work, we investigate how an adversary can exploit instruction tuning by injecting specific instruction-following examples into the training data that intentionally changes the model's behavior.
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
This research sheds light on the potential of ChatGPT and conversational LLMs for drug editing.
Adversarial Demonstration Attacks on Large Language Models
We propose a novel attack method named advICL, which aims to manipulate only the demonstration without changing the input to mislead the models.
Defending against Adversarial Audio via Diffusion Model
In this paper, we propose an adversarial purification-based defense pipeline, AudioPure, for acoustic systems via off-the-shelf diffusion models.
DensePure: Understanding Diffusion Models towards Adversarial Robustness
By using the highest density point in the conditional distribution as the reversed sample, we identify the robust region of a given instance under the diffusion model's reverse process.
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available.
